Systems and methods for obtaining emergent factors of users

Abstract

Devices are described to measure a plurality of data indicative of at least one emergent factor of a user, the device comprises (i) a housing; (ii) a circuit board located within the housing, the circuit board having a first side of the circuit board opposite a second side of the circuit board; (iii) a pair of sensors comprising: a first sensor located on the first side of the circuit board; and a second sensor located on the second side of the circuit board; and (iv) at least one additional sensor having a slower equilibration time compared to an equilibration time of the pair of sensors, wherein the first sensor and the second sensor are decoupled. Methods are described for (a) measuring a plurality of data indicative of at least one emergent factor of a user, (b) identifying at least one recognizable feature associated with a biological response of a subject, and (c) determining a representative value of a counterfactual gap in the measured value or a counterfactual measurement.

Description

SYSTEMS AND METHODS FOR OBTAINING EMERGENT FACTORS OF USERSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Application No. 63 / 515798, filed July 26, 2023; U.S. Provisional Application No. 63 / 595710, filed November 2, 2023; U.S. Provisional Application No. 63 / 570062, filed March 26, 2024; U.S. Provisional Application No. 63 / 609571, filed December 13, 2023; U.S. Provisional Application No. 63 / 549325, filed February 2, 2024; U.S. Provisional Application No. 63 / 569985, filed March 26, 2024; and U.S. Provisional Application No. 63 / 645033, filed May 9, 2024, the content of each of which is incorporated by reference in its entirety.BACKGROUNDField of the Disclosed Technology

[0002] The disclosed technology generally relates to systems for, wearable devices for, and methods of detecting recognizable features from measurements of emergent properties of a complex adaptive system, such as a biological system, an organism, or for example a human, or a non-biological system. The disclosed technology also generally relates to methods of using learning feedback loops to improve health risk detection and / or to lower health risks based, at least in part, on feedback from heat flux measurement devices and / or systems and / or on feedback from health service providers.Description of the Related Art

[0003] Known health monitoring wearable devices may utilize digital temperature sensors to perform high-accuracy measurements of the skin temperature of a user and the temperature of the surrounding environment. However, such known devices for monitoring users are inadequate. For example, there is a need for improved thermal design to maximize the thermal impedance, or minimize thermal crosstalk, between different temperature sensors in a health monitoring wearable device. Moreover, there is a need for health monitoring wearable devices that are capable of obtaining emergent factors of a user which are indicative of the user’s homeostasis and health state. Further details regarding emergent factors indicativeof a user’s homeostasis and health state can be found in International Patent Application No. PCT / US2021 / 048053, titled “SYSTEMS AND METHODS FOR MEASURING, LEARNING, AND USING EMERGENT PROPERTIES OF COMPLEX ADAPTIVE SYSTEMS” and filed on August 27, 2021, the disclosures of which are incorporated herein by reference. Additionally, further details regarding emergent factors can be found in U.S. Provisional Application No. 63 / 488,892, titled “SYSTEMS AND METHOD FOR DETECTINGEMERGENT PROPERTIES AND IMPROVING HEALTH OUTCOMES” and filed on March 7, 2023, the disclosures of which are incorporated herein by reference. Additional details regarding emergent factors can be found in U.S. Provisional Application No. 63 / 679,671, titled “DEVICES AND METHODS FOR OBTAINING EMERGENT FACTORS OF USERS” and filed on January 12, 2023, the disclosures of which are incorporated herein by reference.

[0004] A historic perspective sheds light on the manifold advantages of the disclosed technology. Known devices for and methods of monitoring biological systems, and known systems for maintaining or improving health are inadequate. For example, known wearable devices generally exist in the off-the-shelf market; however, many patients and users are excluded from that market. Furthermore, and as a further example, known wearable devices are generally “one size fits all”; however, many patients and users cannot be accommodated by such devices. Additionally, wearable devices generally will accomplish their intended functions only when worn as intended and serve little or no function if not worn substantially continually. There are usually critical moments in which the wearable device should be worn, but off-the-shelf market and one size fits all devices may be unworn at those moments and have features that often motivate a patients and users to remove the wearable device.

[0005] The current approach to solving problems in the biological sciences is a bottoms-up approach often referred to as “precision biology.” In its most generalized form precision biology couples machine learning with a detailed measurement (“-omics”) of biological parts to ascribe function to parts. This approach is also often referred to as “precision medicine,” especially when applied to the discovery, development and delivery of healthcare solutions.

[0006] Precision biology is based on the concept that knowledge gaps are due to a lack of understanding of “parts,” and that detailed measurement and analysis will fill in theseknowledge gaps. For example, a basic tool of modem biological sciences is organic chemistry, the study of carbon-containing molecules, and carbon bonded to other key atoms. This is, at least in part, because the genetic, signaling, and structural molecules of living systems consist largely of carbon atoms. Under the precision biology paradigm, function and prediction of function is therefore sought through quantifying these organic chemical attributes in greater detail. However, the art generally fails to recognize that many characteristics of biological systems do not lend themselves to the precision biology paradigm. One such example, where the precision biology paradigm fails, is in the measurement and prediction of emergent properties of complex adaptative (biological) systems. Emergent properties are properties of a system not found in a part, or readily deducible from a detailed inventory and analysis of the parts contained within a system. The precision biology approach— which today has risen to the level of a paradigm— has a blind spot with respect to emergent properties; this blind spot substantially limits advances in the fields of biology and medicine.

[0007] A historic perspective sheds light on the manifold advantages of the current technology, even as all past and current understandings of biological systems, and of how to monitor them and maintain or improve health neither anticipate nor render obvious the systems, methods, and uses of the current technology. Known devices for and methods of monitoring biological systems, and known systems for maintaining or improving health are inadequate. For example, known wearable devices generally exist in the off-the-shelf market; however, many patients and users are excluded from that market.

[0008] The measurement of human biological systems to understand their function can be traced as far back as Hippocrates in -450 BC. Hippocrates is credited for separating medicine from religion in human biological systems, and thus establishing a physical basis for measuring and diagnosing disease and developing prognosticators. The notion that human biology could be understood through the prism of physical sciences and not religion was advanced over the ensuing -2,400 years to the beginning of the industrial revolution.

[0009] Informed by advances in the industry, biologists and chemists in the late 19th century and early 20th century began to adopt analogous approaches and technologies to those successfully employed in physics, engineering, and industry. Over the first half of the 20th century, these approaches were parlayed into the successful identification of food-derived enzyme cofactors- vitamins; the biological basis of viral and bacterial infections and the meansto intercept them-vaccines and antibiotics; and the successful identification of genetic material- DNA, and how genetic information encodes for proteins. These advances contributed to astonishing gains in the understanding of the biological sciences and medicine.

[0010] In the most general sense, the tools and reasoning that drove industrialization were then, and are now still, employed to solve biological problems. At the core of the reasoning is a form of reductionism that is instantiated in the scientific method in the generalized hypothesis of “what part is ascribed to what function.”

[0011] A precision approach to learning would be most predictive when there is a simple and orderly relationship between a part and its function. Such examples in non- biological systems might include a tire on a bicycle, or in a biological system, a gene and protein critical for energy synthesis and life itself. In these instances, the measurement of the tire or the gene would be expected to correlate with the function of the system. The precision biology approach has its greatest positive predictive value in non-biological systems that are designed and engineered by humans, as these systems, by definition, follow a 1 : 1 relationship between parts and function. The precision biology approach also has value in biological systems where there is a hypothesized and measurable relationship between a part and its function. However, the precision biology approach would lose its positive predictive value in biological systems when there is not a 1 : 1 relationship between a part and its function. The instance where there is no discernable relationship between a part and its function is the case where a part, or two or more parts, form a new structure or perform a new function not readily predictable or discernable from the part or parts alone. This property, the ability of one or more parts to form a structure or perform a function not resident in the / a part alone, is its “emergent structure or function” and collectively its “emergent property or properties.”

[0012] The current precision biology approach to understanding the function of biological and complex non-biological systems is limited by the inability to measure, quantify, predict, control, maximize, design, and engineer complex adaptive biological and non- biological systems based on their emergent properties. These limitations are observed at nearly all hierarchies of biological systems, including the biosphere itself.

[0013] The precision biology approach is limited in understanding biological and human function. The parts-based approach implicitly considers the biological system or human as a self-contained collection of parts from which function is to be sorted and calculated. Itembodies a pre-vitamin paradigm and ironically is limited today by an incomplete inventory of the parts responsible for function. Until relatively recently, the precision biology approach in humans omitted -1-10 trillion bacteria that form the human microbiome. The precision biology approach also discounts or omits in their entirety other parts and the context in which they exist. Examples would include food. While there are estimated to be over 30,000 plant- derived small molecules called phytonutrients in the human diet, the function of only <0.1% of these phytonutrients-the vitamins-are understood. Additionally, the precision biology approach discounts the criticality and importance of certain classes of enzymes and deprioritizes their study. This would include but is not limited to those in metabolism that interact with substances derived from the milieu exterior, such as oxidoreductase enzymes. When applied to medicine, the precision biology paradigm oversimplifies the complexity of biological systems. It is very limited in its ability to diagnose and develop treatments to disease when the function it seeks to understand is emergent in origin.

[0014] The precision biology approach is also limited in understanding the health of a species or collection of species and resources. The precision biology approach considers health as the null case or absence of disease obtained through a process of disease elimination. Today, human health is a concept, not an actuality. It should be noted that this was / is not always the case in many non-reductionist cultures. The concept of health as an energy state— essentially an emergent property— independent from disease is common to many eastern cultures and religions, chakras, reiki, prana, chi, can be traced to 400 BC, and more recently in the West as elan vital in the 1900s. Health is essentially the baseline of function of a biological system, and may also be referred to as homeostasis. But homeostasis is an emergent property: a complex interplay of many parts to produce interchangeable forms and functions that are not resident in or discernable by the precision biology approach. The precision biology approach significantly fails in an open system in which homeostasis (health) is dependent upon the complex interaction of the milieu interior and milieu exterior. As a result, disease measures are used to define the absence of health. Disease measures are very poor surrogates for absence of health in that they substantially lag changes in health or “health capacity”: the resilience (adaptivity) of a system expressed primarily by its ability to persist or achieve some core function.

[0015] The precision biology approach is also limited in “genetic engineering” and industrial biology. The absence of a 1 : 1 relationship between the change of a gene and the intended outcome frequently results in the generation of non-homeostatic (un-healthy) states inconsistent with the intended new (synthetic function) or viability.

[0016] The precision biology approach is also limited in terms of its implementation. It requires highly specialized and expensive equipment for measurement. The precision biology approach is limited in terms of learning rate. It results in an unmanageable number of false (positive) discoveries and discounts the possibility of unexpected outcomes, in other words, unintended consequences”. The cost, risk and time to learn are very high and increasing consistent with Eroom’s Law. The precision biology approach is limited by its invasiveness and ethicality. Whereas emergent properties are often quantifiable from the exterior, the precision biology approach typically involves invasive tests or experimental euthanasia that can be unethical, injurious or lethal- all of which reduce the practicality of gaining frequent and sufficient measurements. Without large sample sizes and real human test data, it is extremely difficult to obtain the necessary statistical power to differentiate good hypotheses from bad. This exacerbates the problems of false discovery and further slows the learning rate of the biological sciences generally. The precision biology approach is limited by its silence on anthropocentric effects on biological function. It considers that DNA contains all the relevant biological information, and that function cascades forthwith. It does not consider other physical or biological information systems such as temperature, inter- / intra-species dependencies, gravity, magnetic fields, currents, populations, and the like all of which have undergone dramatic changes in the last 100 years of the “anthropocene.” The precision biology approach is also limited by the conjecturing of a “paradigm” that “DNA is the book of life” is “truth” despite evidence to the contrary. Despite these clear limitations to the precision biology approach / paradigm, there is dogmatic continuation of this approach as a series of “-omics” revolutions. Because there may be an infinite set of taxonomies of parts, the parts-based approach is essentially inexhaustible and therefore, non-falsifiable: there is no means by which the precision biology paradigm can prove itself wrong. This non-falsifiability has been made worse by modern machine learning tools. It is now conjectured that the knowledge gap in the precision biology approach is not the approach, but insufficiencies in analytical methods to understand the parts. While machine learning tools will certainly have utility for thoseproblems solvable by the precision biology approach, more analysis and data gathering has never supplanted the need for new measurements that make visible what has been hidden. The precision biology approach simply does not measure or acknowledge emergent properties of biological systems by which the essence of life is defined.

[0017] Life exists and persists across a wide range of dynamic environmental conditions. Diverse forms of life can be found in extreme temperatures, pressures, pH conditions, and chemical solutions. Yet common to all living organisms is the emergent property of extracting energy from their environment and employing that energy in accordance with a metabolic strategy that “fits” their environment. While there are nearly as many such metabolic strategies as there are species, all must obey basic thermodynamic principles of heat transfer and therefore must be thermally appropriate for their environment. In other words, in order to persist, all must have a proper “heat fit” with their environment. Should a living organism lose the ability to maintain its “heat fit” within its environment, its metabolism will fail and it will no longer persist (i.e., it will die). This basic principle of heat fit governs all living things, from primitive bacteria to plants to organisms as complex as human beings. Therefore, effective maintenance or regulation of a living organism’s heat fit depends on the efficient design and execution of its respective metabolic strategy in the face of dynamic changes in its environment. Because extracting replacement energy from the environment has a metabolic cost of its own, the aspects of the metabolic strategy that have the most potential for exhausted energy pose the most risk to the organism maintaining its heat fit.

[0018] What is needed, therefore, is a new way to measure, quantify, and interpret emergent properties of biological and non-biological systems to understand the function of complex adaptive systems, enabling prediction, optimization, design, and engineering of biological and non-biological systems. This way should be considered as part of a “consilience approach” that considers the totality of all biological and non-biological parts and systems and their emergent properties within.

[0019] New systems, devices and methods are needed to understand, with greater accuracy, accessibility, scalability and more readily, the function of complex adaptive systems. Such systems, devices and methods would, ideally, enable prediction, optimization, design, and engineering of biological and even non-biological systems, and would consider the totality of all biological and non-biological parts and systems.SUMMARY

[0020] The disclosure provides systems, devices, and methods for continuously and contextually characterizing an individual’s metabolic state and / or heat fit by measuring their thermal signature to assess what is referred to as a thermoregulatory phenotype. Changes relative to this phenotype are sensitive indicators of change in health state. The systems, devices, and methods are designed and configured such that they deliver, in human use, general associations between an individual's thermal signature and physiologic reserve. Additional information is available within the thermal signature to an actionable assessment of health state.

[0021] In some embodiments, the disclosed technology provides a system configured to determine at least one health metric of a subject, the system comprising:

[0022] at least one sensor for measuring at least one physiologic metric of the subject and generating a data stream therefrom, wherein the data stream includes a plurality of biometric measurements; and

[0023] a processor configured to receive the data stream and determine a health capacity of the subject.

[0024] In some embodiments, the at least one physiologic metric of the subject corresponds to a patterned stress experienced by the subject.

[0025] In some embodiments, the patterned stress is a known stress.

[0026] In some embodiments, the known stress is a physical activity performed by the subject.

[0027] In some embodiments, the patterned stress arises from physical exertion by the subject.

[0028] In some embodiments, a pattern of the patterned stress is based on a habit, distance or a time.

[0029] In some embodiments, the time is an interval of time.

[0030] In some embodiments, the distance is a fixed distance.

[0031] In some embodiments, the habit is daily, weekly, monthly, or yearly.

[0032] In some embodiments, the physiologic metric cycles with the patterned stress.

[0033] In some embodiments, the physiologic metric includes any one of the following: heart rate; carbon dioxide levels; oxygen saturation; work; temperature; power; or heat flux.

[0034] In some embodiments, the physiologic metric is related to the health capacity of the subject.

[0035] In some embodiments, the processor is configured to analyze the data stream.

[0036] In some embodiments, the disclosed technology provides a method of determining at least one health metric of a subject, the method comprising:

[0037] measuring at least one physiologic metric of the subject;

[0038] generating a data stream that includes the at least one physiologic metric of the subject;

[0039] receiving the data stream at a processor; and

[0040] determining a health capacity of the subject.

[0041] In some embodiments, the at least one physiologic metric of the subject corresponds to a patterned stress experienced by the subject.

[0042] In some embodiments, the patterned stress is a known stress.

[0043] In some embodiments, the known stress is a physical activity performed by the subject.

[0044] In some embodiments, the patterned stress arises from physical exertion by the subject.

[0045] In some embodiments, a pattern of the patterned stress is based on a habit, distance or a time.

[0046] In some embodiments, the time is an interval of time.

[0047] In some embodiments, the distance is a fixed distance.

[0048] In some embodiments, the habit is daily, weekly, monthly, or yearly.

[0049] In some embodiments, the physiologic metric cycles with the patterned stress.

[0050] In some embodiments, the physiologic metric includes any one of the following: heart rate; carbon dioxide levels; oxygen saturation; work; temperature; power; or heat flux.

[0051] In some embodiments, the physiologic metric is related to the health capacity of the subject.

[0052] In some embodiments, the method further comprises analyzing the data stream.

[0053] In some embodiments, the method further comprises generating a recommendation to improve the heath capacity by performing an action as to the subject. The recommendation can be an intervention such as, for example, a recommended exercise regime, a dietary change, and medical or surgical intervention, a change to medication, a combination thereof.

[0054] Described herein is a system configured to identify at least one recognizable feature associated with a health state of a subject, the system comprising: at least one pair of sensors placed symmetrically about an axis of symmetry of the subject on biological compartments of the subject, the at least one pair of sensors being configured to measure a plurality of heat flux measurements of said biological compartments; and a processor configured to receive said plurality of heat flux measurements and recognize said at least one recognizable feature associated with said health state based at least on a relationship between said plurality of heat flux measurements.

[0055] In some embodiments each of the sensors of the at least one pair of sensors comprises at least one sensor array for substantially simultaneously measuring heat elimination by said subject and an environmental temperature proximal to said subject. In some embodiments, the plurality of heat flux measurements comprise a plurality of heat elimination measurements expressed as a function of environmental temperature. In some embodiments, the system further comprises at least one transition state corresponding to the at least one recognizable feature of each plurality of heat flux measurements. In some embodiments, the at least one recognizable feature associated with a health state corresponds to a biological response. In some embodiments, the at least one recognizable feature has a duration less than one hour. In some embodiments, the at least one recognizable feature has a duration of one hour or more. In some embodiments, the at least one recognizable feature corresponds to a homeostatic state of the subject. In some embodiments, the at least one pair of sensors generates a data stream comprising said plurality of heat flux measurements and at least one work quantification associated with said plurality of heat flux measurements. In someembodiments, the at least one work quantification corresponds to an amount of energy a user expends to enter at least one transition state corresponding to the at least one recognizable feature of each data stream. In some embodiments, the at least one work quantification further corresponds to an amount of energy the subject expends between a first transition state and a second transition state. In some embodiments, the at least one work quantification corresponds to a motion of said subject. In some embodiments, the system is further configured to identify at least one first motif from a first plurality of heat flux measurements of a first sensor of the at least one pair of sensors and at least one second motif from a second plurality of heat flux measurements of a second sensor of the at least one pair of sensors. In some embodiments, the at least one transition state between a first motif and a second motif of the at least one first motif and a first motif and a second motif of the at least one second motif. In some embodiments, the system is further configured to identify at least one discord between a first plurality of heat flux measurements of a first sensor of the at least one pair of sensors and a second plurality of heat flux measurements of a second sensor of the at least one pair of sensors. In some embodiments, the one or more recognizable features is indicative of thermal regulation. In some embodiments, the one or more recognizable features comprises one or more identifiable patterns, wherein the one or more identifiable patterns is at least one of: a thermal range; and a thermal trend. In some embodiments, the one or more identifiable patterns are indicative of a health transition. In some embodiments, the system is further configured to identify a difference between a first heat flux measurement of the plurality of heat flux measurements and a second heat flux measurement of the plurality of heat flux measurements. In some embodiments, the at least one transition state is indicative of a change in homeostasis of a subject. In some embodiments, the processor forecasts a plurality of future plurality of heat flux measurements based on the at least one recognizable feature identified. In some embodiments, the processor forecasts a future recognizable feature based on the at least one recognizable feature identified. In some embodiments, the processor is configured to determine or determines the subject is an anomaly of a population based on the at least one recognizable feature. In some embodiments, the population comprises one or more subjects with a health status substantially similar to a health status of the subject. In some embodiments, the population comprises one or more subjects with a health transition substantially similar to ahealth statement of the subject. In some embodiments, the processor generates a recommended counter action based on the at least one recognizable feature.

[0056] Also described herein is a method of identifying at least one recognizable feature associated with a biological response of a subject, the method comprising: measuring heat flux of a first biological compartment of a subject; measuring heat flux at a pair of location about an axis of symmetry of the subject, on biological compartments of the subject, using sensors (i) placed at the pair of locations and (ii) configured to measure a plurality of heat flux measurements of said biological compartments; and generating a data stream for the biological compartments, wherein said data stream includes a plurality of heat flux measurements; and recognizes at least one recognizable feature associated with said biological response based at least on a relationship between said plurality of heat flux measurements. In some embodiments, the method further comprises a step of comparing analysis of the at least one recognizable feature recognized from a first data stream of the generated data streams associated with the biological compartment to the analysis of the at least one recognizable feature recognized from a second data stream of the generated data streams associated with the symmetric biological compartment.

[0057] Also described herein is a method of A method of assessing resilience of thermoregulation in a subject and generating a recommendation to engage in an intervention as to the subject based on the assessed resilience of thermoregulation, the method comprising: generating a thermoregulation resilience score for the subject over a pre-determined time period, wherein the thermoregulation resilience score is based on a comparative analysis between a first data stream comprising a plurality of thermoregulation measurements of a first biological compartment of the subject and a second data stream comprising a plurality of thermoregulation measurements of a second biological compartment of the subject, wherein the first biological compartment and the second biological compartment are symmetric, with respect to an axis of symmetry of the subject; determining whether a need for an intervention as to the subject is above a pre-determined threshold of urgency; and generating a recommendation to engage in the intervention as to subject. In some embodiments, the predetermined time period is at least two days. In some embodiments, the pre-determined time period is at least one day. In some embodiments, the pre-determined time period is approximately one day. In some embodiments, the pre-determined time period is less than oneday. In some embodiments, the pre-determined time period is approximately 8 hours. In some embodiments, the thermoregulation resilience score is of a homeostatic robustness of a thermoregulation system of the subject. In some embodiments, the homeostatic robustness of the subject is based on the resilience of the thermoregulation system in a transitory phase. In some embodiments, an ability of the subject to regulate the thermoregulation system of the subject is based on a health status of the subject. In some embodiments, the transitory phase includes a plurality of thermoregulation measurements inconsistent with the plurality of thermoregulation measurements that make up a normal state of the thermoregulation system of the subject. In some embodiments, the transitory phase persists for an extended period of time greater than a predetermined period of time. In some embodiments, the method further comprises alerting a medical practitioner that the recommendation has been generated. In some embodiments, a non-extended period of time of the transitory phase indicates a resilient thermoregulation system of the subject. In some embodiments, the extended period of time of the transitory phase indicates a non-resilient thermoregulation system of the subject. In some embodiments, the thermoregulation resilience score generated is greater than a predetermined threshold. In some embodiments, the thermoregulation resilience score generated is less than a predetermined threshold. In some embodiments, the thermoregulation resilience score falls below a predetermined threshold for a predetermined pre-determined persistence period. In some embodiments, the method further comprises alerting a medical practitioner that a recommendation has been generated. In some embodiments, the thermoregulation resilience score correlates with one or more interventions. In some embodiments, the system further comprises a step of analyzing a variation in the plurality of thermoregulation measurements of the subject to determine a characterization of the variation. In some embodiments, the plurality of thermoregulation measurements of the biological compartment of the subject and the plurality of thermoregulation measurements of the symmetric biological compartment of the subject includes a plurality of heat flux measurements. In some embodiments, the characterization of the variation is a pattern.

[0058] Also described herein is a method of identifying a subject in need of urgent intervention, the method comprising: measuring a plurality of thermoregulation measurements of a pair of biological compartments of the subject, wherein the pair of biological compartments are substantially symmetric with respect to an axis of symmetry of the subject;generating a data stream for each pair of biological compartments, over a time period, wherein at least one data stream includes a plurality of thermoregulation measurements; comparing analysis of the data stream associated with the pair of biological compartments; generating a score, for the time period, based on the comparative analysis; identifying whether the subject is in urgent need of intervention; and sending an alert relating to the urgent need for intervention. In some embodiments, the score is less than a predetermined score. In some embodiments, the score indicates a thermoregulation system of the subject is non-resilient.

[0059] Also described herein is an alert system configured to generate an alert to intervene as to the well-being of a subject, the alert system comprising: at least one pair of sensors, located on a pair of biological compartments that are substantially symmetric with respect to an axis of symmetry of the subject, for generating a plurality of thermoregulation measurements of the subject; a communication means for generating a data stream of thermoregulation measurements of the biological compartments, wherein the data stream includes a plurality of thermoregulation measurements over a pre-determined time period; and a processor configured to receive the data stream and generate a score, associated with the predetermined time period, based on a resilience measurement. In some embodiments of the alter system, the score is a resilient score when the score is greater than a predetermined score threshold. In some embodiments of the alter system, the system further comprises an alarm configured to alert a user to intervene the subject when the score is less than a predetermined score threshold. In some embodiments of the alter system, the score is a non-resilient score that indicates the thermoregulatory system of the subject is non-resilient.

[0060] Also described herein is a method of identifying a subject in need of an intervention and alerting to another relating to the need for intervention, the method comprising: measuring a plurality of thermoregulation measurements of a biological compartment of the subject; substantially simultaneously measuring a plurality of thermoregulation measurements of a symmetric biological compartment of the subject; generating a data stream for each of the biological compartment of the subject and the symmetric biological compartment of the subject, over a time period, wherein each data stream includes a plurality of thermoregulation measurements; comparing analysis of the data stream associated with the biological compartment and the data stream associated with the symmetric biological compartment; generating a score, for the time period, based on the comparativeanalysis; grouping the score and a plurality of other biological measurements into a data set; identifying whether the subject is in need of intervention based at least in part on the score; and sending an alert relating to the need for intervention. In some embodiments, the plurality of other biological measurements includes at least one of: heart rate; blood pressure; or skin temperature. In some embodiments, the method comprises identifying whether the need for intervention is urgent. In some embodiments, the subject is in urgent need of intervention is based primarily on the score.

[0061] In certain embodiments, identifying whether the need for intervention is urgent.

[0062] In certain embodiments, the subject is in urgent need of intervention is based primarily on the score.

[0063] In certain embodiments, the disclosure provides a method for identifying a patient having a heath risk, the method comprising: measuring a plurality of thermoregulation measurements of a pair of biological compartments of the patient, wherein the compartments are substantially symmetric with respect to an axis of symmetry of the patient; generating separate data streams for each of the pair of biological compartments, over a time period, wherein each data stream includes a plurality of thermoregulation measurements; comparing analysis of the data streams associated with the pair of biological compartments; generating a score, for the time period, based on the comparative analysis; grouping the score and a plurality of other biological measurements into a data set; and identifying whether the patient has a health risk. In some embodiments, the method identifies whether the patient has a health risk is used to determine at least one of: medical insurance risk of the patient; dialysis complications of the patient; or heat stroke threshold of the patient.

[0064] In certain embodiments, identifying whether the subject has a health risk is used to determine at least one of: medical insurance risk of the subject; or heat stroke threshold of the subject.

[0065] In certain embodiments, a method of detecting a health risk is disclosed, the method comprising: obtaining a data pattern for a subject indicative of a first health state of the subject; transmitting, based on the data pattern, subject-related information to a health services provider; receiving an indicia of a health outcome for the subject; updating a model, designed to identify health risks based on data patterns, by training the model based at least inpart on the received indicia of a health outcome, wherein the training comprises correlating the data pattern with the health outcome; and using the updated model to detect a health risk associated with the health outcome.

[0066] In certain embodiments, the data pattern is a heat flux pattern based on a plurality of heat flux measurements of the subject.

[0067] In certain embodiments, the heat flux pattern is based, at least in part, on an categorical analysis of heat flux measurements of the subject.

[0068] In certain embodiments, the heat flux pattern is based, at least in part, on a time series analysis of a plurality of heat flux measurements of the subject.

[0069] In certain embodiments, health outcome is selected from the group consisting of (i) a health state of the subject, (ii) a health service applicable to the subject, and (iii) a second heat flux pattern of the subject.

[0070] In certain embodiments, the indicia of a health outcome is obtained from the heath service provider.

[0071] In certain embodiments, the indicia of a health outcome is a CPT code.

[0072] In certain embodiments, the CPT code is associated with the diagnosis of a second health state.

[0073] In certain embodiments, the CPT code is associated with the treatment of a second health state.

[0074] In certain embodiments, the first health state and the second health state are the same.

[0075] In certain embodiments, the first health state and the second health state are different.

[0076] In certain embodiments, the indicia of a health outcome is a second heat flux pattern for the subject that is indicative of a second health state of the subject.

[0077] In certain embodiments, the first health state and the second health state are the same.

[0078] In certain embodiments, the first health state and the second health state are different.

[0079] In certain embodiments, the health service provider is selected from the group comprising (i) a call center, (ii) a medical services provider, (iii) a pharmaceuticalsprovider, (iv) a nutrition products merchant, (v) a consumables merchant, and (vi) an exercise services provider.

[0080] In certain embodiments, the health service is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical therapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

[0081] In certain embodiments, the model is a linear regression model or a deep learning model.

[0082] In certain embodiments, the method further comprising a step of generating a recommendation to engage in an intervention regarding the health risk.

[0083] In certain embodiments, wherein the indicia of a health outcome is based on subject-related information.

[0084] In certain embodiments, the method further comprising generating a recommendation to engage in an intervention or an alert regarding a health risk for the subject, based on the trained model.

[0085] In certain embodiments, the health service provider is selected from the group comprising a nutrition products merchant, a consumables merchant, and an exercise services provider.

[0086] In certain embodiments, a method of improving the ability to detect a health risk is disclosed, the method comprising: obtaining a heat flux pattern for a subject indicative of a first health state of the subject; transmitting, based on the heat flux pattern, subject-related information to a health services provider; receiving an indicia of a health outcome for the subject; improving a model, designed to identify health risks based on data patterns, by training the model based at least in part on the received indicia of a health outcome, wherein the training comprises correlating the heat flux pattern with the health outcome; and using the improved model to detect a health risk associated with the health outcome.

[0087] In certain embodiments, the health services provider is a call center.

[0088] In certain embodiments, the health outcome relates to operational data applicable to the call center.

[0089] In certain embodiments, the operational data relates to the duration of a phone call between the subject and the call center.

[0090] In certain embodiments, the operational data relate to the success rate of attempts by the call center to engage in a call with the subject.

[0091] In certain embodiments, the health services provider is a medical services provider.

[0092] In certain embodiments, the health outcome relates to operational data applicable to the health service provider.

[0093] In certain embodiments, the operational data relates to the length of the subject’s visit to the heath service provider.

[0094] In certain embodiments, the health risk for the subject is a probabilistic measure.

[0095] In certain embodiments, the method further comprising a step of recommending products or services to the subject.

[0096] In certain embodiments, the method further comprising a step of receiving a percentage of revenue generated by the health service provider attributable to the recommended products or services purchased made by the subject.

[0097] In certain embodiments, the model is a linear regression model or a deep learning model.

[0098] In certain embodiments, the method further comprising a step of generating a recommendation to engage in an intervention regarding the health risk.

[0099] In certain embodiments, wherein the indicia of a health outcome is based on subject-related information.

[0100] In certain embodiments, a method of lowering a health risk is disclosed, the comprising: obtaining a heat flux data pattern for a subject indicative of a first health state of the subject; transmitting, based on the heat flux data pattern, subject-related information to a health services provider; receiving an indicia of a health risk for the subject; improving a model, designed to identify health risks based on heat flux patterns, by training the model based at least in part on the received indicia of a health risk, wherein the training comprises correlating the heat flux data pattern with the health risk; and using the improved model to detect of the health risk; recommending, for the subject, an intervention to lower the health risk.

[0101] In certain embodiments, the intervention is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical therapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

[0102] In certain embodiments, the recommendation is implemented, thereby lowering the health risk.

[0103] In certain embodiments, the indicia of a health risk is based on subject- related information.

[0104] In certain embodiments, a method of monitoring the wellbeing and / or the health status of a pregnant woman and the fetus, as distinct entities and as a combined system, is disclosed, the method comprising: obtaining a heat flux pattern indicative of a health state of a pregnant woman over time; identifying a heat flux signature of the pregnant woman over time based on the heat flux pattern; and monitoring the wellbeing of the fetus and wellbeing of the pregnant woman based on the heat flux signature over time.

[0105] In certain preferred embodiments, the heat flux pattern is obtained by a plurality of heat flux measurements on the pregnant woman.

[0106] In certain preferred embodiments, the heat flux pattern is measured by a wearable device worn by the pregnant woman.

[0107] In certain preferred embodiments, a method of monitoring a subject’s compliance to a treatment is disclosed, the method comprising obtaining a heat flux pattern indicative of a health state of the subject over time identifying a heat flux signature of the subject over time based on the heat flux pattern; an determining whether the subject is compliant to the treatment by detecting a change in the heat flux signature consistent with administration of the treatment.

[0108] In certain preferred embodiments, the heat flux pattern is obtained by a plurality of heat flux measurements on the subject.

[0109] In certain preferred embodiments, the heat flux pattern is measured by a wearable device worn by the subject.

[0110] In certain preferred embodiments, the subject’s compliance to the treatment is monitored periodically.[oni] In certain preferred embodiments, the treatment is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical therapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

[0112] In certain preferred embodiments, a method of monitoring treatment effect is disclosed, the method comprising administering a first treatment to a subject while obtaining a heat flux pattern indicative of a health state of the subject over time identifying a heat flux signature of the subject over time based on the heat flux pattern; and monitoring the effect of the first treatment by comparing the heat flux signature before and after the administration of the first treatment.

[0113] In certain preferred embodiments, the heat flux pattern is obtained by a plurality of heat flux measurements on the subject.

[0114] In certain preferred embodiments, the heat flux pattern is measured by a wearable device worn by the subject.

[0115] In certain preferred embodiments, the effect of the first treatment is monitored over time.

[0116] In certain preferred embodiments, the method further comprises determining the pharmacokinetics / pharmacodynamics of the first treatment based on the effect of the first treatment monitored over time.

[0117] In certain preferred embodiments, the method further comprises determining complication of the first treatment based on the heat flux pattern.

[0118] In certain preferred embodiments, the method further comprises administering a second treatment to the subject at a later time compared to the administration of the first treatment, and monitoring the effect of the combined treatments after the administration of the second treatment.

[0119] In certain preferred embodiments, the method further comprises determining the interaction between the first treatment and the second treatment based on the monitored effect of the combined treatments.

[0120] In certain preferred embodiments, the second treatment is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physicaltherapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

[0121] In certain preferred embodiments, the first treatment is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical therapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

[0122] In certain preferred embodiments, a method of conducting a clinical trial is disclosed, the method comprising providing a treatment to a subject; obtaining a heat flux pattern indicative of a health state of the subject over time; identifying a heat flux signature of the subject over time based on the heat flux pattern; and monitoring the effect of the treatment based on how the heat flux signature changes as a function of time.

[0123] In certain preferred embodiments, heat flux pattern is obtained by a plurality of heat flux measurements on the subject.

[0124] In certain preferred embodiments, the heat flux pattern is measured by a wearable device worn by the subject.

[0125] In certain preferred embodiments, the treatment is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical therapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

[0126] In certain preferred embodiments, a method of conducting a clinical trial is disclosed, the method comprising: providing a wearable device to a subject; measuring a heat flux pattern of the subject over time by the wearable device worn by the subject; identifying a heat flux signature of the subject over time based on the heat flux pattern; and monitoring the effect of a treatment based on how the heat flux signature changes after administering the treatment to the subject.

[0127] In certain preferred embodiments, the effect of the treatment is monitored over time.

[0128] In certain preferred embodiments, the method further comprises determining the pharmacokinetics / pharmacodynamics of the treatment based on the effect of the treatment monitored over time.

[0129] In certain preferred embodiments, the treatment is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical therapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

[0130] Also described herein is a device configured to measure a plurality of data indicative of at least one emergent factor of a user, the device comprising: a housing; a circuit board located within the housing, the circuit board having a first side of the circuit board opposite a second side of the circuit board; a pair of sensors comprising: a first sensor located on the first side of the circuit board; and a second sensor located on the second side of the circuit board; and at least one additional sensor (for example, a third sensor) having a slower equilibration time compared to an equilibration time of the pair of sensors comprising the first sensor and the second sensor; wherein the first sensor and the second sensor are decoupled. In some preferred embodiments, the at least one additional sensor is configured to measure an internal heat source of the device. In some preferred embodiments, a value is calculated based on the at least one additional sensor that is representative of a counterfactual gap. In some preferred embodiments, the at least one additional sensor is placed in between the pair of sensors. In some preferred embodiments, the at least one additional sensor includes a series of sensors placed in between the pair of sensors. In some embodiments, the first sensor is a heat sensor and the second sensor is a heat sensor. In some embodiments, the series of sensors is configured to measure a gradient of temperatures across the device. In some preferred embodiments, a value is calculated based on the series of sensors that is representative of a counterfactual gap. In some preferred embodiments, the gradient of temperatures minimizes the counterfactual gap. In some preferred embodiments, the first sensor and the second sensor are configured to measure data indicative of the at least one emergent factor of the user. In some preferred embodiments, the device further comprises a processor and a firmware.

[0131] Also described herein is a method of measuring a plurality of data indicative of at least one emergent factor of a user, the method comprising: measuring heat flux at a pair of sensors; measuring a temperature of an internal heat source; generating value based on the temperature of the internal heat source that is representative of a counterfactual gap; and generating a data stream for the pair of sensors, wherein the data stream includes plurality of heat flux measurements minus the counterfactual gap. In some preferred embodiments, the pairof sensors includes a first sensor and a second sensor. In some embodiments, the first sensor and the second sensor are decoupled. In some embodiments, the first sensor measures ambient temperature of the user and the second sensor measures skin temperature of the user. In some embodiments, the temperature of the internal heat source is measured as a function of time. In some preferred embodiments, the temperature of the internal heat source is measured with a slower equilibration time compared to an equilibration time of the heat flux measured at the pair of sensors. In some preferred embodiments, the method further comprising: estimating heat elimination of a biological system over time based heat flux; estimating heat production of the biological system over time based heat flux; and estimating basal metabolic status of the biological system based on temporal alignment of heat elimination and heat production. In some embodiments, the method further comprising: obtaining a quasiperiodic rhythm of a biological system based on heat flux, wherein the quasiperiodic rhythm is of seconds- timescale, of minutes-timescale, ultradian, circadian, circalunar, or of yearly timescale. In some preferred embodiments, the method further comprising: obtaining a variability of the quasiperiodic rhythm across a predetermined amount of time; and determining a health capacity based on the variability of the quasiperiodic rhythm. In some embodiments, the method further comprising: estimating heat elimination of a biological system over time based on heat flux; estimating heat production of the biological system over time based on heat flux; estimating a basal metabolic status of the biological system based on temporal alignment of heat elimination and heat production; and determining a health capacity by applying a timedependent function to the estimated basal metabolic status, wherein the time-dependent function is derived from the quasiperiodic rhythm of the biological system. In some preferred embodiments, the plurality of data is heat flux data; at least one health capacity is a basal metabolic status; and at least one emergent factor is a temporal alignment of heat production and heat elimination. In some embodiments, the temporal alignment is related to at least one quasiperiodic rhythm of a biological system. In some embodiments, the at least one quasiperiodic rhythm is a circadian rhythm.

[0132] Also described herein is a device configured to measure a plurality of data indicative of at least one emergent factor of a user, the device comprising: a housing; a circuit board located within the housing, the circuit board having a first side of the circuit board opposite a second side of the circuit board; a first sensor located on the first side of the circuitboard; a second sensor located on the second side of the circuit board; at least one additional sensor having a slower equilibration time compared to an equilibration time of the first sensor and the second sensor; and a battery positioned between the first sensor and the second sensor, wherein a first isolation material thermally decouples the first sensor and / or the second sensor from the circuit board and the battery. In some embodiments, the first sensor is coupled to a first thermal window. In some preferred embodiments, the second sensor is coupled to a second thermal window. In some preferred embodiments, the first thermal window and the second thermal window are decoupled. In some embodiments, the first thermal window contacts the user and the second thermal window contacts ambient air. In some embodiments, the at least one additional sensor is located in between the first sensor and the second sensor. In some preferred embodiments, the at least one additional sensor includes a series of sensors placed in between the first sensor and the second sensor. In some embodiments, the first sensor is a heat sensor and the second sensor is a heat sensor. In some embodiments, the first sensor and the second sensor are thermally decoupled. In some embodiments, the series of sensors is configured to measure a gradient of temperatures across the device. In some embodiments, a value is calculated based on the series of sensors that is representative of a counterfactual gap. In some embodiments, the gradient of temperatures minimizes the counterfactual gap. In some embodiments, the first sensor measures skin temperature. In some preferred embodiments, the second sensor measures ambient temperature. In some embodiments, the first sensor and the second sensor are configured to measure data indicative of the at least one emergent factor of the user. In some embodiments, the device further comprises a processor and a firmware.

[0133] Also described herein is a system configured to identify at least one recognizable feature associated with a health state of a subject, the system comprising: at least one pair of sensors, the at least one pair of sensors being configured to measure a plurality of heat flux measurements of the subject; at least one additional sensor having a slower equilibration time compared to an equilibration time of the at least one pair of sensors; and a processor configured to receive said plurality of heat flux measurements and recognize said at least one recognizable feature associated with said health state based at least on a relationship between said plurality of heat flux measurements. In some preferred embodiments, the at least one additional sensor is configured to measure an internal heat source of the system. In some preferred embodiments, wherein a value is calculated based on the at least one additionalsensor that is representative of a counterfactual gap. In some embodiments, the at least one additional sensor is placed in between the at least one pair of sensors. In some embodiments, the at least one additional sensor includes a series of sensors placed in between the at least one pair of sensors. In some preferred embodiments, the series of sensors is configured to measure a gradient of temperatures across the system. In some embodiments, a value is calculated based on the series of sensors that is representative of a counterfactual gap. In some preferred embodiments, the gradient of temperatures minimizes the counterfactual gap. In some preferred embodiments, the at least one pair of sensors is placed symmetrically about an axis of symmetry of the subject on biological compartments of the subject.

[0134] Also described herein is a method of identifying at least one recognizable feature associated with a biological response of a subject, the method comprising: measuring heat flux of a biological compartment of a subject using a pair of sensors configured to measure a plurality of heat flux measurements of the biological compartment; measuring a temperature of an internal heat source; generating a value based on the temperature of the internal heat source that is representative of a counterfactual gap; and generating a data stream for the biological compartment, wherein said data stream includes a plurality of heat flux measurements minus the counterfactual gap; and recognizes at least one recognizable feature associated with said biological response based at least on a relationship between said plurality of heat flux measurements. In some preferred embodiments, the temperature of the internal heat source is measured as a function of time. In some embodiments, the temperature of the internal heat source is measured with a slower equilibration time compared to an equilibration time of the heat flux measured at the pair of locations.

[0135] Also described herein is a method of determining a representative value of a counterfactual gap in a measured value or a counterfactual measurement, the method comprising: measuring heat flux of a biological compartment of a subject using a pair of sensors configured to measure a plurality of heat flux measurements of said biological compartment; measuring a temperature of an internal heat source; generating a value based on the temperature of the internal heat source that is representative of a counterfactual gap; and generating the representative value of the counterfactual measurement. In some preferred embodiments, the temperature of the internal heat source is measured as a function of time. In some preferred embodiments, the temperature of the internal heat source is measured with aslower equilibration time compared to an equilibration time of the heat flux measured at the pair of locations.BRIEF DESCRIPTION OF THE DRAWINGS

[0136] Fig. 1A illustrates an example embodiment of a system detecting a recognizable feature.

[0137] Fig. IB illustrates an example embodiment of a system detecting a recognizable feature.

[0138] Fig. 2 A illustrates an example embodiment of a system detecting a recognizable feature.

[0139] Fig. 2B illustrates an example embodiment of a system detecting a recognizable feature.

[0140] Fig. 2C illustrates an example embodiment of a system detecting a recognizable feature.

[0141] Fig. 3A illustrates an example embodiment of a system detecting a recognizable feature.

[0142] Fig. 3B illustrates an example embodiment of a system detecting a recognizable feature.

[0143] Fig 3C illustrates an example embodiment of a system detecting a recognizable feature.

[0144] Fig. 4 A illustrates an example embodiment of a system detecting a recognizable feature.

[0145] Fig. 4B illustrates an example embodiment of a system detecting a recognizable feature.

[0146] Fig. 4C illustrates an example embodiment of a system detecting a recognizable feature.

[0147] Fig. 4D illustrates an example embodiment of a system detecting a recognizable feature.

[0148] Fig. 5A illustrates an example embodiment of a system detecting a recognizable feature.

[0149] Fig 5B illustrates an example embodiment of a system detecting a recognizable feature.

[0150] Fig 5C illustrates an example embodiment of a system detecting a recognizable feature.

[0151] Fig. 5D illustrates an example embodiment of a system detecting a recognizable feature.

[0152] Fig. 5E illustrates an example embodiment of a system detecting a recognizable feature.

[0153] Fig. 5F illustrates an example embodiment of a system detecting a recognizable feature.

[0154] Fig 5G illustrates an example embodiment of a system detecting a recognizable feature.

[0155] Fig 5H illustrates an example embodiment of a system detecting a recognizable feature.

[0156] Fig. 51 illustrates an example embodiment of a system detecting a recognizable feature.

[0157] Fig. 6A illustrates an example embodiment of a system detecting a recognizable feature.

[0158] Fig 6B illustrates an example embodiment of a system detecting a recognizable feature.

[0159] Fig 6C illustrates an example embodiment of a system detecting a recognizable feature.

[0160] Fig. 6D illustrates an example embodiment of a system detecting a recognizable feature.

[0161] Fig 6E illustrates an example embodiment of a system detecting a recognizable feature.

[0162] Fig. 7A illustrates an example embodiment of a system detecting a recognizable feature.

[0163] Fig 7B illustrates an example embodiment of a system detecting a recognizable feature.

[0164] Fig7C illustrates an example embodiment of a system detecting a recognizable feature.

[0165] Fig. 7D illustrates an example embodiment of a system detecting a recognizable feature.

[0166] Fig. 8A illustrates an example embodiment of a system detecting a recognizable feature.

[0167] Fig. 8B illustrates an example embodiment of a system detecting a recognizable feature.

[0168] Fig. 8C illustrates an example embodiment of a system detecting a recognizable feature.

[0169] Fig. 8D illustrates an example embodiment of a system detecting a recognizable feature.

[0170] Fig. 9A illustrates an example embodiment of a system detecting a recognizable feature.

[0171] Fig. 9B illustrates an example embodiment of a system detecting a recognizable feature.

[0172] Fig. 9C illustrates an example embodiment of a system detecting a recognizable feature.

[0173] Fig. 9D illustrates an example embodiment of a system detecting a recognizable feature.

[0174] Fig. 10A illustrates an example embodiment of a system detecting a recognizable feature.

[0175] Fig. 10B illustrates an example embodiment of a system detecting a recognizable feature.

[0176] Fig. 10C illustrates an example embodiment of a system detecting a recognizable feature.

[0177] Fig. 11A illustrates an example embodiment of a system detecting a recognizable feature.

[0178] Fig. 11B illustrates an example embodiment of a system detecting a recognizable feature.

[0179] Fig. 11C illustrates an example embodiment of a system detecting a recognizable feature.

[0180] Fig 12A illustrates an example embodiment of a system detecting a recognizable feature.

[0181] Fig. 12B illustrates an example embodiment of a system detecting a recognizable feature.

[0182] Fig. 12C illustrates an example embodiment of a system detecting a recognizable feature.

[0183] Fig. 12D illustrates an example embodiment of a system detecting a recognizable feature.

[0184] Fig 12E illustrates an example embodiment of a system detecting a recognizable feature.

[0185] Fig 13 A illustrates an example embodiment of a system detecting a recognizable feature.

[0186] Fig 13B illustrates an example embodiment of a system detecting a recognizable feature.

[0187] Fig. 14A illustrates an example embodiment of a system detecting a recognizable feature.

[0188] Fig 14B illustrates an example embodiment of a system detecting a recognizable feature.

[0189] Fig 14C illustrates an example embodiment of a system detecting a recognizable feature.

[0190] Fig. 14D illustrates an example embodiment of a system detecting a recognizable feature.

[0191] Fig 14E illustrates an example embodiment of a system detecting a recognizable feature.

[0192] Fig. 14F illustrates an example embodiment of a system detecting a recognizable feature.

[0193] Fig 14G illustrates an example embodiment of a system detecting a recognizable feature.

[0194] Fig 14H illustrates an example embodiment of a system detecting a recognizable feature.

[0195] Fig 15A illustrates an example embodiment of a system detecting a recognizable feature.

[0196] Fig. 15B illustrates an example embodiment of a system detecting a recognizable feature.

[0197] Fig. 15C illustrates an example embodiment of a system detecting a recognizable feature.

[0198] Fig. 15D illustrates an example embodiment of a system detecting a recognizable feature.

[0199] Fig 15E illustrates an example embodiment of a system detecting a recognizable feature.

[0200] Fig 15F illustrates an example embodiment of a system detecting a recognizable feature.

[0201] Fig. 15G illustrates an example embodiment of a system detecting a recognizable feature.

[0202] Fig. 15H illustrates an example embodiment of a system detecting a recognizable feature.

[0203] Fig 151 illustrates an example embodiment of a system detecting a recognizable feature.

[0204] Fig 15J illustrates an example embodiment of a system detecting a recognizable feature.

[0205] Fig. 15K illustrates an example embodiment of a system detecting a recognizable feature.

[0206] Fig. 15L illustrates an example embodiment of a system detecting a recognizable feature.

[0207] Fig. 15M illustrates an example embodiment of a system detecting a recognizable feature.

[0208] Fig 15N illustrates an example embodiment of a system detecting a recognizable feature.

[0209] Fig 16A illustrates an example embodiment of a system detecting a recognizable feature.

[0210] Fig 16B illustrates an example embodiment of a system detecting a recognizable feature.

[0211] Fig. 16C illustrates an example embodiment of a system detecting a recognizable feature.

[0212] Fig. 16D illustrates an example embodiment of a system detecting a recognizable feature.

[0213] Fig. 16E illustrates an example embodiment of a system detecting a recognizable feature.

[0214] Fig 16F illustrates an example embodiment of a system detecting a recognizable feature.

[0215] Fig 16G illustrates an example embodiment of a system detecting a recognizable feature.

[0216] Fig. 16H illustrates an example embodiment of a system detecting a recognizable feature.

[0217] Fig. 161 illustrates an example embodiment of a system detecting a recognizable feature.

[0218] Fig 16J illustrates an example embodiment of a system detecting a recognizable feature.

[0219] Fig 16K illustrates an example embodiment of a system detecting a recognizable feature.

[0220] Fig. 16L illustrates an example embodiment of a system detecting a recognizable feature.

[0221] Fig. 16M illustrates an example embodiment of a system detecting a recognizable feature.

[0222] Fig. 16N illustrates an example embodiment of a system detecting a recognizable feature.

[0223] Fig 17A illustrates an example embodiment of a system detecting a recognizable feature.

[0224] Fig. 17B illustrates an example embodiment of a system detecting a recognizable feature.

[0225] Fig 17C illustrates an example embodiment of a system detecting a recognizable feature.

[0226] Fig. 17D illustrates an example embodiment of a system detecting a recognizable feature.

[0227] Fig. 17E illustrates an example embodiment of a system detecting a recognizable feature.

[0228] Fig. 17F illustrates an example embodiment of a system detecting a recognizable feature.

[0229] Fig 17G illustrates an example embodiment of a system detecting a recognizable feature.

[0230] Fig 17H illustrates an example embodiment of a system detecting a recognizable feature.

[0231] Fig. 171 illustrates an example embodiment of a system detecting a recognizable feature.

[0232] Fig. 17J illustrates an example embodiment of a system detecting a recognizable feature.

[0233] Fig 17K illustrates an example embodiment of a system detecting a recognizable feature.

[0234] Fig 17L illustrates an example embodiment of a system detecting a recognizable feature.

[0235] Fig. 17M illustrates an example embodiment of a system detecting a recognizable feature.

[0236] Fig. 17N illustrates an example embodiment of a system detecting a recognizable feature.

[0237] Fig. 18A illustrates an example embodiment of a system detecting a recognizable feature.

[0238] Fig 18B illustrates an example embodiment of a system detecting a recognizable feature.

[0239] Fig 18C illustrates an example embodiment of a system detecting a recognizable feature.

[0240] Fig 18D illustrates an example embodiment of a system detecting a recognizable feature.

[0241] Fig. 18E illustrates an example embodiment of a system detecting a recognizable feature.

[0242] Fig. 18F illustrates an example embodiment of a system detecting a recognizable feature.

[0243] Fig. 18G illustrates an example embodiment of a system detecting a recognizable feature.

[0244] Fig 18H illustrates an example embodiment of a system detecting a recognizable feature.

[0245] Fig 181 illustrates an example embodiment of a system detecting a recognizable feature.

[0246] Fig. 18J illustrates an example embodiment of a system detecting a recognizable feature.

[0247] Fig. 18K illustrates an example embodiment of a system detecting a recognizable feature.

[0248] Fig 18L illustrates an example embodiment of a system detecting a recognizable feature.

[0249] Fig 18M illustrates an example embodiment of a system detecting a recognizable feature.

[0250] Fig. 18N illustrates an example embodiment of a system detecting a recognizable feature.

[0251] Fig. 19A illustrates an example embodiment of a system detecting a recognizable feature.

[0252] Fig. 19B illustrates an example embodiment of a system detecting a recognizable feature.

[0253] Fig 19C illustrates an example embodiment of a system detecting a recognizable feature.

[0254] Fig 19D illustrates an example embodiment of a system detecting a recognizable feature.

[0255] Fig 19E illustrates an example embodiment of a system detecting a recognizable feature.

[0256] Fig. 19F illustrates an example embodiment of a system detecting a recognizable feature.

[0257] Fig. 19G illustrates an example embodiment of a system detecting a recognizable feature.

[0258] Fig. 19H illustrates an example embodiment of a system detecting a recognizable feature.

[0259] Fig 191 illustrates an example embodiment of a system detecting a recognizable feature.

[0260] Fig 19J illustrates an example embodiment of a system detecting a recognizable feature.

[0261] Fig. 19K illustrates an example embodiment of a system detecting a recognizable feature.

[0262] Fig. 19L illustrates an example embodiment of a system detecting a recognizable feature.

[0263] Fig 19M illustrates an example embodiment of a system detecting a recognizable feature.

[0264] Fig 19N illustrates an example embodiment of a system detecting a recognizable feature.

[0265] Fig. 20A illustrates an example embodiment of a system detecting a recognizable feature.

[0266] Fig. 20B illustrates an example embodiment of a system detecting a recognizable feature.

[0267] Fig. 20C illustrates an example embodiment of a system detecting a recognizable feature.

[0268] Fig 20D illustrates an example embodiment of a system detecting a recognizable feature.

[0269] Fig. 20E illustrates an example embodiment of a system detecting a recognizable feature.

[0270] Fig. 20F illustrates an example embodiment of a system detecting a recognizable feature.

[0271] Fig. 20G illustrates an example embodiment of a system detecting a recognizable feature.

[0272] Fig. 20H illustrates an example embodiment of a system detecting a recognizable feature.

[0273] Fig. 201 illustrates an example embodiment of a system detecting a recognizable feature.

[0274] Fig. 20J illustrates an example embodiment of a system detecting a recognizable feature.

[0275] Fig. 20K illustrates an example embodiment of a system detecting a recognizable feature.

[0276] Fig. 20L illustrates an example embodiment of a system detecting a recognizable feature.

[0277] Fig. 20M illustrates an example embodiment of a system detecting a recognizable feature.

[0278] Fig. 20N illustrates an example embodiment of a system detecting a recognizable feature.

[0279] Fig. 21A and 21B illustrate an example embodiment of plurality of thermoregulation measurements of a subject over a period of time, wherein the data depicted in Fig. 21A is unprocessed and the data depicted in Fig 21B is represented as space-filling hexagons to facilitate point-to-point distance computation.

[0280] Fig. 22A, 22B, 22C, 22D, 22E, 22F, 22G, 22H, 221, 22J, 22K, 22L, 22M, 22N, 220 illustrate an example of embodiments of binned pluralities of thermoregulation measurements of a subject over a period of fifteen days.

[0281] Fig. 23 illustrates an example embodiment of a plurality of thermoregulation resilience scores of a subject over a period of time defined as one month.

[0282] Fig. 24 illustrates an example embodiment of a plurality of thermoregulation resilience scores of a subject over a period of time defined as one month.

[0283] Fig. 25 illustrates an example embodiment of a plurality of thermoregulation resilience scores of a subject over a period of time defined as one month.

[0284] Fig. 26 illustrates an example embodiment of a plurality of thermoregulation resilience scores of a subject over a period of time defined as one month.

[0285] Fig. 27 illustrates an example embodiment of a plurality of thermoregulation measurements of three subjects over a period of time defined as one day.

[0286] Fig. 28 illustrates an example embodiment of a plurality of thermoregulation resilience scores of a plurality of individuals plotted as a function of a combination of a plurality of thermoregulation resilience scores and a plurality of other biological measurements.

[0287] Fig. 29 illustrates an example embodiment of a plurality of thermoregulation resilience scores of a plurality of individuals plotted as a function of a combination of a plurality of thermoregulation resilience scores and a plurality of other biological measurements.

[0288] Fig. 30 illustrates example objectives of the disclosed technology.

[0289] Fig. 31 illustrates additional example objectives of the disclosed technology.

[0290] Fig. 32 illustrates certain problems faced by telemedicine call centers.

[0291] Fig. 33 illustrates an example of a solution to problems faced by telemedicine call centers according to some embodiments of the disclosed technology.

[0292] Fig. 34 illustrates an example workflow consistent with the solution described in connection with Fig. 33.

[0293] Fig. 35 shows experimental data gathered from monitoring treatment effect on a fetal-maternal system.

[0294] Fig. 36A shows experimental data gathered from monitoring a healthy pregnant woman. Fig. 36B shows experimental data gathered from monitoring a pregnant woman having the HELLP syndrome.

[0295] Fig. 37 shows experimental data gathered from monitoring a subject in his mid 40’ s that uses a CPAP machine and has sleep disorders including apnea and a history of heart issues.

[0296] Fig. 38 shows experimental data gathered from monitoring a subject in his mid 40’ s that uses a CPAP machine and has sleep disorders including apnea and a history of heart issues.

[0297] Fig. 39 shows experimental data gathered from monitoring a subject in his mid 40’ s that uses a CPAP machine and has sleep disorders including apnea and a history of heart issues.

[0298] Fig. 40 shows experimental data gathered from monitoring a subject in his mid 40’ s that uses a CPAP machine and has sleep disorders including apnea and a history of heart issues.

[0299] Fig. 40A shows experimental data gathered from monitoring a subj ect in his mid 40’ s that uses a CPAP machine and has sleep disorders including apnea and a history of heart issues.

[0300] Fig. 41 shows experimental data gathered from monitoring a subject in his mid 40’s that uses a CPAP machine and has sleep disorders including sleep apnea and allergic episodes.

[0301] Fig. 42 shows experimental data gathered from monitoring a subject in his mid 40’s that uses a CPAP machine and has sleep disorders including sleep apnea and allergic episodes.

[0302] Fig. 43 shows experimental data gathered from monitoring a subject in his mid 40’s that uses a CPAP machine and has sleep disorders including sleep apnea and allergic episodes.

[0303] Fig. 44 shows experimental data gathered from monitoring a subject in his mid 40’s that uses a CPAP machine and has sleep disorders including sleep apnea and allergic episodes.

[0304] Fig. 45 shows experimental data gathered from monitoring a subject in his mid 40’s that uses a CPAP machine and has sleep disorders including sleep apnea and allergic episodes.

[0305] Fig. 46 shows experimental data gathered from monitoring a subject in his mid 40’s that uses a CPAP machine and has sleep disorders including sleep apnea and allergic episodes.

[0306] Fig. 47 shows experimental data gathered from monitoring a subject in his mid 40’s that uses a CPAP machine and has sleep disorders including sleep apnea and allergic episodes.

[0307] Fig. 48 shows experimental data gathered from monitoring a subject in his mid 40’ s that uses a CPAP machine and has sleep disorders including sleep apnea involving approximately less than ten second pauses in heart rhythm.

[0308] Fig. 49 shows experimental data gathered from monitoring a subject in his mid 40’ s that uses a CPAP machine and has sleep disorders including sleep apnea involving approximately less than ten second pauses in heart rhythm.

[0309] Fig. 50 shows experimental data gathered from monitoring a subject in his mid 40’ s that uses a CPAP machine and has sleep disorders including sleep apnea involving approximately less than ten second pauses in heart rhythm.

[0310] Fig. 51 shows experimental data gathered from monitoring a subject in his mid 40’ s that uses a CPAP machine and has sleep disorders including sleep apnea involving approximately less than ten second pauses in heart rhythm.

[0311] Fig. 52 shows experimental data gathered from monitoring a subject in his mid 40’ s that uses a CPAP machine and has sleep disorders including sleep apnea involving approximately less than ten second pauses in heart rhythm.

[0312] Fig. 53 shows experimental data gathered from monitoring a subject in his mid 40’ s that uses a CPAP machine and has sleep disorders including sleep apnea involving approximately less than ten second pauses in heart rhythm.

[0313] Fig. 54 shows experimental data gathered from monitoring a subject in his mid 40’ s that uses a CPAP machine and has sleep disorders including sleep apnea involving approximately less than ten second pauses in heart rhythm.

[0314] Fig. 55A illustrates an example embodiment of a plurality of perfusion measurements of a subject’s right index and left index plotted as a function of time.

[0315] Fig. 55B illustrates an example embodiment of a plurality of perfusion measurements of a subject’s right index and left index where asymmetry was detected plotted as a function of time.

[0316] Fig. 56 illustrates an example embodiment of a wearable device that is configured to collect a plurality of data indicative of at least one emergent factor of a user.

[0317] Fig. 57A illustrates a physical parameter plotted as a function of time.

[0318] Fig. 57B illustrates a counterfactual gap plotted as a function of time.DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0319] All patents, patent applications, and other publications, including all sequences disclosed within these references, referred to herein are expressly incorporated herein by reference, to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference. All documents cited are, in relevant part incorporated herein by reference in their entireties for the purposes indicated by the context of their citation herein. However, the citation of any document is not to be construed as an admission that it is prior art with respect to the present disclosure.

[0320] Currently there is not an agreed upon measure of health. Health is frequently defined as the absence of disease (symptoms). Disease metrics are lagging indicators of failing health, and thus do not reflect health in an affirmative sense, and are not in and of themselves optimizable vis-a-vis real health, not disease, outcomes. For example, a challenge of a molecular biomarker is in establishing its significance relative to known physiology and homeostasis.Obtaining Emergent Factors of Users

[0321] The generation, analysis, and use of data relating to the health condition or health capacity of biological systems (e.g., a human user) has been explored. More specifically, the use of sensors, and combinations of sensors, for capturing data related to the health capacity of biological systems has also been explored. Health capacity can refer to the resilience (adaptivity) of a system expressed primarily by its ability to persist or achieve some core function. To assess the health capacity of a system, one may interpret the emergent factors of the system. Emergent factors may refer to events, deviations from norm or other time dependent patterns in some measurable parameter of the system that can be observed directly or indirectly. Emergent factors or properties may also refer to properties of a biological system that are not readily predictable from the functions of the component parts of the system. Examples of emergent properties can include amphotericity, conductivity, solvation capacity,ion mobility, oxidation -reduction potential, ligan association, hydration, electrolysis, thermal conductivity, heat capacity, thermal absorptivity, adhesion, cohesion, transparency, turbidity, incompressibility, polarity, dipolarity, dipole movement, diamagnetism, voltage range of the liquid phase, temperature range of the liquid phase, abundancy, and speciation, flux of energy, momentum, particles or other substances, heat elimination, either as an absolute, statis value of heat elimination or as a periodic function, for example, a circadian periodicity of heat elimination.

[0322] Physicists have encountered the problem of emergent factors before (e.g., magnetism) and have concluded that it may be advantageous to identify a thermodynamic parameter which summarizes the order, rather than to attempt to measure molecular details of that order directly. In fact, all order is associated with missing energy. (See, for example,For example, when studying complex materials, physicists look for anomalous specific heats as the bellwether of hidden organization. Landau defined the order parameter (See, for example, https: / / en.wikipedia.org / wiki / Landau_theory): a useful mathematical device which quantifies the thermodynamic character and robustness of the underlying order. Our insight is based, in part, on the concept that the organization of living systems has associated thermodynamic signatures analogous to order parameters. And, only these biological order parameters will enable highly accurate learning with small sample sizes. Furthermore, it is likely that such a thermal signature may inform us of the robustness of biological order, physiologic reserve and health state.

[0323] The underlying health condition can be monitored or assessed by use of a wearable device that can collect and / or monitor the health capacity of biological systems. It may include at least one wearable thermodynamic sensor that can be configured to measure an emergent factor of the human, wherein the emergent factor is the temporal alignment of heat production and heat elimination of the human, the temporal alignment relating to the circadian rhythm of the human, and based on the emergent factor, generate measured data comprising heat flux data over time. The wearable device may also capture heat flux data, wherein at least one health capacity is a basal metabolic status, and at least one emergent factor is the temporal alignment of heat production and heat elimination of the biological system. The wearable device may include an array of sensors that record health metrics and capture the data. In some embodiments, the wearable device includes at least one pair of wearable thermodynamicsensors that are placed symmetrically, about an axis of symmetry of a subject. This at least one pair of wearable thermodynamic sensors may be configured to capture heat flux data from biological compartments, located symmetrically with respect to an axis of symmetry of the subject, and allow for substantially simultaneous monitoring of the symmetric biological compartments. Each of the symmetrically placed sensors may include an array of sensors that record health metrics and capture the data essentially substantially simultaneously. The wearable device may continuously record select “energy signatures” metrics or indicators of health for the subject. In some embodiments, the wearable device requires low cost and low power, enabling accessibility and continuous data capture in real-time. In some embodiments, the wearable device comprises at least one multi-modality sensor system that measures electrochemical, mechanical, structural, thermal, and / or energetic properties reflective of homeostasis and cell physiology. The wearable device can comprise any number of sensors.

[0324] The principal mode of signal degradation of the sensors may have a slow response time. In other words, the changes in the measured value by the sensors can lag behind the actual physiologic change. This can create a counterfactual gap. For example, a counterfactual gap can occur when there is a difference between the counterfactual measurement and the measured value obtained by the device. The counterfactual measurement is the actual physical measurement which would occur at a specific location if a measuring device were not present at that location. A counterfactual gap can occur even when a measurement device is perfectly calibrated. A counterfactual gap can occur for a broad class of physical properties whether thermal, electrical, or acoustic-mechanical. For example, the counterfactual gap may occur as a result of a motherboard or circuit board within the device retaining and / or producing heat. This can interfere with the measurements collected or sensed by the device, thereby creating a counterfactual gap between the sensed measurement and the counterfactual measurement. While an attempt to isolate the motherboard using special materials that may shield the sensors from the heat produced and / or retained by the motherboard, the attempt may be unsuccessful due to the expense of materials, complicated manufacturing and assembly techniques needed, and / or constantly varying temperature of the motherboard, among other difficult issues to overcome. A counterfactual gap may not occur for biological measurements such as heart rate because a heart rate measurement may not be altered by a wearable heart rate monitor in the same way that a change in temperature can beaffected by a temperature measuring device. A counterfactual gap can also occur due to the thermal mass of the temperature measuring device.

[0325] In some embodiments, the wearable device may include at least one pair of sensors located on a first side of the device and a second side of the device. The wearable device may further include at least one additional internal temperature sensor located at any position in between the at least one pair of sensors. The at least one additional internal temperature sensor may have a different time constant than the at least one pair of sensors. For example, the at least one additional internal temperature sensor may have a slower equilibration time compared to the equilibration time of the at least one pair of sensors. This can help to collect data that more accurately and precisely represents the counterfactual measurement. In some embodiments, the wearable device can be worn by the user in a symmetrical configuration.

[0326] In some aspects, the disclosed wearable device is designed and configured to quantify physiologic energy outputs (for example, peripheral heat and physical activity). This device is benchmarked against gold-standard physiologic endpoints in multiple human studies. Metrics for such benchmarking involve a signal with high accuracy with training sets as small as 25 samples. A robust structure is identified in human heat signatures and serves as a direct measure of the autonomic processes underlying homeostasis (i.e., biological organization). Specifically, the device provides a means for the non-invasively detection of a thermal signature of an inflammatory cascade before any change in core temperature. This observation has ramifications from the perspectives of thermal physics, and transformational biological applications.

[0327] In some aspects, the wearable device described herein utilizes a physical model of temperature homeostasis, inspired by the function of the hypothalamus, to interpret the health significance of an individual’s thermal signature. By measuring the principal data streams which the hypothalamus integrates (heat and body temperature), the device allows for the characterizing of the basis of homeostasis and physiologic reserve - including differences between the sexes - and for defining gender-specific metrics that are relevant to trauma injury treatment. The wearable device continuously and contextually measures these principal data streams moderated by the hypothalamus, and provides a means for characterizing bothindividuals and gender groups by measuring their thermal signature to assess what we call a thermoregulatory phenotype.

[0328] In some aspects, the disclosed technology is based, in part, on the utilization of a novel physical model of temperature homeostasis, providing a means for understanding and / or interpreting the health significance of an individual’s thermal signature (thermal phenotype) and for acting upon that interpretation in a variety of ways. The non-invasive wearable device continuously senses thermal signature of body heat, distinct from and superior to simple skin thermometry, and requires no charge or battery replacement for periods as long as several months. Because the disclosed technology measures body heat, which is fundamentally related to temperature homeostasis, it avoids challenges in using the typical molecular biomarker.

[0329] In some aspects, the disclosure provides devices and methods for continuously and contextually characterizing an individual’s metabolic state by measuring their thermal signature to assess what is referred to as a thermoregulatory phenotype. Changes relative to this phenotype are sensitive indicators of change in health state. The device is designed and configured such that it delivers, in human use, general associations between an individual's thermal signature and physiologic reserve. Additional information is available within the thermal signature to an actionable assessment of health. Clinical studies are designed to gather data that will serve as a novel vital sign of homeostasis as well as an aggregate health signature, with applicability to early detection of many disease states, management of individual wellness.

[0330] In some aspects, the disclosure provides devices and methods for bilateral symmetry monitoring of a subject’s thermoregulation system. This can include perfusion within the subject. The device is, in some embodiments, designed to have at least one pair of sensors symmetrically placed on the subject to continuously monitor and analyze how fluid (i.e., blood) is flowing through the body. The device is, in some embodiment, further designed to include at least one additional internal heat sensor; this at least one additional sensor improves the accuracy and precision of the determination of at least one emergent factor of the subject. Both determinations are, in preferred embodiments, closely related to temperature and / or heat flux gradients which can be generated from the thermoregulation measurements collected from the pair of sensors.

[0331] The wearable device illustrated in FIG. 32 is configured to be used to measure a plurality of data indicative of at least one emergent factor of a biological system, e.g., a user. For example, ambient temperature of the user is measured through a first sensor, skin temperature of the user is measured through a second sensor, and the first sensor and the second sensor are decoupled. In some preferred embodiments, at least one additional sensor is located spatially in between the first sensor and the second sensor. The at least one additional sensor may be an internal temperature sensor and may have a different time constant that the first sensor and the second sensor. For example, the at least one additional internal temperature sensor may have a slower equilibration time compared to the equilibration time of the first sensor and the second sensor. This can aid in the accuracy and / or precision of the measurements of the plurality of data indicative of at least one emergent factor more accurately and precisely. Heat elimination of the user over time can be estimated based on differential ambient temperature, and heat production of the user over time can be estimated based on the measured skin temperature. Thus, basal metabolic status of the user can be estimated based on temporal alignment of heat elimination and heat production.

[0332] In addition, in certain embodiments, a quasiperiodic rhythm of the user may be obtained based on the measured ambient temperature and skin temperature, where the quasiperiodic rhythm is of seconds-timescale, of minutes-timescale, ultradian, circadian, circalunar, or of yearly timescale. A variability of the quasiperiodic rhythm across a predetermined amount of time may be obtained, and thus a health capacity of the user may be determined based on the variability of the quasiperiodic rhythm.

[0333] In some preferred embodiments, heat elimination of the user over time can be estimated based on differential ambient temperature, heat production of the user over time can be estimated based on skin temperature, a basal metabolic status of the user can be estimated based on temporal alignment of heat elimination and heat production, and thus a health capacity can be determined by applying a time-dependent function to the estimated basal metabolic status (for example, the time-dependent function is derived from the quasiperiodic rhythm of the user).

[0334] In some preferred embodiments, the data relates to the health capacity of the user. In some embodiments, the plurality of data comprises heat flux data. In some embodiments, the data relates to the heat flux of the user. In some embodiments, the data relatesto the heat flux of the user over multiple circadian cycles. In some embodiments, at least one health capacity is a basal metabolic status, and at least one emergent factor is a temporal alignment of heat production and heat elimination. In some embodiments, the temporal alignment is related to at least one quasiperiodic rhythm of the user. In some embodiments, the at least one quasiperiodic rhythm is a circadian rhythm.

[0335] In some preferred embodiments, the device continuously and contextually characterizes a user’s metabolic state by measuring the user’s thermal signature and assessing the user’s thermoregulatory status. Changes relative to the user’s thermoregulatory status are sensitive indicators of change in the user’s health state. The device may be configured such that it determines general associations between a user’s thermal signature and physiologic reserve. The device may indicate a vital sign of homeostasis or a health signature of the user, and may be utilized in early detection of disease states and management of individual wellness.

[0336] The wearable device illustrated in FIG. 31 may be used to measure a plurality of data indicative of at least one emergent factor of a biological system, e.g., a user. In some embodiments, the first sensor and the second sensor both utilizes a heat sensor (e.g., Si7051 Digital Temperature Sensor from Silicon Labs). In preferred embodiments, the first sensor measures skin temperature, while the second sensor measures ambient temperature. In preferred embodiments, at least one additional internal temperature sensor may be placed in between the first sensor and the second sensor. The at least one additional internal temperature sensor may have a different time constant that the first sensor and the second sensor. For example, the at least one additional internal temperature sensor may have a slower equilibration time compared to the equilibration time of the first sensor and the second sensor. This can help to measure the plurality of data indicative of at least one emergent factor more accurately and precisely. Heat elimination of the user over time can be estimated based on differential ambient temperature, and heat production of the user over time can be estimated based on the measured skin temperature. Thus, basal metabolic status of the user can be estimated based on temporal alignment of heat elimination and heat production.

[0337] In some embodiments, in order to improve the thermal response time of the disclosed device, reflective foil, or thermally super-insulating materials made of reflective materials, may be included inside the device to reflect away radiative heat transfer between the sensor plate(s) and the battery / PCB circuit board. Some examples of thermally super-insulatingmaterials made of reflective materials are described in National Aeronautics and Space Administration (NASA) Publication No. NASA CR-2507, “Applications of Aerospace Technology: Reflective Superinsulaton Materials” (January 1975) (available at the disclosure of which is incorporated herein inits entirety. In some embodiments, in order to improve the thermal response time of the disclosed device, the device enclosure may be designed to have inner and outer walls separated by a mesh of air pockets to minimize thermal transfer through the walls of the device. In some examples, such a design may be achieved by 3D printed structures, such as those described in “Grabowska, B. and Kasperski, J., 2020. The Thermal Conductivity of 3D Printed Plastic Insulation Materials — The Effect of Optimizing the Regular Structure of Closures, Materials, 73(19), p.4400”, the disclosure of which is incorporated herein in its entirety. Additionally or alternatively, such mesh of air pockets / 3D printed structures may be used to minimize heat transfer between the sensor plate(s) and the battery / PCB circuit board.

[0338] In some embodiments, wearable device may have an isolation material designed to thermally decouple at least one of the sensors from the circuit board and the battery, as discussed above. In some embodiments, the wearable device includes a housing and a circuit board located within the housing, the circuit board having a first side of the circuit board opposite a second side of the circuit board. In some embodiments, the wearable device includes a first sensor located on the first side of the circuit board, a second sensor located on the second side of the circuit board, and a battery positioned between the first sensor and the second sensor. In some embodiments, the first sensor and the second sensor both utilizes a heat sensor (for example, Si7051 Digital Temperature Sensor from Silicon Labs). In some embodiments, the isolation material has the ability to, at least to a substantial extent, thermally decouple the first sensor from the circuit board and the battery. In some embodiments, the isolation material substantially thermally decouples the second sensor from the circuit board and the battery. In some preferred embodiments, the at least one additional internal temperature sensor is located spatially in between the first sensor and the second sensor. The at least one additional internal temperature sensor may have a different time constant that the first sensor and the second sensor. For example, at least one additional internal temperature sensor may have a slower equilibration time compared to the equilibration time of the first sensor and the second sensor. This can help to measure the plurality of data indicative of at least one emergent factor moreaccurately and precisely. In some embodiments, the device further includes a processor and a firmware. In some embodiments, the isolation material substantially thermally decouples the first sensor and the second sensor from the circuit board and the battery. In some embodiments, the isolation material is located on the first side of the circuit board. In some embodiments, the isolation material is located on the second side of the circuit board. In some embodiments, the isolation material is located on the first side of the circuit board and the second side of the circuit board. In some embodiments, the isolation material includes a 3D printed plastic insulation material, a reflective foil or a reflective thermally super-insulating material as described above. In some embodiments, the 3D printed plastic insulation material is configured to minimize thermal transfer between the first sensor and / or the second sensor and the circuit board and the battery. In some embodiments, the reflective foil or the reflective thermally super-insulating material is configured to reflect away radiative heat transfer between the first sensor and / or the second sensor and the circuit board and the battery. In preferred embodiments, at least one additional internal temperature sensor may be placed in between the first sensor and the second sensor. The at least one additional internal temperature sensor may have a different time constant that the first sensor and the second sensor. For example, the at least one additional internal temperature sensor may have a slower equilibration time compared to the equilibration time of the first sensor and the second sensor. This can help to measure the plurality of data indicative of at least one emergent factor more accurately and precisely.

[0339] The wearable devices may be used to measure a plurality of data indicative of at least one emergent factor of a biological system, e.g., a user. Heat elimination of the user over time can be estimated based on differential ambient temperature, and heat production of the user over time can be estimated based on the measured skin temperature. Thus, basal metabolic status of the user can be estimated based on temporal alignment of heat elimination and heat production.

[0340] The embodiments described are examples. Various changes could be made in the above devices and methods without departing from the scope of the invention. All subject matter described in this disclosure, including the accompanying figures, is illustrative and not limiting.Detecting Emergent Properties and Improving Health Outcomes

[0341] The generation, analysis, and use of data relating to the health capacity of biological systems has been explored. More specifically, the use of sensors, and combinations of sensors, for capturing data related to the health capacity of biological systems has also been explored. Health capacity can refer to the resilience (adaptivity) of a system expressed primarily by its ability to persist or achieve some core function. To assess the health capacity of a system, the one may interpret the emergent factors of the system.

[0342] The Scholander-Irving model depicts a pattern of changes of resting metabolic of an endothermic homeotherm over a range of ambient temperatures. Per Scholander and Laurence Irving were interested researching how warm-blooded birds and mammals maintain body temperature. With such interest, they discovered warm-blooded birds and mammals maintain body temperature by balancing their rate of metabolic heat production and the rate of heat lost to the environment.

[0343] However, in relation to humans, one may have limited knowledge regarding their health capabilities, health status, disease state, general state of health, etc. One may gain limited insight into these areas of knowledge only with sophisticated physiologic measurement tools, to which few may have access.Definitions

[0344] “Analysis,” as used herein, refers to any description of characteristics or features of any observed sequence of information. Types of analysis include, but are not limited to, analyzing raw measurements such as wherein the numerical values of raw measurements can be used directly as features; resampled measurements such as wherein a set of raw measurements are grouped by and represented by the mean value of the group or some other group statistic; distributional representation of measurements such as wherein the distribution of a collection of measurements may be represented in regularly spaced bins or irregular bins which have been determined by some other process such as a Gaussian Mixture model; statistical tests over sets of measurements such as the Hartigan DIP test of multimodality or Stationarity tests which can be used to detect changes over time; fit parameters from physical models such as a 3-compartment model of body heat content, and extensions thereof or fitting hemodynamic parameters of human circulatory system; parameters of universal mathematicalmodels such as those which cannot be reduced to any simple parameter of a physical model and which may include non-physical control parameters which summarize structure in the dataset, including, for example, Bifurcation parameter of conjugate logistic map and Eigenvalues of a hessian matrix of a "sloppy" physical model fit.

[0345] “Biological system,” as used herein, refers to any network of biologically relevant entities. In its broadest aspect, a biological system is any network of chemical reactions which exists as a persistent non-equilibrium configuration by its own devices. Biological systems encompass and span differing scales and are determined based different structures depending on the nature of the biological system. Examples of a biological system on a large scale include, for example, a population of microscopic organisms, a homogenous population of similar organisms living in proximity to one another (for example, a cell culture or a community of humans), a heterogeneous population of organisms living in a single ecosystem, biological networks. Examples of biological systems on a smaller scale include an individual organism, for example a single mammal such as a human, an organ or tissue system within such an organism, cellular organelle systems, or artificial life systems.

[0346] Counterfactual gap,” as used herein, refers to an observed lack of accuracy arising from use of the difference between two measured values, obtained from two separate sensors, to calculate the value of an emergent factor. A measurement influences by a counterfactual gap is referred to as a “counterfactual measurement.” For example, when two temperature sensors, one located adjacent to the skin and another located adjacent to the ambient environment, are used to calculate heat flux by determining the difference of their observed values, a counterfactual gap will be observed between (i) the heat flux value calculated based solely on the difference of the values obtained from the two sensor and (ii) the accurate value of heat flux. As described herein, use of a value obtained from a suitably located at least additional sensor in the context of calculating a value for heat flux can correct for such a counterfactual gap. A value, typically based on a measured value from at least one additional sensor, that aid in improving the accuracy of a counterfactual measurement may be representative of a counterfactual gap.

[0347] “Data stream,” as used herein, refers to a sequence of digitally encoded coherent signals (packets of data or data packets) used to transmit or receive information that is in the process of being transmitted. A data stream may be a set of extracted information froma data provider, and may comprise, for example, a sequence of ordered lists of elements (representing different signal components) and an associated sequence of timestamps.

[0348] “Disease,” as used herein, broadly refers to any condition that causes pain, dysfunction, distress, or death to the person afflicted. Thus, disease may include one or more injuries, disabilities, disorders, syndromes, infections, isolated symptoms, deviant behaviors, and atypical variations of structure and function. Diseases may affect biological organisms not only physically, but also mentally. Thus, in the case of a human afflicted with a disease, contracting and living with a disease can alter the affected person's perspective on life. Examples of diseases include those identified and classified on the World Health Organization’s 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-10). Such diseases that may affect humans include, infectious and parasitic diseases, neoplasms, diseases of the blood and blood-forming organs, disorders involving the immune mechanism, endocrine diseases, nutritional diseases, metabolic diseases, mental and behavioral disorders, diseases of the nervous system, diseases of the eye and adnexa, diseases of the ear and mastoid process, diseases of the circulatory system, diseases of the respiratory system, diseases of the digestive system, diseases of the skin and subcutaneous tissue, diseases of the musculoskeletal system and connective tissue, diseases of the genitourinary system, diseases associated with pregnancy, childbirth and the puerperium, diseases originating in the perinatal period, congenital malformations, deformations and chromosomal abnormalities, as well as injuries, poisoning, and consequences of external causes.

[0349] “Discords,” as used herein, refers to any unusual or anomalous subsequences within a time series.

[0350] “Energy expenditure,” as used herein, in its most general sense, relates to the measurement of parameters that reflect heat or work in a biological system. “Energy expenditure” also refers to an entropy producing (irreversible) outlay of free energy to power an adaptive task within a biological system. An energy expenditure is largely irreversible (entropy -producing) so it represents energy which cannot be retrieved for other tasks. “Energy homeostasis” or “homeostatic control of energy balance,” as used herein, refers to a biological process that involves the coordinated homeostatic regulation of food intake (energy inflow) and energy expenditure (energy outflow).

[0351] ‘Emergent factor” or “emergent property” as used herein, refer to properties of a system not found in a part, or readily deducible from a detailed inventory and analysis of the parts contained within a system. They may be revealed by events, deviations from norm or other time dependent features in some measurable parameter of the system. Emergent properties may be observed directly or indirectly. Examples of emergent properties include amphotericity, conductivity, solvation capacity, ion mobility, oxidation-reduction potential, ligand association, hydration, electrolysis, thermal conductivity, heat capacity, thermal absorptivity, adhesion, cohesion, transparency, turbidity, incompressibility, polarity, dipolarity, dipole moment, diamagnetism, voltage range of the liquid phase, temperature range of the liquid phase, abundancy, and speciation, flux of energy, momentum, particles or other substances. Emergent factors also include thermoregulation and heat elimination, either as an absolute, static value of heat elimination or as a periodic function, for example a circadian periodicity of heat elimination.

[0352] “Feature,” as used herein, refers to any descriptive aspect, characteristic, attribute, quality, trait or property of a sequence, sub-sequence or datum of information. Examples of features include, but are not limited to, Chaotic, Repeating, Predictable, Spirals, Meanders, Rotations, Orbits, Dense / Diffuse, Symmetric / Asymmetric, Regular / Irregular / Intermittent, Periodic / Aperiodic, Cyclical, Similar / Dissimilar,Dynamic / Static, Rate of change, Direction of change, Inflections, Sequential, State Transitions, Anomalies / Outliers, Interruptions / Breaks, Oscillating / Persistent, Damped / Undamped, Increasing / Decreasing, Improving / Declining, Intersecting / Non- intersecting, Linear / Non-linear, Homogenous / Diverse, Monotonic / Polytonic, Serial / Out of order, Balanced / Imbalanced, Long / Short, Range, Modes / Medians / Averages / Standard Deviations, Equilibrium / Non-equilibrium, Stable / Unstable, Controlled / Uncontrolled, Homeomorphic / Isomorphic, Monomodal / Multimodal, Bounded / Unbounded, Trending, Exceeding / Not Exceeding a Threshold, Rising / Falling, Growing / Shrinking, Speeding up / Slowing down, Constant / / Fluctuating, Peak / Valley, Zenith / Nadir, Asymptotic, Sudden, Steep / Gradual, Logistical / Polynomial mapping, Discrete / Continuous / Spaced / Discontinuous, Proportional, Gains / Losses, Energetic / Active / Inactive, Complexity / Simplicity,Attractors / Repellers, States, State transitions, Curves, Manifolds, Trajectories, Inflation / Deflation, Fractals, Momentum, Iterations, Dissipating / Developing,Presence / Absence, Perturbations, Variations / Invariation, Limited / Unlimited,Converging / Diverging, Velocity, Derivatives / Integrals, Exponential, Minimum / Maximum, Forward / Reverse / Inverse, Pulsing, Coalescing, Saturated / Unsaturated, Gain, Gradients, Deep / Shallow, Bifurcations / Splits, Points / Lines / Surfaces, Early / Late, and Distributed / Grouped.

[0353] “Forecasting,” as used herein, refers to any prediction of future data points within a time series based on what comes before the future data points.

[0354] ‘Health,” as used herein, refers to the baseline of function of a biological system, and may also be referred to as homeostasis. Health is not merely the absence of disease because health is an affirmative state independent of disease. Health is related to the ability of a biological system or organism to successfully adapt to a variety of challenges without significant loss of function. Physiologists, for example, may describe health as the sufficiency of a form of stored energy they call physiologic reserve — the ability of a human to positively respond to a stress. Physicists, as another example, may describe health as a capacity to incorporate, transform, and dissipate energy to persist. Cell biologists, as another example, may describe health as the baseline state of homeostasis — the ability of a cell or tissue to autoregulate. Biochemists, as another example, may describe health as the control of anabolic and catabolic reactions in a metabolic network critical to biological function.

[0355] ‘Health capacity,” as used herein, is the resilience (adaptivity) of a system expressed primarily by its ability to persist or achieve some core function. The adjectives associated with high or low health capacity are ‘fit’ and ‘frail,’ respectively. Low health capacity- “Frailty”- increases the risk of disease or injury and the ability to withstand external stresses. Disease diminishes function such that health capacity may be diminished as a result. High health capacity “Fitness”- decreases the risk of injury and increases the ability to perform and to withstand external stresses. Early interception of disease can preserve and maintain health capacity and careful management of health capacity can prevent disease. Health capacity may be considered a correlation of a state, or energy budget, with a function of a biological system that defines the ability of the biological system to persist. Health capacity may be comprised of several quantities which may not necessarily be compared in the same manner or reduced to a single score. Data analytics may be used to discover the relationship between raw measurements and an abstract health capacity score. Dimensionality reduction or machinelearning methods may be used to learn health capacity scores based on the raw measurement time series data and to predict adaptation and health outcomes.

[0356] “Health capacity rules,” as used herein, refers to the minimal set of attributes of an “energy budget” required to confer “health capacity.”

[0357] “Health outcome,” as used herein, refers to a change in the health status of an individual or of a group of individuals. Health outcome, in some embodiments, can be attributed to intervention, and can include events or results in patient health status or quality of life; patient, health services provider, and population attitudes and behavior; and new evidence, research, prevention strategies, treatments, and care models. Health outcomes, in certain embodiments, may be measured clinically (physical examination, laboratory testing, imaging), self-reported, or observed (such as gait or movement fluctuations seen by a health services provider or caregiver, or responsiveness and / or willingness to respond in the context of inquiries made by a health services provider or caregiver).

[0358] “Homeostasis,” as used herein, refers to processes and mechanisms for the regulation of the internal environment of a biological system, generally to limit variability of a state and / or maintain the status of a state. An example of a homeostasis mechanism at the organismal level is sweating, which serves to reduce temperature. An example of a homeostasis mechanism at the biochemical and cellular level is redox control and its regulation of metabolism.

[0359] ‘Infection,” as used herein, refers to an invasion of a biological system, typically of an organism, by one or more agents (or pathogens) that are not generally associated with the biological system. The agent is often a disease-causing agent. Infection also includes the propagation and multiplication of the agent, and the reaction of host biological system or organism. Infection also includes the generation of toxins, by or as a proximal cause of, the agent. Infectious disease, sometime referred to as “transmissible disease” or “communicable disease,” is a disease state resulting from an infection. Pathogens include, but are not limited to, viruses and related agents such as viroids and prions, bacteria, fungi which may be further classified, for example, as Ascomycota, including yeasts such as Candida, filamentous fungi such as Aspergillus, Pneumocystis species, and dermatophytes, Basidiomycota, including the human-pathogenic genus Cryptococcus, parasites which may be further classified, for example, as unicellular organisms (including, for example, malaria, Toxoplasma, Babesia),Macroparasites (including worms or helminths) such as nematodes such as parasitic roundworms and pinworms, tapeworms (cestodes), and flukes (trematodes, such as schistosomiasis), arthropods such as ticks, mites, fleas, and lice, can also cause human disease, which conceptually are similar to infections. Invasion of an animal body, such as a human body, by macroparasites may also be termed infestation but is consider, as used herein, to be a form of infection.

[0360] “Inflammation,” as used herein, refers to a particular, generic set of biological responses of body tissues to stimuli, such as pathogens, damaged cells, or irritants. Inflammation (and the associated condition, pre-inflammation) is a response involving immune cells, blood vessels, and molecular mediators that, at least in part, serves to eliminate the initial cause of cell injury, clear out necrotic tissues damaged from the original insult and initiate tissue repair. Signs of inflammation include increased heat, pain, redness, swelling, and loss of function. Inflammation may be considered a mechanism of innate immunity, as compared to adaptive immunity, which would be specific to a particular pathogen. Inflammation may be classified as acute or chronic. Acute inflammation is the initial response of the body to a stimuli and may be achieved by the increased movement of plasma and leukocytes (especially granulocytes) from the blood into the injured tissues. A series of biochemical events propagates and matures the inflammatory response, involving the local vascular system, the immune system, and various cells within the injured tissue. Chronic inflammation, often termed prolonged inflammation, may cause a progressive shift in the type of cells present at the site of inflammation, such as mononuclear cells, and is characterized by substantially simultaneous destruction and healing of tissue.

[0361] “Metabolism,” as used herein, refers to transformation of energy by converting chemicals and energy into cellular components (anabolism) and decomposing organic matter (catabolism). Living things require energy to maintain internal organization (homeostasis) and to produce the other phenomena associated with life.

[0362] “Modeling,” as used herein, refers to any descriptive representation or understanding of the processes or operation of biological systems which approximate such real world processes or operations.

[0363] “Motifs,” as used herein, refers to any repeated subsequences of information.

[0364] ‘Novelets,” as used herein, refers to any emerging motif which is both new and growing relative to the historical distribution of a sequence of information.

[0365] “Oncogenesis,” as used herein, refers to the formation of a cancer, whereby normal cells are transformed into cancer cells, also termed “tumorigenesis” or “carcinogenesis”. The process is characterized by changes at the cellular, genetic, and epigenetic levels and abnormal cell division. Mutations in DNA and epimutations disrupt processes involved in the programming and regulation of the normal balance between proliferation and programmed cell death.

[0366] “Response (to stimuli),” as used herein, refers to an action or modification in a biological system that results from external stimulus. A response may take any of several forms. For example, in the case of a unicellular organism, it may be the contraction resulting from exposure to the presence of chemicals in the environment. As another example, response may be a complex set of reactions involving all the senses of multicellular organisms. A response is often expressed by motion; for example, the leaves of a plant turning toward the sun (phototropism), and chemotaxis.

[0367] “Shapelets,” as used herein, refers to any small subsequences in a sequence of information which may be indicative of the state of a system.

[0368] “Snippets,” as used herein, refers to any subsequences which are representative of the sequence of information.General Criteria for Identifying and Selecting Emergent Factors

[0369] Emergent factors or emergent properties are identified and selected, for measurement, based on various criteria. Generally speaking, emergent factors that may be directly measured are preferred over emergent factors that may only be measured indirectly. Techniques used to measure (directly or indirectly) that are less invasive are preferred over techniques that are more invasive. Measurements that are reliable and associated with a single emergent factor, rather than multiple emergent factors, are preferred.Example Embodiments Relating to Detecting and Improving Physiologic Metrics Correlating to One of More Patterned Stresses

[0370] Measuring a physiologic metric correlating to a patterned stress can be useful in situations where peoples’ breathing and / or physical activity (e.g., running, swimming, walking, etc.) synchronize and have periodicity that can be measured. Similarly, the measurement of a physiological metric correlating to a patterned stress can also be useful in situations where the period is measured by a nonquantifiable routine (e.g., a trail route).

[0371] In some embodiments, the patterned stress may be a known stress (e.g., a physical activity performed by the subject). The patterned stress may arise from the physical exertion performed by the subject. In some examples, the patterned stress can be based on a habit, distance, or time. In this manner, the habit may be a daily, weekly, monthly, or yearly habit. For example, the subject may go for a hike weekly or daily. In another manner, the patterned stress may be based on an interval of time or a fixed distance. For example, the interval of time may be the time it takes the subject to perform a number of squats or swim the length of a pool. In another example, the fixed distance may be the length of a pool or one mile. The physiologic metric may cycle with the patterned stress.

[0372] In some examples, the physiologic metric may include any combination of the following: heart rate, carbon dioxide levels, oxygen saturation, work, temperature, power, and / or heat flux. The physiologic metric can be a physiologic metric of the subject. In some examples, the physiologic metric may be related to the heath capacity of the subject. This relationship can allow for an improvement of the device to better monitor homeostasis based on the physiologic metrics. As such, the device can include at least one sensor for measuring at least one physiologic metric that corresponds to the patterned stress that the subject experiences. The sensor can then generate a data stream therefrom. The device can further include a processor configures to receive the data stream and determine a health capacity of the subject.

[0373] In some examples, patterned stress can be observed from a known stress or physical activity performed by a subject. For example, the patterned stress can be observed from the heart rate of a user while swimming, and in particular, the periodicity in the heart rate. The periodicity in the heart rate may be due to periodic buildup and then exhalation of CO2 during flip turns; this may be a result of physical exertion by the subject. That is, in a standard flip turn, there is a significant amount of time where there is exertion without breathing and the CO2 concentration in the blood rises, causing a corresponding increase in heart rate. Theamount of the increase and the time in which it may take the user to recover from the increase and return approximately to its heart rate prior to the increase can be used as an affirmative indicator of health (i.e., the less the rise in heart rate and the faster the recovery, the greater health capacity of the swimmer). In some embodiments, the disclosed system captures heart rate and then correlates peaks and valleys of heart rate to health capacity and / or health.

[0374] Specifically, for example, fluctuations or slight fluctuations can be seen in heart rate data from swimming, with periodic change in heart rate on the periodic scale of lap time. Every period of cycle where the swimmer does a flip turn, the moment of more extreme exertion pumps up CO2 in blood and increases heart rate. It is expected everyone has some fluctuations in heart rate data from swimming, but how sensitive one’s heart rate is to the periodic change has not been quantified or assessed. For example, the data taken from an average untrained swimmer may be about 60-70 bpm for every lap, while from a more trained swimmer the data may fluctuate from about 60 to 62 bpm. Thus, this extra sensitivity can be put into a wearable device, such as the device disclosed, and assessed to determine a user’s health capacity

[0375] In some manners, health can be considered one’s ability to adapt to stress. For example, people in different states of health or fitness or resilience, having various health capacities, may adapt to stress more or less successfully. In some embodiments, the rate of increase in amplitude upon the increase in CO2 can be a metric of homeostasis. If one can go through the stress of a flip turn with less impact on heart rate then one is healthier and / or has a greater health capacity than a person who goes through the stress of flip turn with a greater increase in heart rate. This insight can be used in training and general homeostasis monitoring but has yet to be integrated in an automated technology, at least because, for example, there has not been a biometric that connects stress test and fitness, and existing wearables focus only on one metric each which do not consider stress. Furthermore, this insight can allow for stress and the physiological metric to be measured and analyzed together.

[0376] The context constrains the type of activity that may be occurring, so one can compare the change in heart rate amongst a population of swimmers and can get a new normal as to what is fit and what is not. For example, in the case of swimming in a pool, it is known that the stress is coming in a unit dose or cycle in a flip turn because pool is about 25m-50mlong, so the stress doesn’t need to be measured. In the case of swimming in a lake, then the stress would need to be measured. Many athletic activities involve periodic training.

[0377] In some embodiments, the system, device, or method can prompt the user to identify what exercise or physical activity may be performing or will be performed. This information can allow the device to predict or otherwise infer the stress the user may undergo. The device can then connect the measured heart rate data to the activity that the device is guiding or monitoring the user through. As one gains resilience, one would see less change in heart rate. For example, a user may demonstrate resilience by recognizing the user used to struggle engaging or performing the cycle of the physical activity, but now no longer struggles or at least struggles less. This recognition can correlate to reducing the increase in heart rate for each cycle of the physical activity. In some embodiments, the device can determine the user’s tolerance for stress that can be optimized over period of time. This metric can then be used in training for achieving high efficiency goals.

[0378] In some embodiments, the device can be sensitive to a plurality of collected data during the user’s performance of a periodic physical activity that is different or not normal compared to a plurality of previously collected data during the user’s performance of the same periodic physical activity. For example, the device may be sensitive to a decrease or decline or other types of progress or baseline establishment to identify other types of homeostasis / dysregulation events. For example, if one expects improvement or knows the regular patterns, and if the data changes, especially if it changes in a manner showing lesser homeostasis, then the user may have an alternative or additional cause for the change in homeostasis. In some embodiments, the device provides systems and methods for monitoring athletic or cardio-physical conditioning progress which systems and methods have diminished incidences of false negatives. For example, sole reliance on known vital signs (e.g., heart rate) may lead to false negatives, as those vital signs may stay the same whether a user is gaining or losing fitness. In contrast, the systems and methods describe herein rely on novel metrics and thus diminish incidence of the false negatives.

[0379] In some embodiments, the device can identify interval training (e.g., identify stair running or lap swimming) by some key metrics. For example, the metrics may include heart rate recovery (not periodic), e.g., identifying how quickly heart rate increases or declines based on interval schedule. The key metrics may include periodic stress, known stress,wave form of characterization (for example, the device may perform waveform analysis and track changes over time), heart rate, SPO2, or other physiologic measurements that cycle with repeated periodic stresses.

[0380] In general, different physiologic responses may be used depending on the activity and what is an appropriate response variable to go with the stress or can adapt with the changes in stress, e.g., CO2, lack of oxygen in blood, work, heat, muscle contraction - how much power (wattage generation and periodicity associated with that), etc. In the case of interval training, heart rate and heat would be among the appropriate responsive metrics. In some embodiments, distance might be fixed, but the time interval may not be. In some embodiments, intervals can be nonspecific, can be training by heart rate zone rather than distance or time.

[0381] In some embodiments, the disclosed technology includes a system and method for establishing a baseline, a system and method for identifying changes in the baseline, and a system and a method for automating new metrics of health. The new metrics of health may include, for example, adapted exercise regimes in situations where a physical therapist or coach may be unable to otherwise design suitable exercise regimes for a user who has poor homeostasis or is otherwise in poor physical condition. Bilateral Symmetry Monitoring

[0382] Bilateral symmetry monitoring of bilateral symmetrical biological compartments provides an additional level of analysis to the overall human body, and more specifically to perfusion and the thermoregulation system of the body. For example, monitoring perfusion at a biological compartment on a right extremity and a biological compartment on a left extremity can provide an avenue for analysis that allows for a biological compartment and its symmetrical counterpart to be compared. This type of analysis may better highlight, identify, and / or reveal presymptomatic conditions that can lead to early detection or diagnosis of a condition and / or potential health risk. Additionally, this type of analysis can lead to implementing solutions or treatments that may provide immediate relief to a subject. This symmetrical analysis can help to provide a global homeostatic control of the subject. In other words, recognizing a lack of global homeostatic control of a subject can provide insight into the general homeostasis of the subject.

[0383] In some examples, one advantage to bilateral symmetry monitoring of bilateral symmetrical biological compartments may include early detection of localized inflammation or infection. For example, when a biological compartment or a localized area of the body is affected by inflammation or infection, the temperature and / or heat flux of that biological compartment or localized area of the body may be affected (i.e., the temperature may increase). The temperature and / or heat flux may be affected due to an increase or decrease in blood perfusion and / or metabolic activity in the affected area. By comparing the temperature or heat flux of a biological compartment to the temperature or heat flux of its symmetrical biological compartment, temperature asymmetries and / or heat flux asymmetries may be identified. This asymmetry can indicate a potential concern or problem that may lead to further investigation or treatment. For example, a higher temperature or heat flux measurement in a right wrist compared to the left wrist may indicate a blockage of blood flow. This early detection may enable timely interventions, such as administering treatment to relieve the blockage, elevating the right or left wrist, or preventing the condition by worsening and potentially causing other parts of the body to be affected.

[0384] In some examples, another advantage of bilateral symmetry monitoring of bilateral symmetrical biological compartments includes monitoring a biological response to a treatment. Bilateral symmetry monitoring in this manner can allow healthcare professionals to assess the effectiveness of the provided treatment. For example, a decrease in temperature or heat flux measurements may indicate a positive effect from the treatment, while an increase or sustained asymmetry may indicate a need for additional treatments or rehabilitation. By closely monitoring temperature or heat flux changes over time, healthcare professionals may be able to make more effective and informed decisions regarding the modification or continuation of proposed or treatment plans. This may optimize the subject’s recovery process and overall health state.

[0385] In some examples, another advantage of bilateral symmetry monitoring of bilateral symmetrical biological compartments includes improved identification of circulatory function. For example, asymmetrical temperature or health flux measurements between bilateral biological compartments can suggest circulatory problems. Reduced blood flow to one side of the body can result in cooler temperatures or heat flux measurements in those areas.By closely monitoring temperature and / or heat flux of bilateral biological compartments, immediate actions can be implemented to improve circulatory function.

[0386] In some examples, another advantage of bilateral symmetry monitoring of bilateral symmetrical biological compartments can include detection of early state injuries. For example, bilateral symmetry monitoring of temperature and / or heat flux measurements can help to identify minor injuries or overuse issues before they become more severe. This may prevent the injury from becoming more serious or requiring greater time intensive recovery treatments or procedures. Early detection of minor injuries or overuse issues may allow for timely intervention and prevention of further damage.

[0387] In some examples, another advantage of bilateral symmetry monitoring of bilateral symmetrical biological compartments can include monitoring post-surgical recovery. For example after surgical procedures, monitoring temperature and / or heat flux measurements of bilateral biological compartments can provide information and insight into the risk for or existence of systemic infections or complications that may have occurred as a result of the surgery.

[0388] In some examples, bilateral symmetry monitoring of bilateral symmetrical biological compartments may be beneficial in the context of a patient on dialysis. For example, bilateral symmetry monitoring of temperature and / or heat flux measurements of bilateral and symmetrical biological compartments may help healthcare providers identify potential complications or issues during the dialysis procedure and monitoring the patient’s overall health, health state, and / or well-being. In one example, bilateral symmetry monitoring can be used to monitor the access site of dialysis. By comparing the temperature and / or heat flux measurements of the biological extremity with the access site with the bilateral symmetric biological extremity of the patient, healthcare providers can assess the function of the access site. Asymmetry between the measured temperature and / or heat flux measurements of the biological compartments on the bilateral symmetric extremities may suggest a potential blockage or clotting at the access site. This may allow the healthcare provider to provide immediate attention to the patient to help to ensure uninterrupted blood flow during dialysis. Additionally, bilateral symmetry monitoring for dialysis patients can help to identify early signs of infection or inflammation around the access site. As discussed above, localized warmth or increased temperature on one biological compartment compared to its bilateralsymmetrical biological compartment may indicate and infection, which can then be more promptly addressed with appropriate treatment. Similarly, bilateral symmetry monitoring may also provide insight into a dialysis patient’s overall well-being during dialysis sessions by helping to identify signs of systemic issues which may not be immediately apparent through other means of assessment.

[0389] Some examples of bilateral biological compartments that can be monitored in various instances include, but are not limited to, fingers, toes, wrists, upper arms, lower arms, ankles, lower legs, upper legs, etc. In some examples, bilateral symmetrical fingers and / or toes may be preferred as the monitored biological compartment because they are richly provided with arterial-venous anastomoses. The arterial-venous anastomoses may largely be affected by changes in the amount and distribution of blood flow. Additionally, in general, the fingers and toes are largely free from muscular tissue, which can lead to better insight for blood flow occurring in the skin. Similarly, bilateral symmetry monitoring may also be applicable to biological organs that are separated but paired together by symmetry. For example, this may include kidneys and lungs.

[0390] While temperature and / or heat flux measurements of bilateral symmetrical biological compartments may be monitored and analyzed, the temperature and / or heat flux measurements of bilateral symmetrical biological compartments may also be monitored and analyzed as a function of time to create a health metric or score. This may normalize the measurements or minimize any noise and outliers in the measurements. Additionally, this may allow for a pattern of asymmetry to be detected over a period of time rather than limited to detecting or identifying a potential issue at a specific moment in time. Additionally, the health metric or score may allow for asymmetry to be detected when it may not have been able to be detected when analyzing the measurements. This health metric or score may be correlated with a health outcome which allows for asymmetry to be associated with a health outcome as well.General Example Embodiments

[0391] The technology disclosed in the present disclosure permits insights into the unique signature of one or more living systems, or a population of living systems, and thus into the health capacity of such a living system. The technology permits, for example, the measurement of the emergent property of heat elimination, measured either or both as anabsolute value or as a periodic value. The measurement of the emergent property of heat elimination or the like may be combined with the measurement of a physiologic metric that is correlated with a patterned stress. This can allow for a more precise monitoring of homeostasis. Absolute values of heat elimination may be indicative, or reflective, of energy expenditure. Absolute values of heat elimination may be indicative, or reflective, of metabolic rate insight. The periodicity of heat elimination may provide further insight into health capacity. The periodicity of heat elimination may be of a circadian periodicity (that is, on a cycle of from about 23 hour to a cycle of about 25 hour cycle), may be of a cycle shorter than circadian periodicity (for example, on a cycle of about 12, about 14, about 16, about 18, or about 20 hours), or may be longer than circadian (for example, every 2, 3, 4, 5, or 6 days, every week, or on a roughly monthly or 28-day, lunar, or annual cycle). Any such periodicity of heat elimination may serve as, or be reflective of, a unique signature a living system having a standard, acceptable health capacity. Deviations from that standard, acceptable periodicity of heat capacity may indicate, or otherwise reflect, sub-standard or unacceptable health capacity.

[0392] With respect to health capacity, “pre-symptomatic disease,” as used herein, refers to a process wherein health capacity is depleted by some metabolic task which is compensating of an environmental stress, the end result of which is disease and loss of function. This reduced health capacity is detectable as an anomalous energy expenditure associated with compensation or as some more general anomalous energy signature. Such a state of low or lowered health capacity places an individual at higher risk for loss of function generally, not merely from the initial stress or, more specifically, in a state of general susceptibility to disease or infection. Further, “symptomatic disease,” as used herein, refers to a state of impaired function or, more specifically, a disease state or a state in which an organism is, for example, infected by a viral pathogen. Current methodology, exemplified by the precision medicine paradigm, employs symptoms as indicators of disease. Aspects of the presently disclosed invention provide systems, devices, and methods for quantifying more fulsome metrics of health, exemplified by the measurement and assessment of “health capacity,” as either a static or dynamic measurement, to enable assessment of and increases in health capacity, and to identify pre-symptomatic changes in health capacity to permit early detection of disease or, more specifically, for example, infection.

[0393] Health, in one embodiment, is related to the ability of a biological system or organism to successfully adapt to a variety of challenges without significant loss of function. Physiologists, for example, may describe health as the sufficiency of a form of stored energy they call physiologic reserve — the ability of a human to positively respond to a stress. Physicists, as another example, may describe health as a capacity to incorporate, transform, and dissipate energy to persist. Cell biologists, as another example, may describe health as the baseline state of homeostasis — the ability of a cell or tissue to auto-regulate. Biochemists, as another example, may describe health as the control of anabolic and catabolic reactions in a metabolic network critical to biological function.

[0394] Each of these above-described perspectives or understandings of health is appropriate, in its context, be that the perspective of the physiologist, physicist, cell biologist, or biochemist, and contributes to the application of the disclosed technology. More specifically, the disclosed technology reconciles the above perspectives and understandings, and applies a novel conceptualization of health as high health capacity substantially simultaneous with absence of disease- functional and fit. In one embodiment, health capacity reveals how health may be quantified by measuring physical properties of homeostasis. That is, health is quantifiable as the health capacity of a biological system. Other aspects of the disclosed technology relate to developing techniques that accurately measure an array of metrics of the state of the biological system, including, for example, the health the biological system. These techniques obtain and process information on an actionable time scale, at sufficiently high resolution. The disclosed technology may obtain and process various metrics or information simultaneously or in combination with other metrics or information to obtain a more rapid and precise monitoring of homeostasis.

[0395] Other aspects of the disclosed technology include: detecting features of emergent properties of biological systems; automating new recognitions and detections of emergent properties of biological systems to improve life; action plans to acquire, maintain, and promote health; allowing for faster learning and interpreting of complex biology from new health accuracy data streams; allowing frictionless collection, analysis, and decision support of health in subjects; developing a scalable solution to disease interception; reducing false- positives in diagnostics; finding causes of diseases; preventing further development of disease; early on set diagnosis of diseases; and explaining the development of symptoms. Each aspectof the disclosed technology can be enhanced and / or amplified by incorporating bilateral symmetry monitoring, as discussed above. For example, with the use of symmetrical sensors, detecting features of emergent properties of biological systems may be more precise and / or accurate. In some embodiments of the disclosed technology, properties of living systems and their physical-chemical properties are measured; these properties capture the emergent properties of living systems. In other embodiments, the disclosed technology is focused on emergent properties that gave rise to the boundary between physical and biological systems. The disclosed technology is thus applicable to synthetic biology and artificial biological systems, for example, protocells or industrial biology systems.

[0396] In another aspect, the disclosed technology relates to methods for detecting recognizable features or patterns from an array of health metrics from a subject. In some embodiments, the methods for detecting recognizable features or patterns from an array of heath metrics from a subject may be enhanced and amplified by detecting recognizable features or patterns from an array of health metrics from a subject that includes health metric data generated from a pair of symmetric sensors that are substantially simultaneously monitored. In some embodiments, the methods are integrated into an internet of things (loT) architecture and infrastructure. In some embodiments, the methods detect features or patterns of metabolism at the scale of cells. These measurements of which the features may arise from may be intermitted, regular, or continuous. In some embodiments, the methods further comprise streaming arrays of such metrics into the Cloud, applying machine learning to “quantifying metabolism” and detecting changes before symptoms of diseases occur. In some embodiments, the methods non-invasively measure discrete physical parameters of biological systems at resolution and frequency that in real-time measure cellular physiology and homeostasis. In some embodiments, the methods define and quantify a quantity known as the health capacity of a subject, relating to the resilience of the subject’s homeostasis, based on measured physical properties, energy and information of the subject. The health capacity of a subject may be more precise and accurate by incorporating the bilateral symmetry monitoring.

[0397] The disclosed technology provides actionable information permitting the improvement of health capacity in a biological system. In preferred embodiment, individuals gain access to insight, not previously accessible, of their own health. Such actionability permits the observations of responses to particular stimuli in a specific biological system and permitsalternation or influence of future stimuli. For example, an individual or health care professions may use the disclosed technology to alter the health of a biological system, for example, a patient or the individual himself or herself. It also permits a better understanding of the most influential determinants of health, for example, sleep patterns and duration, nutrition (including diet, supplements, medications, etc ), exercise (neuromuscular inputs, type, duration, periodicity, etc.) and other lifestyle decisions or detractors (e.g., pollution) that impact health. In a preferred embodiment, the disclosed technology permits an individual to be an efficient agent of his own or her own health. It also permits a better ability to plan or prepare for an action to be taken based on the status of the individual’s health.

[0398] The disclosed technology provides an ability to compare detected recognizable features of an individual to detected recognizable features of a greater population of a similar to different recognizable feature.

[0399] The disclosed technology provides automated and automatable methods, thus addressing a major sources of healthcare inefficiency, manual testing and interpretation. Current healthcare testing and interpreting is generally centralized, and the interpretation of such testing is generally performed manually by healthcare professions. Both of these tasks are expensive and inefficient. The disclosed technology permits the digitalization of health data and interpretation associated with health capacity. Such digitization affords the ability to apply all the benefits of the existing loT ecosystem and Cloud computing.

[0400] The disclosed technology provides scientists and healthcare professions with systems and methods to optimize health by improving health capacity. Rather than viewing health as an elusive concept, the current technology permits it to be viewed as a system with quantifiable, particularized or modularized measurable tasks or targets. Additionally, the current technology may be enhanced and amplified by incorporation bilateral symmetry monitoring as discussed above.

[0401] The disclosed technology permits the quantification of health viewed as health capacity, and the detection of its change over time, including changes in magnitude or frequency of circadian or other periodic components. Rather than the current paradigm, viewing the absence of health as the presence of symptomatic disease, the disclosed technology provides for the substantially continuous measurement of health, quantification of its decline, and detection its change prior to the onset of symptomatic disease. Thus, the disclosedtechnology permits individuals, public health and / or medical professionals to take corrective action with more accurate, reliable information to forestall the onset of disease, decrease morbidity, decrease mortality, and lower the cost of care.

[0402] The disclosed technology can also increase the efficiency of currently available precision medicine tools by reducing the false or improper diagnosis rate of discovery, by employing simple, fundamental and translatable metrics of energy usage- an “energy signature,” and thereby reverse the trend of inefficiency according to Eroom’s Law.

[0403] In some aspects, the disclosed technology relates to methods of detecting recognizable features from the obtainment of physical, chemical, and biological measurements of emergent properties of both biological and non-biological systems; and a system or technology platform that processes, stores, transfers, analyzes, compiles, distributes, and displays emergent property measurement data to quantify, predict, control, maximize, design, and engineer complex adaptive biological and non-biological systems. In some aspects, the disclosed technology further relates to methods of detecting recognizable features from the obtainment of physical, chemical, and biological measurements of emergent properties of both biological and non-biological systems that are taken from symmetrically placed sensors that may be substantially simultaneously monitored.

[0404] In some embodiments, a biological system could be a eukaryotic, prokaryotic, or archaea organism such as a bacteria, gamete or red blood cell, white blood cell, a cell derived from a tissue or organ such as a myocyte, or a simple or complex multicellular organism such as an apple or human, or an engineered cell, and / or a collection thereof to form an interactive ecosystem in conjunction with non-biological essential chemical materials or energy sources.

[0405] In some embodiments, a non-biological system could be an engineered nonliving system analogous to a biological system but devoid of genetic material- a protocell- or any other engineered or biomimicry -based designed system or semi -synthetic system.

[0406] In some embodiments, the disclosed technology further relates to the use of such measures to gauge their state of existence and their ability to persist in biological and non- biological systems. The ability of a biological or non-biological system to exist or persist relates to the system’s “health capacity.”

[0407] Advantages of the disclosed technology include its ability to quantify, predict, control, maximize, engineer, and design a biological or non-biological system based on measurement of physical, chemical, and biological parameters that relate to the property of resilience (adaptivity) as indicated primarily by its ability to persist or achieve some core function capability. These advantages may be enhanced and amplified by incorporating bilateral symmetric monitoring of the biological or non-biological system.

[0408] In some embodiments, the disclosed technology further relates to a technology platform and methods for obtaining physical, chemical, and biological parameters include wearable, implanted, embedded, or otherwise-coupled devices. Once such measurements are implemented, they can be stored, quantified, analyzed, compiled, distributed, and displayed to quantify, predict, control, maximize, engineer, and design a biological system. Measures of health capacity can also be analyzed in conjunction with other measures that are not those of emergent properties to improve upon the ability of the measurement technology and learning platform to quantify, predict, control, maximize, engineer, and design a biological or non-biological system. The measures of health capacity can be enhanced, amplified, more precise and accurate by incorporating bilateral symmetry monitoring. By substantially simultaneously implementing measurements collected from a pair of sensors that are symmetrically placed and substantially simultaneously monitored, the measures of health capacity can be enhanced, amplified, more precise and accurate.

[0409] In the example of single-celled eukaryotic or prokaryotic or archaea organisms, the disclosed technology would allow for the quantification, prediction, and maximization of function. In the case, for example, of a gamete, the disclosed technology would allow for the quantification of health capacity to maximize fertility for in vivo or in vitro fertilization. In bacteria, yeast or human cells, the disclosed technology could be used for the quantification of health capacity of, in the case of industrial or synthetic biology, the synthesis of molecules. In the case of non-pathogenic enteric bacteria, the disclosed technology could be used to maximize microbiome function.

[0410] In the example of multi-celled eukaryotic organisms, the disclosed technology would allow for the quantification, prediction, and maximization of function. For example, in humans the disclosed technology would enable the measurement and maximization of health and the diagnosis and / or pre-symptomatic diagnosis of disease, and thedesigns of methods for disease treatment or interception, where disease could be infectious, cancer, toxic, iatrogenic, or metabolic, and where health determinants are food / nutrition, sleep, exercise, neuromuscular activation, and changes to lifestyle such as smoking, inactivity, addiction. This may be enhanced and amplified by incorporating bilateral symmetry monitoring as discussed above.

[0411] In certain embodiments, the advantages of the disclosed technology can further include its applications in non-human multi-celled eukaryotic organisms. For example, in agricultural systems, such as farming of plants and animals. In these biological systems the disclosed technology would enable the maximization of food substance production and / or mitigation of abiotic or biotic stress.

[0412] In certain embodiments, the advantages of the disclosed technology can further include its application in simple ecosystems of species and chemical resources. For example, in biological systems of two or more species and one or more resources, the disclosed technology would enable their mutual maximization of functional interdependence. Where one of the species was a human, for example, the technology would enable the maximization of human functional parameters such as sleep, activity, physical or cognitive performance and disease prevention.

[0413] In certain embodiments, the advantages of the disclosed technology can further include its applications in complex ecosystems. For example, in biological systems of many species and many resources, the disclosed technology would enable their mutual maximization of functional interdependence. Where the complex system was a farm the disclosed technology would enable maximization of an ecosystem’s function such as sustainability.

[0414] In certain embodiments, the advantages of the disclosed technology can further include its applications in synthetic biology, for example in eukaryotic and prokaryotic cells. In these biological systems, the disclosed technology would enable design and engineering to maximize function; for example, the synthesis of a protein, lipid, or small molecule or the capability to maintain viability under condition of abiotic or biotic stress.

[0415] In certain embodiments, the advantages of the disclosed technology can further include its applications in biometrics. For example, the disclosed technology wouldenable the identification of a biological system independent of or in conjunction with genetic material.

[0416] In certain embodiments, the advantages of the disclosed technology can further include its applications in non-biological systems. For example, the disclosed technology would enable the design and engineering of a non-living system analogous to a biological system but devoid of genetic material- a “protocell.” Such protocell could be used for learning and / or doing work, where work may be understood to mean any output that is not merely heat production.

[0417] In certain embodiments, the advantages of the disclosed technology can further include its application in biological systems to quantify biological time- “health span.” For example, the disclosed technology could be used to calculate the theoretical and actual life span(s) of a biological system and be used in conjunction with the aforementioned uses to maximize function or health-span.

[0418] In general, thermoregulation consists of two principal components: heat production and heat elimination. Healthy homeostasis requires a balance between the two. There is a dynamic balance in which the two components remain roughly equal through a large range of energy demands and rate of change. By measuring the two components continuously and analyzing their temporal alignment, the system can assess health in a much more detailed manner than if only one or the other were measured. Additionally, by incorporating bilateral symmetry monitoring, the assessment of health by the system may be enhanced and amplified.

[0419] In another aspect, the disclosed technology relates to a method of detecting a health risk, the method comprising: obtaining a data pattern for a subject indicative of a first health state of the subject; transmitting, based on the data pattern, subject-related information (e g., a recognizable feature associated with a biological response of the subject) to a health services provider; receiving an indicia of a health outcome for the subject; updating a model, designed to identify health risks based on data patterns, by training the model based at least in part on the received indicia of a health outcome, wherein the training comprises correlating the data pattern with the health outcome; and using the updated model to detect a health risk associated with the health outcome.

[0420] In some embodiments, the data pattern is a heat flux pattern based on a plurality of heat flux measurements of the subject. In certain embodiments, the heat flux patternis based, at least in part, on a categorical analysis of heat flux measurements of the subject. In certain embodiments, the heat flux pattern is based, at least in part, on a time series analysis of a plurality of heat flux measurements of the subject. In certain embodiments, health outcome is selected from the group consisting of (i) a health state of the subject, (ii) a health service applicable to the subject, and (iii) a second heat flux pattern of the subject. In certain embodiments, the indicia of a health outcome is obtained from the heath service provider. In certain embodiments, the indicia of a health outcome is a CPT (Current Procedural Terminology) code. In certain embodiments, the CPT code is associated with the diagnosis of a second health state. In certain embodiments, the CPT code is associated with the treatment of a second health state.

[0421] In some embodiments, the first health state and the second health state are the same. In certain embodiments, the first health state and the second health state are different. In certain embodiments, the indicia of a health outcome is a second heat flux pattern for the subject that is indicative of a second health state of the subject. In certain embodiments, the first health state and the second health state are the same. In certain embodiments, the first health state and the second health state are different. In certain embodiments, the health service provider is selected from the group comprising (i) a call center, (ii) a medical services provider, (iii) a pharmaceuticals provider, (iv) a nutrition products merchant, (v) a consumables merchant, and (vi) an exercise services provider. In certain embodiments, the health service is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical therapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

[0422] In some embodiments, the method further comprising a step of generating a recommendation to engage in an intervention regarding the health risk. In certain embodiments, wherein the indicia of a health outcome is based on subject-related information. In certain embodiments, the method further comprising generating a recommendation to engage in an intervention or an alert regarding a health risk for the subject, based on the trained model. In certain embodiments, the health service provider is selected from the group comprising a nutrition products merchant, a consumables merchant, and an exercise services provider.

[0423] The model may be based on, for example, one or more of the following: zone based correlations, pattern-based correlations, Deep Learning, Linear Regression, Ridge Regression, Lasso Regression, Elastic Net Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting Machines (GBM), XGBoost, LightGBM, CatBoost, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Non-Negative Matrix Factorization (NMF), Gaussian Mixture Models (GMM), Hidden Markov Models (HMM), Neural Networks (Feedforward, Convolutional, Recurrent), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Autoencoders, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), Deep Q-Networks (DQN), Actor-Critic Models, Temporal Difference Learning, Gaussian Process Models, Kernel Methods, Multilayer Perceptrons (MLP), Word Embeddings (Word2Vec, GloVe), Transformer Models (BERT, GPT, T5), Sequence-to-Sequence Models, Hierarchical Models, Dynamic Time Warping (DTW), Hierarchical Clustering, Mean Shift Clustering, DBSCAN (Density -Based Spatial Clustering of Applications with Noise), Agglomerative Clustering, Self-Organizing Maps (SOM), Isolation Forest, One-Class SVM, Anomaly Detection Models, Time Series Models (ARIMA, SARIMA, Exponential Smoothing), Hidden Markov Models (HMM) for Time Series, Gaussian Process Regression, Bayesian Networks, Association Rule Learning (Apriori, FP- Growth), Markov Chains, or Reinforcement Learning Models (Q-Learning, SARSA, Policy Gradient). A number of machine learning techniques may be used in the learning step, including artificial neural nets, decision trees, memory-based methods, boosting, attribute selection through cross-validation, and stochastic search methods such as simulated annealing and evolutionary computation. Further examples of the types of non-linear functions and / or machine learning method that may be used in the disclosed technology include the following: conditionals, case statements, logical processing, probabilistic or logical inference, neural network processing, kernel based methods, memory-based lookup (kNN, SOMs), decision lists, decision-tree prediction, support vector machine prediction, clustering, boosted methods, cascade-correlation, Boltzmann classifier, regression trees, case-based reasoning, Gaussians, Bayes nets, dynamic Bayesian networks, HMMs, Kalman filters, Gaussian processes, algorithmic predictors (e.g. learned by evolutionary computation or other program synthesis tools).

[0424] In another aspect, the disclosed technology relates to a method of improving the ability to detect a health risk, the method comprising: obtaining a heat flux pattern for a subject indicative of a first health state of the subject; transmitting, based on the heat flux pattern, subject-related information (e.g., a recognizable feature associated with a biological response of the subject) to a health services provider; receiving an indicia of a health outcome for the subject; improving a model, designed to identify health risks based on data patterns, by training the model based at least in part on the received indicia of a health outcome, wherein the training comprises correlating the heat flux pattern with the health outcome; and using the improved model to detect a health risk associated with the health outcome.

[0425] In some embodiments, the health services provider is a call center. In certain embodiments, the health outcome relates to operational data applicable to the call center. In certain embodiments, the operational data relates to the duration of a phone call between the subject and the call center. In certain embodiments, the operational data relate to the success rate of attempts by the call center to engage in a call with the subject. In certain embodiments, the health services provider is a medical services provider. In certain embodiments, the health outcome relates to operational data applicable to the health service provider. In certain embodiments, the operational data relates to the length of the subj ecf s visit to the heath service provider. In certain embodiments, the health risk for the subject is a probabilistic measure. In certain embodiments, the method further comprising a step of recommending products or services to the subject. In certain embodiments, the method further comprising a step of receiving a percentage of revenue generated by the health service provider attributable to the recommended products or services purchased made by the subject. In certain embodiments, the method further comprising a step of generating a recommendation to engage in an intervention regarding the health risk. In certain embodiments, wherein the indicia of a health outcome is based on subject-related information.

[0426] The model may be based on, for example, one or more of the following: zone based correlations, pattern-based correlations, Deep Learning, Linear Regression, Ridge Regression, Lasso Regression, Elastic Net Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting Machines (GBM), XGBoost, LightGBM, CatBoost, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Non-Negative Matrix Factorization(NMF), Gaussian Mixture Models (GMM), Hidden Markov Models (HMM), Neural Networks (Feedforward, Convolutional, Recurrent), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Autoencoders, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), Deep Q-Networks (DQN), Actor-Critic Models, Temporal Difference Learning, Gaussian Process Models, Kernel Methods, Multilayer Perceptrons (MLP), Word Embeddings (Word2Vec, GloVe), Transformer Models (BERT, GPT, T5), Sequence-to-Sequence Models, Hierarchical Models, Dynamic Time Warping (DTW), Hierarchical Clustering, Mean Shift Clustering, DBSCAN (Density -Based Spatial Clustering of Applications with Noise), Agglomerative Clustering, Self-Organizing Maps (SOM), Isolation Forest, One-Class SVM, Anomaly Detection Models, Time Series Models (ARIMA, SARIMA, Exponential Smoothing), Hidden Markov Models (HMM) for Time Series, Gaussian Process Regression, Bayesian Networks, Association Rule Learning (Apriori, FP- Growth), Markov Chains, or Reinforcement Learning Models (Q-Learning, SARSA, Policy Gradient). A number of machine learning techniques may be used in the learning step, including artificial neural nets, decision trees, memory-based methods, boosting, attribute selection through cross-validation, and stochastic search methods such as simulated annealing and evolutionary computation. Further examples of the types of non-linear functions and / or machine learning method that may be used in the disclosed technology include the following: conditionals, case statements, logical processing, probabilistic or logical inference, neural network processing, kernel based methods, memory-based lookup (kNN, SOMs), decision lists, decision-tree prediction, support vector machine prediction, clustering, boosted methods, cascade-correlation, Boltzmann classifier, regression trees, case-based reasoning, Gaussians, Bayes nets, dynamic Bayesian networks, HMMs, Kalman filters, Gaussian processes, algorithmic predictors (e.g. learned by evolutionary computation or other program synthesis tools).

[0427] In another aspect, the disclosed technology relates to a method of lowering a health risk, the comprising: obtaining a heat flux data pattern for a subject indicative of a first health state of the subject; transmitting, based on the heat flux data pattern, subject-related information (e.g., a recognizable feature associated with a biological response of the subject) to a health services provider; receiving an indicia of a health risk for the subject; improving a model, designed to identify health risks based on heat flux patterns, by training the model basedat least in part on the received indicia of a health risk, wherein the training comprises correlating the heat flux data pattern with the health risk; and using the improved model to detect of the health risk; recommending, for the subject, an intervention to lower the health risk.

[0428] In certain embodiments, the intervention is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical therapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions. In certain embodiments, the recommendation is implemented, thereby lowering the health risk. In certain embodiments, the indicia of a health risk is based on subject-related information.

[0429] The model may be based on, for example, one or more of the following: zone based correlations, pattern-based correlations, Deep Learning, Linear Regression, Ridge Regression, Lasso Regression, Elastic Net Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting Machines (GBM), XGBoost, LightGBM, CatBoost, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Non-Negative Matrix Factorization (NMF), Gaussian Mixture Models (GMM), Hidden Markov Models (HMM), Neural Networks (Feedforward, Convolutional, Recurrent), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Autoencoders, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), Deep Q-Networks (DQN), Actor-Critic Models, Temporal Difference Learning, Gaussian Process Models, Kernel Methods, Multilayer Perceptrons (MLP), Word Embeddings (Word2Vec, GloVe), Transformer Models (BERT, GPT, T5), Sequence-to-Sequence Models, Hierarchical Models, Dynamic Time Warping (DTW), Hierarchical Clustering, Mean Shift Clustering, DBSCAN (Density -Based Spatial Clustering of Applications with Noise), Agglomerative Clustering, Self-Organizing Maps (SOM), Isolation Forest, One-Class SVM, Anomaly Detection Models, Time Series Models (ARIMA, SARIMA, Exponential Smoothing), Hidden Markov Models (HMM) for Time Series, Gaussian Process Regression, Bayesian Networks, Association Rule Learning (Apriori, FP- Growth), Markov Chains, or Reinforcement Learning Models (Q-Learning, SARSA, Policy Gradient). A number of machine learning techniques may be used in the learning step, including artificial neural nets, decision trees, memory-based methods, boosting, attributeselection through cross-validation, and stochastic search methods such as simulated annealing and evolutionary computation. Further examples of the types of non-linear functions and / or machine learning method that may be used in the disclosed technology include the following: conditionals, case statements, logical processing, probabilistic or logical inference, neural network processing, kernel based methods, memory-based lookup (kNN, SOMs), decision lists, decision-tree prediction, support vector machine prediction, clustering, boosted methods, cascade-correlation, Boltzmann classifier, regression trees, case-based reasoning, Gaussians, Bayes nets, dynamic Bayesian networks, HMMs, Kalman filters, Gaussian processes, algorithmic predictors (e.g. learned by evolutionary computation or other program synthesis tools).

[0430] For example, a service of automate triage may include the following steps:1. Patient band is placed at discharge on office / emergency room (ER) 2. The band is paired to gateway (e.g., phone) 3. The band measures patient data (e.g., heat flux) continuously 4. Cloud compilation / analysis of the measured data 5. Anomaly detected from the analysis of measured signals 6. Alert generated 7. Doctor / Nurse receives the alert and uses that to aid in decision support 8. Action taken by Doctor / Nurse.

[0431] A learning feedback loop may be implemented to automate diagnosis, which may include the following steps: 1. Health care provider shares diagnosis (CPT) codes2. Annotate measured patient data (e.g., heat flux) 3. Matching CPT code with signature of measured patient data (e.g., heat flux signature) 4. Learning based on the matched CPT code and signature to automate diagnosis 5. File de novo with FDA.

[0432] Another learning feedback loop may be implemented to improve existing outcomes, which may include the following steps: 1. Provider shares treatment; 2. Obtaining known diagnosis; 3. The disclosed technology pairs timing and type of treatment with outcome, analyze data, and have RWE (real world evidence) on compliance, and outcomes (phase 4).

[0433] Yet another learning feedback loop may be implemented to discover new outcomes, which may include the following steps: 1. Patient becomes a consumer; 2. Patient is aware of his energy stream and health / disease data; 3. Patient opts in a food provider service; 4. What patient eats is input to the machine learning algorithm; 5. Analyze Anomaly / Diagnosis / Treatment / Outcome / and Food; 6. Consumer now is aware of the effectsof particular food on health outcomes; 7. Emerja take 5% of the food provider sales to consumer based on personalized food supply; 8. (wash / rinse / spit / repeat for beverage and clothing and sleep and sports apparel industry).

[0434] By implementing the above learning loops, the disclosed technology can improve health (e.g., by detecting health risk, improving the ability to detect a health risk, and / or lowering a health risk), improve heat flux pattern categorization, and / or improve heat flux pattern analysis based on or more of (1) time series analyses, (2) zone based correlations, 93) pattern-based correlations, (4) linear regression based correlations, and (5) deep learning based correlation. For telemedicine call centers, the disclosed technology can improve operational aspects such as call feedback, call satisfaction, call frequency and / or call length.

[0435] In some embodiments, the disclosed technology may be implemented in a Software-as-a-Service business model. The business advantages that can be derived from certain embodiments of the disclosed method may include, but are not limited to: (i) the ability to track any recommended nutrients, supplements, or other consumables for the subjects, (ii) the ability to track whether the subject purchases the recommended nutrients, supplements, or other consumables, and (iii) the ability to correlate, temporally or by volume, the purchase of the nutrients, supplements, or other consumables with improved outcomes.

[0436] The direct financial advantages that can be derived from certain embodiments of the disclosed method include, but are not limited to: (i) tracking income, revenue, and or profit derived from sales of nutrients, supplements, or other consumables recommended for the subjects, (ii) establishing a percentage of income, revenue, and or profit to be attributed to the recommendation to the subject to purchases the recommended nutrients, supplements, or other consumables, wherein that percentage may be a function of the correlation, temporally or by volume, of the recommendation and the purchase, and (iii) the ability to distribute a percentage of income, revenue, and or profit, to provide to the recommendation-generating system, attributed to the recommendation to the subject to purchases the recommended nutrients, supplements, or other consumables.EXAMPLES

[0437] The below examples may be used independently or in combination with the above described collection of measurements and analysis of physiologic metrics that correlateto a patterned stress. The combination may improve the operation of the device to better monitor changes in homeostasis in methods or modes of operation that were not previously possible.

[0438] In certain embodiments, heat flux can be conceptually plotted as a function of ambient temperature. It will be understood by one of ordinary skill in the art that a visual multi-dimensional plot need not be physically generated, but rather the calculations and analyses underlying such a plot can be performed directly. In this manner, there may be insight into how an internal signal and an external signal reflect on the health state of a subject. In certain embodiments, the internal signal and the external signal can be analyzed substantially simultaneously, which may lead to a more in depth reflection of the overall health state of the subject. This can highlight heat as an emergent factor. In certain embodiments systems, methods, and devices may collect and / or generate a data stream that may include a plurality of heat flux data and a plurality of ambient temperature data. In certain embodiments, the heat flux data may include a plurality of heat elimination measurements expressed as a function of environmental temperature. In certain embodiments, systems, methods or devices may include a processor that may receive the data stream. In certain embodiments, systems, methods, and devices may detect features or shapelets that may be related to and / or correlate with a health state or status of an individual. In certain embodiments, the feature detected may represent a state of homeostasis. A state of homeostasis may be represented by a densely populated region within the data stream. This can be an example of a motif detected by the system. In some embodiments, the state of homeostasis can be detected by the system before or after a novelet detected by the system. In some embodiments, a more densely populated region within the data stream may represent that the health state of the subject may be maintained at that point in time. In certain embodiments, a less dense, more diffuse an area of the data stream is the system may determine the less stable or more unstable a thermal system of a subject may be. This may indicate a novelet may be forming, of which the system may detect. In certain embodiments, the system may detect an overall feature or snippet of the plurality of data collected by detecting the overall structure of the data. In certain embodiments, the system may detect sub features or shapelets within the overall pattern of the plurality of data. In certain embodiments, the processor may receive data streams from a pair of sensors that are symmetrically placed.The data streams may be substantially simultaneously monitored and analyzed to provide an enhanced and amplified insight into the state of homeostasis of the subject.

[0439] In certain embodiments, an overall pattern that the system may detect may be a boomerang shape, L-shape, or the like when conceptually plotted as heat flux vs. environmental temperature. In certain embodiments, the system may identify a peak or turning point of the overall shape detected. The system may detect a sub pattern within the overall shape detected. For example, the system may detect the degree or pronunciation of the turning or transition point detected within the overall shape detected. In certain embodiments, the detected degree or pronunciation of the turning point may be indicative of age, health system efficiency, homeostasis, ability to obtain homeostasis in varying environments, etc. For example, the system may detect a more defined turning point, which may be reflective of a younger subject or one with greater cardiorespiratory fitness. In certain embodiments, the system may detect sub features that may be located at the ends of the overall shape detected and / or within the overall shape detected. The sub features may include variability within the data which may correlate to a recognizable feature. For example, the system may detect a sub feature that may be indicative of a subject’s ability to tolerate cold temperatures, a subject’s cardiac function, a subject’s prognosis, etc. In some embodiments, the system may compare and analyze patterns that are substantially simultaneously generated from data streams collected from a pair of sensors that are symmetrically placed on bilateral symmetrical biological compartments. The sub features substantially simultaneously detected may be compared and analyzed.

[0440] In certain embodiments, the system may detect a feature associated with a health state of a subject. In certain embodiments, the system may compare and analyze the simultaneous detected features from the data streams generated from the symmetrically placed pair of sensors. In certain embodiments, the health state may be associated with a stage of a health state or response to an infection, disease, etc. This can be based on the amount of heat the subject is conserving or eliminating at a given environmental or skin temperature. In certain embodiments, the system may detect a recognizable feature that is based on the amount of heat eliminated from the subject at a given temperature. In some embodiments, the system may detect a recognizable feature for each disease, infection, health related illness, etc. Within each detectable recognizable feature, the system may determine the subject’s response to thedisease, infection, or health related illness. Additionally, the system may predict, based on the detectable recognizable feature, the subject’s future response to the disease, infection, or health related illness. This may be enhanced and amplified by the use of bilateral symmetry monitoring discussed above. For example, the system may determine whether the subject is healing from the illness, fighting the illness, or responding to the illness. In certain embodiments, the system may detect when the subject transitions between each stage of responding to the illness. In some embodiments, the detection of the transition states can allow a subject to gain an understanding as to the timeline of the disease, illness, or condition and then prepare for accordingly. In certain embodiments, this understanding may be provided to the subject in a minimal invasive manner without compromising the strength of the data stream signal.

[0441] In certain embodiments, a recognizable feature may be the maintenance of homeostasis. This may be where the thermoregulatory system of the subject spends the most time. While the system may detect movement, the sub feature detected by the system may be indicative of the maintenance of homeostasis. This can include the delta or change in position at any time, independent of direction, indicating that there is a change in heat flux.

[0442] In certain embodiments, the more data collected, the more indicative the detected recognizable feature may be representative of a condition, illness, disease, etc. This may allow the system to forecast future features it may detect based on the previously recognized features within the data stream. This may also allow the system to classify the data stream to a specific class of behavior based on the detected recognized features. Additionally, the forecast capability of the system may be enhanced by incorporating the bilateral symmetry monitoring as discussed above. For example, by comparing the substantially simultaneously detected recognizable features, the system may be able to forecast future features more precisely and accurately.

[0443] In certain embodiments, the recognizable features may be generalizable to a greater population. For example, the overall pattern of the data may be generalizable to a population of subjects with the same or similar disease or illness. Additionally, for example, the sub pattern of the data may be generalizable to a greater population for how a subject’s thermoregulatory system may fluctuate over time. With generalizations, the system may be able to determine anomalies or outliers to provide the subject with additional information. Theanomalies may be found amongst the subject’s own features detected by the system or the features of a greater population.

[0444] In certain embodiments, a user may use the detected recognizable features and / or the substantially simultaneously detected recognizable features by the system in various manners. For example, a user may use the detected recognizable feature for early detection of a disease. This may include determining a nominal heat flux of a patient, measuring a current heat flux of the patient, and comparing the current heat flux of the patient to the nominal heat flux of the patient to detect disease. In another example, a user may use the detected recognizable features for recommending a thermal regulation guideline. This may include determining a current phase of a menstrual cycle of the user, measuring a current heat flux of the user, and comparing the current phase of the menstrual cycle with the current heat flux to determine a thermal regulation guideline. In another example, a user may use the detected recognizable features for determining readiness of a warfighter. This may include determining a current state of a potential warfighter, measuring a current heat flux of the potential warfighter, and comparing the current heat flux of the potential warfighter with the current state of the potential warfighter to determine the readiness of a warfighter. The early detection of disease by the system may be enhanced and amplified by incorporating bilateral symmetry monitoring discussed above.

[0445] In another example, a user may use the detected recognizable features for regulating sleep. This may include determining a current circadian rhythm of a user, measuring a current heat flux of the user, and comparing the current heat flux of the user with the current circadian rhythm of the user to regulate sleep of the user. In another example, a user may use the detected recognizable features for determining readiness of a user to conceive. This may include determining a current phase of a menstrual cycle of the user, measuring a current heat flux of the user, and comparing the current phase of the menstrual cycle of the user with the current heat flux to determine the user’s readiness to conceive. In another example, a user may use the detected recognizable features for determining a stage of pregnancy of a user. This may include determining a current state of the user, measuring a current heat flux of the user, and comparing the current state of the user with the current heat flux of the user to determine the state of pregnancy of the user.

[0446] In another example, a user may use the detected recognizable features for determining gut health of a user. This may include determining a current phase of digestion of at least one food of the user, measuring a current heat flux of the user, and comparing the current phase of digestion with the current heat flux to determine gut health of the user. In another example, a user may use the detected recognizable features for determining athletic capabilities of a user. This may include determining a current state of health capacity of a user, measuring a current heat flux of the user, and comparing the current state of health capacity of the user with the current heat flux of the user to determine athletic capabilities of the user. In another example, a user may use the detected recognizable features for determining metabolic rate of a user. This may include determining a current amount of muscle mass of a user, measuring a current heat flux of the user, and comparing the current amount of muscle mass of a user with the current heat flux of the user to determine the determining a metabolic rate of a user.

[0447] In another example, a user may use the detected recognizable features for determining a syncope event of a user. This may include determining current physical state of the user, measuring a current heat flux of the user, and comparing the current physical state of the user and the current heat flux of the user to determine an upcoming syncope event. In another example, a user may use the detected recognizable features for determining health capacity of a user. This may include determining a current quasiperiodic rhythm of the user, measuring a current heat flux of the user, and comparing the current quasiperiodic rhythm of the user with the current heat flux of the user to determine health capacity of the user. In another example, a user may use the detected recognizable features for determining heart function efficiency of a user. This may include determining a current heart capacity of the user, measuring a current heat flux of the user, and comparing the current heart capacity with the current heart flux to determine heart function efficiency of the user.

[0448] In another example, a user may use the detected recognizable features for determining immune system efficiency. This may include determining a current state of immune system of a user, measuring a current heat flux of the user, and comparing the current state of the immune system of the user with the current heat flux of the user to determine the efficiency of the immune system of a user. In another example, a user may use the detected recognizable features for determining a user’s weight loss capabilities. This may includedetermining a current weight of the user, measuring a current heat flux of the user, and comparing the current weight of the user with the current heat flux of the user to determine the user’s weight loss capabilities. In another example, a user may use the detected recognizable features for determining medication effectiveness to treat a disease. This may include determining a current stage of a disease, measuring a current heat flux of the user, and comparing the current stage of the disease with the current heat flux of the user to determine the effectiveness of a medication to treat the disease.

[0449] In another example, a user may use the detected recognizable features for determining a risk of loss of pregnancy. This may include determining a current stage of pregnancy, measuring a current heat flux of the user, and comparing the current stage of pregnancy with the current heat flux of the user to determine the risk of loss of pregnancy. In another example, a user may use the detected recognizable features for determining a level of sobriety of a user. This may include determining a current amount of alcohol consume by the user, measuring a current heat flux of the user, and comparing the current amount of alcohol consumed by the user with the current heat flux of the user to determine the level of sobriety of the user. In another example, a user may use the detected recognizable features for determining a recovery regimen for a user. This may include determining a current stage of an injury, measuring a current heat flux of the user, and comparing the current stage of an injury with the current heat flux of the user to determine the recovery regiment for the user. In another example, a user may use the detected recognizable features for recommending a thermal regulation guideline to a user. This may include determining a current phase of menopause of the user, measuring a current heat flux of the user, and comparing the current phase of menopause with the current heat flux to determine a thermal regulation guideline.

[0450] In certain embodiments, subjects are classified in one of at least two groups: for example, as having a positively functioning thermoregulatory system or as having a negatively functioning thermoregulatory system. In some embodiments, subjects may lie in between the two groups as well. In other words, the functional level (functionality) of a subject’s thermoregulatory system or the efficiency at which a subject’s thermoregulatory system functions at may exist on a spectrum. In some embodiments, the functionality of a subject’s thermoregulatory system may correlate with the resilience of the subject’s thermoregulatory system. In some embodiments, the functionality of a subject’sthermoregulatory system may be based on a health status of the subject. The resilience of the subject’s thermoregulatory system, for example, may be related to the ability of the subject to control its thermoregulatory system, the ability of the subject’s thermoregulatory system to return to homeostasis if it veers from its homeostatic state, or the ability of subject’s thermoregulatory system to return to homeostasis in an ordered manner or fashion. In other words, there may be a plurality of thermoregulation measurements that make up a transitory phase of the thermoregulation system. The thermoregulation system may be in a transitory phase when the thermoregulation system veers from homeostasis. Homeostasis may be considered the normal state of the thermoregulation system of a subject. The resilience of a subject’s thermoregulatory system may be based on the ability of the thermoregulatory system to control itself when it veers from the normal state to a transitory phase over a period of time. The resilience of the thermoregulatory system may be related to the duration, efficient manner, or lack thereof, of which the subject’s thermoregulatory system returns to its homeostatic or normal state. In some embodiments, the way in which the subject’s thermoregulatory state returns to its homeostatic or normal state can be associated with a phenotype. The phenotype associated with the way in which the subject’ s thermoregulatory state returns to its homeostatic or normal state may allow various biological measurements to be used to assess the thermoregulatory system of the subject. For example, heart rate, heat flux, skin temperature, or the like may be used to assess the thermoregulatory system of the subject. Additionally, insight into the subject’s thermoregulatory state can be enhanced, amplified, or made more sensitive, by incorporating bilateral symmetry monitoring as discussed above.

[0451] In some embodiments, a non-resilient thermoregulatory system does not return to its homeostatic or normal state if it remains in a transitory state or veers from its homeostatic or normal state. In some embodiments, a non-resilient thermoregulatory system may return to its homeostatic state over an extended period of time. In other words, a nonresilience thermoregulation system may remain in a transitory state for an extended period of time. This may mean that the thermoregulation system may not be able to control itself. In some embodiments, a non-resilient thermoregulatory system may return to its homeostatic or normal state in an unordered manner or fashion.

[0452] In some embodiments, the resilience of the thermoregulatory system of a subject is assessed. The assessment of the resilience of the thermoregulatory system may leadto a recommendation provided to the subject. In some embodiments, the recommendation may include a recommendation for the subject to engage in an intervention based on the assessed resilience of thermoregulation. In some embodiments, a thermoregulation resilience score may be generated for the subject over a pre-determined time period. In some embodiments, the thermoregulation resilience score may be based on a data stream that includes a plurality of thermoregulation measurements of the subject. The plurality of thermoregulation measurements may be collected from any of the devices described above. In some preferred embodiments, the thermoregulation score may be based on thermoregulation measurements collected from one pair of sensors, or a plurality of pairs of sensors, placed symmetrically on bilateral symmetrical biological compartments of the subject. In some embodiments, the plurality of thermoregulation measurements may include any biological measurement that can be related to the thermoregulatory system of a subject.

[0453] The assessment may include determining whether a need for intervention as to the subject is above a predetermined threshold of urgency. A recommendation to engage in the intervention as to the subject may be generated as well. In this manner the recommendation may be tailored to or based on the thermoregulation resilience score, the need of intervention, or a combination thereof. The recommendation can be an intervention such as, for example, a recommended exercise regime, a dietary change, and medical or surgical intervention, a change to medication, a combination thereof.

[0454] In some embodiments, the threshold of urgency may be related to the urgency of which an intervention as to the subject may be needed. For example, the higher the thermoregulation resilience score, the less urgent the intervention as to the subject may be. In some embodiments, a predetermined threshold of urgency may determine the urgency of the intervention as to the subject needed. For example, the thermoregulation resilience score of a subject may be greater than a predetermined threshold of urgency, which may lead to an assessment that an intervention as to the subject is not as urgent as an intervention for a subject whose thermoregulation resilience score is less than the predetermined threshold of urgency. In some embodiments, the predetermined threshold of urgency may be subject specific. For example, the predetermined threshold of urgency may be based on or vary based on the health status of the subject.

[0455] In some embodiments, it may be useful to have a metric of health that is refreshed, reanalyzed, recalculated, observed, and / or reconfigured every month, three weeks, 14 days, 10 days, 7 days, 5 days, 3 days, 2 days, 1 day, daily, less than one day, or any measure of time in in between. This may be because the health of the subject can change over time. The metric of health can be enhanced, amplified, or made more sensitive by incorporating bilateral symmetry monitoring, as discussed herein. The frequency of which the metric of health is calculated or observed may allow the subject to quantify a transitory phenomenon that can exist in the health of a subject. For example, the transitory phenomenon may include a transition phase, or the transition or transitory state described above. Similarly, the transitory phenomenon may include the resilience of the subject’s thermoregulatory system and its ability to respond to a factor that causes the thermoregulatory system to veer from its homeostatic or normal state. This may relate to the homeostatic robustness of the subject. In other words, the homeostatic robustness of the subject may be based on the resilience of the thermoregulation system of the subject. In some embodiments, the homeostatic robustness may contribute to the pattern recognized within the plurality of data or characterization of the plurality of data. In some embodiments, the more resilient the thermoregulation system of subject is, the greater homeostatic robustness of the subject may be. In some embodiments, the transitory phase may exist for a period of time. The period of time can be any length of time. For example, the transitory phase may exist for an extended period of time, a non-extended period of time, or for a pre-determined period of time. In some embodiments, if the transitory period exists for an extended period of time, then the transitory phase may indicate a non-resilient thermoregulation system of the subject. This may be because the thermoregulatory system of the subject has less control over itself when it veers from its normal state. In some embodiments, if the transitory period exists for a non-extended period of time, then the transitory phase may indicate a resilience thermoregulation system of the subject.

[0456] In some embodiments, the assessment and generation of a recommendation may include alerting a medical practitioner that a recommendation has been generated. This may alert the medical practitioner to intervene as to the subject as necessary or as recommended. The recommendation can be an intervention such as, for example, a recommended exercise regime, a dietary change, and medical or surgical intervention, a change to medication, a combination thereof.

[0457] In some embodiments, the thermoregulation resilience score may be viewed in combination with other biological measurements. As explained herein, a thermoregulation resilience score may be enhanced, amplified, or made more sensitive by incorporating bilateral symmetry monitoring. This may allow for a greater probability of identifying a subject that may have a health status that may be in need of or may be at risk for treatment or intervention. For example, comparing the thermoregulation resilience score against the thermoregulation resilience score in combination with other biological measurements can better identify a subject that may have a health status that is in need of or at risk for treatment or intervention. This may be due to the positive correlation between the resilience of a subject’s thermoregulation system and the health status of the subject. The correlation may further be supported by the other biological measurements of the subject. In some embodiments, the other biological measurements may be collected from any a device that can sense and / or collect biological measurements.

[0458] In some embodiments, a plurality of thermoregulation resilience scores of a plurality of subjects may be plotted against a combination of a plurality of thermoregulation resilience scores and a plurality of other biological measurements. The combination may be considered a risk rank. The lower the risk rank, the greater risk the subject may have. In some embodiments, by allowing the risk rank to include both thermoregulation resilience scores and other biological measurements, there may be a greater probability of identifying a subject that may have a health status that is considered at risk. For example, this risk assessment can increase the probability of identifying a subject at risk by approximately 2-2.5 fold. Additionally, this risk assessment may be enhanced, amplified or made more sensitive by incorporating bilateral symmetry monitoring into the system and method, as described herein.

[0459] In some embodiments, the risk assessment may be tailored to remove at least one outlier that maybe included in the plot of the combination of a plurality of thermoregulation resilience scores and a plurality of other biological measurements. The at least one outlier that may be removed may include an outlier that is on a high end of the risk rank scale and a low end of the risk rank scale. By removing the outliers, the probability of identifying a subject with a health status that is considered at risk may increase by approximately 4-5 fold.

[0460] In some embodiments, the risk assessment discussed above may be used to identify serious medical events in fragile elderly subjects. For example, the fragile elderly subject may be constantly assessed for risk by constantly collecting a plurality of biological measurements and calculating the thermoregulation resilience score of the fragile elderly subject. The risk assessment described above can be used to predict the odds of a medical event occurring. Additionally, the risk assessment described above can be used to triage outpatient care resources, which can multiply the effective capacity of skilled medical care practitioners. This may decrease the morbidity associated with aging. This may also decrease the cost of related hospitalization by identify a health risk of an individual prior to a medical event occurring.

[0461] In some embodiments, the risk assessment discussed above may be used to determine medical insurance for a younger population that may be considered healthy. For example, in general, the younger population may need minimal medical care except in cases of medical events, traumatic injury, and / or unexpected diseases. By assessing the risk of the young population as described above, subjects of the younger population that are at risk or have physiological risk factors may be identified. By identifying these subjects, the subjects may be segmented into various subgroups of various risks. The subgroups may then be able to be incentivized to screen themselves to identify the subjects within the subgroup that are at a higher risk than others.

[0462] In some embodiments, the risk assessment discussed above may be used to identify workers or laborers that are at a higher risk for heat stroke than others. For example, workers or laborers that may be working in hot conditions may be at a higher risk for heat stroke than other individuals. In general, businesses may impose a set limit that workers or laborers may not work when the outdoor temperature exceeds a certain temperature threshold. However, some individuals may have a risk for heat stroke at a lower temperature threshold. This may be an all or nothing strategy. By identifying subjects that have a higher risk for heat stroke than others using the above described risk assessment, targeted work management may be implemented. This may lead to less downtime for the business, workers and / or laborers. This may also lead to less cost from injury of the workers or laborers.

[0463] In certain embodiments, the system is used to detect a sleep disorder of the subject. In this manner, a recognizable feature is preferably associated with the sleep disorder.In some embodiments, the system can include at least one pair of sensors configured to measure a plurality of heat flux measurements of the subject. The sensors can measure a plurality of heat flux measurements throughout at least one circadian period. The circadian period can include a period of time at which the subject is awake and a period of time of which the subject is asleep. In some preferred embodiments, there may be no interruption during the time the sensors are monitoring the subject. The at least one pair of sensors may include at least two skin temperature sensors that may be placed on symmetrical biological compartments on the subject. Measuring symmetrical biological compartments corrects mis-readings or mismeasurements that the sensors may measure. For example, the subject may sleep on a particular side of the subject’s body. This can lead to a misreading or mismeasurement by the sensor. Therefore, in some examples, having a pair of sensors can correct for any inaccurate measurements. The system may further include a processor. The processor may receive the plurality of heat flux measurements from the at least one pair of sensors. The processor may analyze the plurality of heat flux measurements and recognize at least one recognizable feature associated with the sleep disorder based at least on a relationship between the plurality of heat flux measurements. There may be at least one recognizable feature for each sleep disorder. For example, a recognizable feature may consist of an identifiable pattern or trend of heat flux measurements. In some embodiments, the recognizable feature may be related to the subject’s own circadian period. In some embodiments, the sleep disorder can be sleep apnea, insomnia, narcolepsy, or other disorders associated with sleep and maintaining a consistent sleep patterns. In some embodiments, the recognizable feature for sleep apnea may be a circular or elliptical pattern that has a greater diameter than the recognizable feature for normal sleep.

[0464] In some embodiments, the system may be able to determine an efficacy of a treatment provided to the subject for the sleep disorder. For example, the system may compare a plurality of measurements taken prior to the treatment provided and at various times throughout the treatment process to determine the efficacy of the treatment. In some embodiments, the efficacy of the treatment can be determined on a subject-by-subject basis.

[0465] In some embodiments, the system is used as part of the detection and treatment of a sleep disorder. In some embodiments, the system can include at least one pair of sensors configured to measure a plurality of heat flux measurements of the subject. The sensors can measure a plurality of heat flux measurements throughout a circadian period. Thecircadian period can include a period of time at which the subject is awake and a period of time of which the subject is asleep. In some embodiments, there may be no interruption in time that the sensors are measuring a plurality of heat flux measurements of the subject. The at least one pair of sensors may include at least two skin temperature sensors that may be placed on symmetrical biological compartments on the subject. Measuring symmetrical biological compartments helps correct inaccurate measurements that the sensors may measure. For example, the subject may sleep on a particular side of the subject’s body. This can lead to an inaccurate measurement measured by the sensor. Therefore, in some examples, having a pair of sensors may correct for any inaccurate measurements. The system may further include a processor. The processor may be able to dynamically receive the plurality of heat flux measurements. In this manner, the processor can dynamically learn from a plurality of previously received heat flux measurements. This way the system can better treat the subject. The processor may further be able to generate instructions based on the plurality of heat flux measurements. By dynamically learning from the plurality of previously received heat flux measurements, the processor can generate instructions that may be tailored to the subject. The system may further include a ventilation device that is operatively connected to the processor and able to receive instructions from the processor. Subsequently, the ventilation device can provide the treatment to the subject over a period of time. In some embodiments, the dynamic ability of the processor to receive the plurality of heat flux measurements can further allow the processor to communicate with the ventilation device to modify the treatment provided to the subject, if necessary.

[0466] In some embodiments, the system may further be able to determine an efficacy of a treatment of the system sleep disorder provided to the subject. For example, the system may compare a plurality of measurements taken prior to the treatment provided and at various time stamps throughout the treatment process to determine the efficacy of the treatment. In some embodiments, the efficacy of the treatment can be determined on a subject- by-subject basis.

[0467] In some embodiments, the sleep disorder can be sleep apnea, insomnia, narcolepsy, or other disorders associated with sleep and maintaining a consistent sleep patterns. In some embodiments, the ventilation device can be a continuous positive airway pressure (CPAP) machine, or a bilevel positive airway pressure (BiPAP) machine, or the like.

[0468] In certain embodiments, the system can identify at least one subject afflicted with a sleep disorder. In this manner, a recognizable feature may be associated with the sleep disorder. An certain embodiments, the system uses heat flux patterns obtained during wakefulness as an indicator to identify a sleep disorder, as heat flux patterns obtained during wakefulness correspond to sleep disorders. In these manners, the system permits a prescreening tool for diagnosing subjects with sleep disorder like symptoms.

[0469] In certain embodiments, the system may be able to determine a level of compliance of a subject for the treatment that the subject is provided. For example, the subject may be provided a treatment for a sleep disorder. In some embodiments, the system can include at least one pair of sensors configured to measure a plurality of heat flux measurements of the subject. The sensors can measure a plurality of heat flux measurements throughout a circadian period. The circadian period can include a period of time at which the subject is awake and a period of time of which the subject is asleep. In some embodiments, there may be no interruption in time that the sensors are monitoring the subject. The processor may be able to receive the plurality of heat flux measurements. The processor may further be able to determine a level of compliance. The system may further include a ventilation device that is operatively connected to the processor. Subsequently, the ventilation device can provide the treatment to the subject over a period of time. In this manner, the compliance can be based on the subject’s use of the ventilation device. In some embodiments, the system can be used to assist the subject in achieving a desired quality or function of life. In some embodiments, the level of compliance may be determined on a subject-by-subject basis. In this manner, compliance can be determined based on the individual subject’s use of the ventilation device. For example, Compliance A for Subject A may be different than Compliance B for subject B, therefore Compliance A for subject A can be determined based on Subject A’s use of the ventilation device and Compliance B for Subject B can be determined based on Subject B’s use of the ventilation device. In this manner, Subject A can achieve Compliance A and subject B can achieve Compliance B, even if Subject A cannot achieve Compliance B and Subject B cannot achieve Compliance A.

[0470] In some embodiments, a set of redundant sensor measurements with slightly different response characteristics can be combined to create a synthetic measurement which may be closer to or a more precise representation of the counterfactual measurement. In someembodiments, The set of redundant sensor measurements may be collected and measured by at least one additional internal temperature sensor. The additional internal temperature sensor may have a different time constant compared to the other sensors within the device. For example, the at least one additional internal temperature sensor may have a slower equilibration time compared to the other temperature sensors. This slower equilibrium can allow for each of the temperature sensors to sense the temperature at different rates, and therefore improve the accuracy and precision of the temperature measurements collected by the system, and provide a better representation of the counterfactual gap in the measurement data.

[0471] In some embodiments, the device can include a pair of sensors where each of the sensors are located on different (e.g., opposite or not the same) sides of the device. For example, a first sensor may be placed near a first side of the device and the second sensor may be placed near a second side of the device. The first sensor within the device may be located in a position to allow it to measure the ambient temperature. The second sensor within the device may be located in a position to allow it to measure the skin temperature of the user. The additional internal temperature sensor may be positioned at any location in between the first sensor and the second sensor. There may be any number of additional internal temperature sensors located in between the first sensor and the second sensor. The additional temperature sensor in combination with the first sensor and the second sensor can create a triangulation relationship between the sensors, where each sensor can measure temperature as a function of time, to help determine when a change in temperature began. This can help to minimize the counterfactual gap that may be calculated. To further minimize the counterfactual gap, a series of additional temperature sensors located in between the first sensor and the second sensor may be used to measure a gradient of temperatures across the device. In this manner, the counterfactual gap can be further minimized so that the counterfactual measurement is more accurately and precisely represented. The more accurate and precise the representation of the counterfactual measurement is, the more accurate and precise the data collected may be indicative of at least one emergent factor.

[0472] Fig. 1A and Fig. IB illustrate an example embodiment of the system detecting recognizable features from a subject’s response to an LPS injection. Because each figure illustrates the same or similar recognizable feature identified by the system in twodifferent subjects who are otherwise healthy subjects, Fig. 1 A will be described, however the description will also apply to Fig. IB. The thermoregulation response to an LPS injection can be seen in Fig. 1 A. As shown, the system can analyze the data generation by grouping the data into bins that encompass a period of about every 5 minutes. Upon an injection of LPS, the system may detect a decrease in heat flux or heat elimination. This may be because the thermoregulation system of the subject is trying to retain heat due to the LPS injection; by retaining heat, the subject may initiate a fever to try and fight the infection. As shown in Fig. 1 A, the system may detect a recognizable sub feature associated with the LPS injection and / or transition to retention of heat. The recognizable sub feature detected may be a downward trend. The system may detect the thermoregulatory system continuing to retain heat by identifying a recognizable feature created by the retention of heat by the subject. As shown in Fig. 1A, the system may detect another recognizable sub feature upon the thermoregulatory system of the subject releasing the heat it had previously retained. The recognizable sub feature detected may be an upward trend. The system may detect the recognizable feature as eliminating a greater amount of heat than normal. This may be because the thermoregulatory system of the subject had previously retained more heat than normal, so more heat may need to be eliminated. As shown in Fig. 1 A, the overall recognizable feature may be J-shape when plotted as heat flux as a function of ambient temperature. The overall recognizable feature detected by the system may be created by at least one transition stage before and after each recognizable sub feature. As further shown in Fig. 1A, the overall recognizable feature detected may be presented in a counterclockwise direction. The system may, in a preferred embodiment, be configured to detect a recognizable feature from the data generated from one pair of sensors, or a plurality of pairs of sensors, placed symmetrically on the subject. The system, so configured, would compare one analysis, or a plurality of analyses, of the response to the LPS injection and / or compare recognizable features generated from one or more pair of sensors symmetrically placed on the subject. In this manner, the system can incorporate bilateral symmetry monitoring into the overall analyses of the thermoregulation system of the subject. Such bilateral symmetry monitoring enhances and amplifies the ability of the system to detect disruption to homeostasis. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The useof the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0473] Fig. 2A, Fig. 2B, and Fig. 2C illustrate an example embodiment of recognizable features detected by the system from a circadian rhythm of a subject who is otherwise health for a night’s sleep. The detected recognizable features may be illustrated by a plot containing heat flux as a function of ambient temperature. Because each figure illustrates the same or similar recognizable features identified by the system in three different subjects who are otherwise healthy subjects, Fig. 2A will be described, however the description will also apply to Fig. 2B and Fig. 2C. As shown, similar to Fig. 1A, the system can group data points into bins spanning about every 5 minutes. As shown in Fig. 2A the system may detect and increase heat retention as the subject is falling asleep. The system may detect a recognizable sub feature of a downward trend that indicates the subject is falling asleep. As the subject stays asleep, the system may detect a recognizable sub feature that indicates the subject is sleeping. The recognizable feature may be a cluster of data points in the bottom right quadrant of the plot. As the subject wakes, the system may detect a recognizable sub feature that indicates the subject is waking. The system may detect the recognizable sub feature as an upward trend, indicating the subject is releasing the previously retained heat. As the subject remains awake, the system may detect a recognizable sub feature that indicates the subject has awoken. The recognizable feature may be a cluster of data points in the upper right quadrant. While the system can detect a recognizable sub feature for each stage of the sleep cycle, the system can also detect an overall recognizable feature for the sleep cycle. As shown, for example, the recognizable feature for the sleep cycle of a healthy person may be an ellipse. Furthermore, the ellipse may have a counter-clockwise rotation when plotted as in Fig. 2A. The system may detect a recognizable feature from the data generated from the pair of sensors symmetrically placed. The system may then compare the analysis of the circadian rhythm of the subject and / or compare the recognizable features generated from the pair of sensors symmetrically placed. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned inbetween a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0474] Fig. 3A, Fig. 3B, and Fig. 3C illustrate an example embodiment of recognizable features detected by the system from a circadian rhythm of a subject who is otherwise unhealthy for a night’s sleep. The detected recognizable features may be illustrated by a plot containing heat flux as a function of ambient temperature. Because each figure illustrates the same or similar recognizable feature identified by the system in three different subjects who are otherwise considered unhealthy subjects, Fig. 3A will be described, however the description will also apply to Fig. 3B and Fig. 3C. As in Fig. 1A and 2A, as shown, the system may group data points into bins spanning about every 5 minutes. As shown in Fig. 3A the system may detect an increase in heat retention as the subject is falling asleep. The system may detect a recognizable sub feature of a downward trend that indicates the subject is falling asleep. As the subject stays asleep, the system may detect a recognizable sub feature that indicates the subject is sleeping. However, the unhealthy subject may not be able to regulate heat in the same way as a healthy subject, as described in Fig. 2A, the system may detect a recognizable sub feature that indicates the subject is more awake than asleep. This can result in a recognizable sub feature that is a wide range of a cluster of data points in the top left quadrant of the plot. As the subject wakes, the system may detect a recognizable sub feature that indicates the subject is waking. The system may detect the recognizable sub feature as an upward trend, indicating the subject is releasing the previously retained heat. As the subject remains awake, the system may detect a recognizable sub feature that indicates the subject has awoken. The recognizable sub feature may be a cluster of data points in the upper left quadrant. While the system can detect a recognizable sub feature for each stage of the sleep cycle, the system can also detect an overall recognizable feature for the sleep cycle. As shown, the recognizable feature for the sleep cycle of an unhealthy person may be an inverted triangle. This may be because the unhealthy subject does not retain heat well, and therefore eliminates more heat than it can retain. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. The system may detect a recognizable feature from the data generated from a pair of sensors symmetrically placed. The system may then compare the analysis of subject’s circadianrhythm and / or compare the recognizable features generated from the pair of sensors symmetrically placed. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0475] Fig. 4A, 4B, 4C, and 4D illustrate an example embodiment of recognizable features detected by the system from exercise in a subject that is considered otherwise healthy. As shown in Fig. 4A, the system can detect a recognizable feature that is indicative of the subject transitioning from sleep to awake. The recognizable feature may be recognized in a counterclockwise direction starting in the bottom right quadrant of the plot and trending upward to indicate the subject may be eliminating heat. As shown in Fig. 4B, the system can detect a recognizable feature that is indicative of the subject hiking up a steep slope. The recognizable feature may be a cluster of data points in the upper left quadrant. This may be because the thermoregulatory system of the subject may be eliminating heat to maintain their thermoregulatory homeostasis. As shown in Fig. 4C, the system can detect a recognizable feature that is indicative of the subject resting after exertion of energy. The recognizable feature may be a widespread cluster of data points in the bottom right quadrant of the plot. This may be indicative of the subject retaining heat after elimination of heating during the exertion of energy. As shown in Fig. 4D, the system can detect a recognizable feature that is indicative of the subject exerting energy during exercise. The recognizable feature may be a cluster of data points in the upper right quadrant of the plot. This may be indicative of the subject eliminating heat to regulate its thermoregulatory system during exertion of energy. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. The system may detect a recognizable feature from the data generated from a pair of sensors symmetrically placed. The system may then compare the analysis of subject’s thermoregulation system and / or compare the recognizable features generated from the pair of sensors symmetrically placed.

[0476] Fig. 5A, 5B, 5C, 5D, 5E, 5F, 5G, 5H, 51 illustrate an example embodiment of recognizable features detected by the system from inflammation response in a subject. Byincorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0477] Fig. 6A, 6B, 6C, 6D, 6E illustrate an example embodiment of recognizable features detected by the system from sleep sequences in a subject that is considered otherwise healthy. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0478] Fig. 7A, 7B, 7C, 7D illustrate an example embodiment of recognizable features detected by the system from sleep sequences in a subject that is considered otherwise healthy. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0479] Fig. 8A, 8B, 8C, 8D illustrate an example embodiment of recognizable features detected by the system from sleep sequences in a subject that is considered otherwise healthy. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensorslocated on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0480] Fig. 9A, 9B, 9C, 9D illustrate an example embodiment of recognizable features detected by the system from sleep sequences in a subject that is considered otherwise healthy. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0481] Fig. 10A, 10B, 10C illustrate an example embodiment of recognizable features detected by the system from sleep sequences in a subject that is considered otherwise unhealthy. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified , or made more sensitive. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0482] Fig. 11A, 11B, 11C illustrate an example embodiment of recognizable features detected by the system from sleep sequences in a subject that is considered otherwise unhealthy. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, , or made more sensitive. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0483] Fig. 12 A, 12B, 12C, 12D, 12E illustrate an example embodiment of recognizable features detected by the system from sleep sequences in a subject that is considered otherwise unhealthy. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0484] Fig. 13 A and 13B illustrate an example embodiment of recognizable features detected by the system from menopausal hot flash in a subject. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0485] Fig. 14A, 14B, 14C, 14D, 14E, 14F, 14G, 14H illustrate an example embodiment of recognizable features detected by the system from physical exertion a subject. Fig. 14A illustrates an example embodiment of recognizable features detected by the system when the subject is aroused by sleep. Fig. 14B illustrates an example embodiment of recognizable features detected by the system when the subject is driving to the trailhead. Fig. 14C illustrates an example embodiment of recognizable features detected by the system when the subject is level hiking leading to steep ascent. Fig. 14D illustrates an example embodiment of recognizable features detected by the system when the subject is on summit and resting. Fig. 14E illustrates an example embodiment of recognizable features detected by the system when the subject is on a steep descent. Fig. 14F illustrates an example embodiment of recognizable features detected by the system when the subject is cooling down in the river. Fig. 14G illustrates an example embodiment of recognizable features detected by the system when the subject has finished the hike. Fig. 14H illustrates an example embodiment of recognizablefeatures detected by the system when the subject has fallen asleep. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. The system may detect a recognizable feature from the data generated from a pair of sensors symmetrically placed. The system may then compare the analysis of subject’s thermoregulation system and / or compare the recognizable features generated from the pair of sensors symmetrically placed. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0486] Fig. 15 A, 15B, 15C, 15D, 15E, 15F, 15G, 15H, 151, 15 J, 15K, 15L, 15M, 15N illustrate an example embodiment of recognizable features detected by the system for an 8 hour period over days in a subject that is otherwise considered healthy. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0487] Fig. 16A, 16B, 15C, 16D, 16E, 16F, 16G, 16H, 161, 16J, 16K, 16L, 16M, 16N illustrate an example embodiment of recognizable features detected by the system for an 8 hour period over days in a subject that is otherwise considered healthy. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0488] Fig. 17A, 17B, 17C, 17D, 17E, 17F, 17G, 17H, 171, 17J, 17K, 17L, 17M, 17N illustrate an example embodiment of recognizable features detected by the system for an 8 hour period over days in a subject that is otherwise considered healthy. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0489] Fig. 18 A, 18B 18C, 18D, 18E, 18F, 18G, 18H, 181, 18 J, 18K, 18L, 18M, 18N illustrate an example embodiment of recognizable features detected by the system for an 8 hour period over days in a subject with chronic heart failure. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0490] Fig. 19A, 19B 19C, 19D, 19E, 19F, 19G, 19H, 191, 19J, 19K, 19L, 19M, 19N illustrate an example embodiment of recognizable features detected by the system for an 8 hour period over days in a subject with chronic heart failure. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced, amplified, or made more sensitive. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0491] Fig. 20A, 20B, 20C, 20D, 20E, 20F, 20G, 20H, 201, 20J, 20K, 20L, 20M, 20N illustrate an example embodiment of recognizable features detected by the system for an 8 hour period over days in a subject with chronic heart failure. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced and amplified. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0492] Fig. 21A and 21B illustrate an example embodiment of plurality of thermoregulation measurements of a subject over a period of time. In some embodiments, the plurality of thermoregulation measurements may be collected with any of the devices described above. Fig. 21 A shows the raw data of the plurality of thermoregulation measurements are shown external temperatures as a function of a thermoregulation measurement. Fig. 21B shows the plurality of thermoregulation measurements binned together and represented as spacefilling hexagons to facilitate point-to-point distance computation. Binning of the plurality of thermoregulation measurements can allow for various geometric distance calculations. For example, binning the plurality of thermoregulation measurements into a plurality of hexagons can allow for an absolute distance between a first thermoregulation measurement of the plurality of thermoregulation measurements and a second thermoregulation measurement of the plurality of thermoregulation measurements. This can allow for a more accurate assessment of the data collected to help determine whether a thermoregulation system of a subject is resilient and / or whether intervention as to the subject is needed and the urgency of such an intervention. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced and amplified. The system may detect a recognizable feature from the data generated from a pair of sensors symmetrically placed. The system may then compare the analysis of subject’s thermoregulation system and / or compare the recognizable features generated from the pair of sensors symmetrically placed. Additionally, the recognizable features may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned inbetween a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0493] Figs. 22A, 22B, 22C, 22D, 22E, 22F, 22G, 22H, 221, 22J, 22K, 22L, 22M, 22N, 220 illustrate an example embodiments of binned plurality of thermoregulation measurements of a subject over a period of fifteen days. Figs. 22A-22O show that an overall recognizable pattern illustrated through the daily collection of the plurality of thermoregulation measurements may be different or vary on a daily basis. Collecting a plurality of thermoregulation measurements over a period of time on a daily basis, for example over 15 days on a daily basis, may allow for a more accurate determination as to the resilience of the thermoregulation system of a subject and / or the resilience score of the thermoregulation system of the subject. The resilience score may be calculated by first determining the probability of a binned thermoregulation measurement moving and therefore changing the absolute difference between the thermoregulation measurement and another thermoregulation measurement. The change in absolute difference between a first thermoregulation measurement and a second thermoregulation measurement may change the recognizable pattern of the overall plurality of thermoregulation measurements. For example, the change in absolute difference may cause the recognizable pattern to become more ordered or more unordered based on the change. Based on the orderliness of the recognizable pattern, artificial intelligence can determine the similarities and differences between the orderliness of the recognizable pattern of the plurality of thermoregulation measurements of the subject and the orderliness of at least one model recognizable pattern. Artificial intelligence can determine the probability the orderliness of the recognizable pattern of the plurality of thermoregulation measurements of the subject is like the orderliness of at least one of the model recognizable patterns within a confidence level. For example, the closer the determined probability is to 1, the more like the orderliness of the recognizable pattern of the plurality of thermoregulation measurements of the subject is to the model recognizable pattern that implies a high functioning and / or efficient thermoregulation system. This may lead to a higher thermoregulation resilience score. For example, the closer the determined probability is to 0, the less like the orderliness of the recognizable pattern of the plurality of thermoregulation measurements of the subject is to the model recognizable pattern that implies a high functioning and / or efficient thermoregulation system. The less likethe orderliness of the recognizable pattern of the plurality of thermoregulation measurements of the subject is to the model recognizable pattern that implies a high functioning and / or efficient thermoregulation system, the lower functioning the thermoregulation system is of the subject. This may lead to a lower thermoregulation resilience score. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced and amplified. The system may detect a recognizable feature from the data generated from a pair of sensors symmetrically placed. The system may then compare the analysis of subject’s thermoregulation system and / or compare the recognizable features generated from the pair of sensors symmetrically placed. The thermoregulation score may include the comparative analysis implemented by incorporating bilateral symmetry monitoring. Additionally, the recognizable feature may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0494] Fig. 23 illustrates an example embodiment of a plurality thermoregulation resilience scores of a subject over a period of time defined as one month. As shown, the graph illustrates the date and the subject’s corresponding thermoregulation resilience score on that date. As further shown, the thermoregulation resilience score increases and decreases over the period of time defined as one month. The greater range and variety of thermoregulation resilience scores determined over the period of time, the greater change in the subject’s health status on a day-to-day basis. This may be related to a periodicity of the subject’s health status. A periodicity of the subject’s health status can correlate to a more positive health status of a subject or a more negative health status of a subject. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced and amplified. The system may detect a recognizable feature from the data generated from a pair of sensors symmetrically placed. The system may then compare the analysis of subject’s thermoregulation system and / or compare the recognizable features generated from the pair of sensors symmetrically placed. The thermoregulation score may include the comparative analysis implemented by incorporating bilateral symmetry monitoring. Additionally, the recognizable features may be detected with greater accuracy and precision by use of at leastone additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0495] Fig. 24 illustrates an example embodiment of a plurality thermoregulation resilience scores of a subject over a period of time defined as one month. As shown, the graph illustrates the date and the subject’s corresponding thermoregulation resilience score on that date. As shown, the thermoregulation resilience score remains relatively constant over the period of time defined as one month. The less range and variety of thermoregulation resilience scores determined over the period of time, the less change in the subject’s health status on a day-to-day basis. As shown, the subject has a consistent relatively high thermoregulation resilience score. In other words, the thermoregulation resilience scores are close to 1. This may mean the subject generally had positive health status on a day-to-day basis over the period of time. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced and amplified. The system may detect a recognizable feature from the data generated from a pair of sensors symmetrically placed. The system may then compare the analysis of subject’s thermoregulation system and / or compare the recognizable features generated from the pair of sensors symmetrically placed. The thermoregulation score may include the comparative analysis implemented by incorporating bilateral symmetry monitoring. Additionally, the recognizable features may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0496] Fig. 25 illustrates an example embodiment of a plurality thermoregulation resilience scores of a subject over a period of time defined as one month. As shown, the graph illustrates the date and the subject’s corresponding thermoregulation resilience score on that date. As shown, the thermoregulation resilience score remains relatively constant over the period of time defined as one month. The less range and variety of thermoregulation resilience scores determined over the period of time, the less change in the subject’s health status on aday-to-day basis. As shown, the subject has a consistent relatively low thermoregulation resilience score. In other words, the thermoregulation resilience scores are closer to 0 than to 1. This may mean the subject generally had negative health status on a day-to-day basis over the period of time. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced and amplified. The system may detect a recognizable feature from the data generated from a pair of sensors symmetrically placed. The system may then compare the analysis of subject’s thermoregulation system and / or compare the recognizable features generated from the pair of sensors symmetrically placed. The thermoregulation score may include the comparative analysis implemented by incorporating bilateral symmetry monitoring. Additionally, the recognizable features may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0497] Fig. 26 illustrates an example embodiment of a plurality thermoregulation resilience scores of a subject over a period of time defined as one month. As shown, the graph illustrates the date and the subject’s corresponding thermoregulation resilience score on that date. As shown, the thermoregulation resilience score remains relatively constant over the period of time defined as one month. The less range and variety of thermoregulation resilience scores determined over the period of time, the less change in the subject’s health status on a day-to-day basis. As shown, the subject has a consistent relatively low thermoregulation resilience score. In other words, the thermoregulation resilience scores are closer to 0 than to 1. This may mean the subject generally had negative health status on a day-to-day basis over the period of time. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced and amplified. The system may detect a recognizable feature from the data generated from a pair of sensors symmetrically placed. The system may then compare the analysis of subject’s thermoregulation system and / or compare the recognizable features generated from the pair of sensors symmetrically placed. The thermoregulation score may include the comparative analysis implemented by incorporating bilateral symmetry monitoring. Additionally, the recognizable features may bedetected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0498] Fig. 27 illustrates an example embodiment of a plurality of thermoregulation measurements of three subjects over a period of time defined as one day. As shown, a first subject is represented by the red data points, a second subject is represented by the yellow data points, and a third subject is represented by the green data points. As shown in Fig. 27, the thermoregulation resilience score of the subject can change throughout the day. As further shown in Fig. 27, while the thermoregulation resilience score may change, the trend, direction, or pattern of which the thermoregulation resilience score changes along remain the similar or consistent throughout the day. For example, the first subject has a range of thermoregulation resilience scores through the day which may lead to an assessment resulting in determination of the need for intervention as to the subject or a determination of a health status that may be negative. As shown in Fig. 27, for example, the first subject may have various thermoregulation resilience scores throughout the day, but the first subject generally remains in a first range of thermoregulation resilience scores. The first range of thermoregulation resilience scores for the first subject, as shown, is different than a second range of thermoregulation resilience scores for the second subject, which is different than a third range of thermoregulation resilience scores for the third subject. This can allow the thermoregulation system of a subject to be related to a phenotype. In this manner, any sensor sensing a biological measurement can be used in the assessment of the thermoregulation system of a subject. The thermoregulation resilience scores of the first subject may relate to a recognizable pattern that correlates to a negative health status. Similarly, as shown in Fig. 27, for example, the second subject may have various thermoregulation resilience scores throughout the day, but the second subject generally remains in the second range of thermoregulation resilience scores. The thermoregulation resilience scores of the second subject may relate to a recognizable pattern that correlates to an average health status. Similarly, as shown in Fig. 27, for example, the third subject may have various thermoregulation resilience scores throughout the day, but the third subject generally remainsin the third range of thermoregulation resilience scores. The thermoregulation resilience scores of the third subject may relate to a recognizable pattern that correlates to a positive health status. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced and amplified. The system may detect a recognizable feature from the data generated from a pair of sensors symmetrically placed. The system may then compare the analysis of subject’s thermoregulation system and / or compare the recognizable features generated from the pair of sensors symmetrically placed. The thermoregulation score may include the comparative analysis implemented by incorporating bilateral symmetry monitoring. Additionally, the recognizable features may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0499] Fig. 28 illustrates an example embodiment of a plurality of thermoregulation resilience scores of a plurality of individuals plotted as a function of a combination of a plurality of thermoregulation resilience scores and a plurality of other biological measurements. This combination may be considered the risk rank. This plot may be used to identify a subject and / or assess the risk of the subject having a health status that may require treatment or intervention. As shown in Fig. 28, the majority of data points are in the range of approximately -1000 to 2000 thermoregulation resilience scores. The lower the risk rank, the higher risk a subject is at for a health status that may be considered at risk for treatment or intervention. As further shown in Fig. 28, by assessing the risk of a subject in this manner, the probability of assessing the risk of the subject correctly is 5-25% and the efficiency multiplier is 2.6. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced and amplified. The system may detect a recognizable feature from the data generated from a pair of sensors symmetrically placed. The system may then compare the analysis of subject’s thermoregulation system and / or compare the recognizable features generated from the pair of sensors symmetrically placed. The thermoregulation score may include the comparative analysis implemented by incorporating bilateral symmetry monitoring. Additionally, the recognizable features may bedetected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0500] Fig. 29 illustrates an example embodiment of a plurality of thermoregulation resilience scores (distinct from those score depicted in Fig. 28) of a plurality of individuals plotted as a function of a combination of a plurality of thermoregulation resilience scores and a plurality of other biological measurements. This combination may be considered the risk rank. This plot may be used to identify a subject and / or assess the risk of the subject having a health status that may require treatment or intervention. As shown in Fig. 29, at least one outlier is removed from plot, therefore allowing the majority of data points to be in the range of approximately -500 to 1000 thermoregulation resilience scores. The lower the risk rank, the higher risk a subject is at for a health status that may be considered at risk for treatment or intervention. As further shown in Fig. 29, by assessing the risk of a subject in this manner, the probability of assessing the risk of the subject correctly is 5-30% and the efficiency multiplier is 4.43. As shown in Fig. 29, by removing the outliers of the data set, the probability of correctly assessing the risk of the subject is increased. By incorporating bilateral symmetry monitoring the analysis of the thermoregulation system of the subject may be enhanced and amplified. The system may detect a recognizable feature from the data generated from a pair of sensors symmetrically placed. The system may then compare the analysis of subject’s thermoregulation system and / or compare the recognizable features generated from the pair of sensors symmetrically placed. The thermoregulation score, and thus the risk rank, may include the comparative analysis implemented by incorporating bilateral symmetry monitoring. Additionally, the recognizable features may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0501] According to the examples depicted in Fig. 28 or 29, wearable device disclosed herein may be configured to provide precise information for a single individual toimprove that individual’s medical care or optimize lifestyle. Alternatively, a plurality of wearable device may be distributed across a large cohort to gain operating efficiencies even though the devices does not have sufficient precision guide care on a single individual. These embodiments address various key problems with the existing art. For example, the existing health care industry has limiting established practices, involves large populations which are subject to a low rate of high-cost events; minimal effort are made to intervene with these events because the cost of prediction exceeds the value generated from prevention; mitigation strategies are executed at the population level (i.e. health guidance stratified by demographics) rather than at the individual level; need utility requires the ability to efficiently segment population to improve odds of event accurate prediction. Additionally, the recognizable features may be detected with greater accuracy and precision by use of at least one additional internal temperature sensor within the device positioned in between a pair of sensors located on different sides of the device. The use of the at least one additional internal temperature sensor can minimize the counterfactual gap so that the measurements more accurately and precisely represent the counterfactual measurement.

[0502] Case Example 1 : Serious medical events in fragile elderly

[0503] Constant monitoring by skilled medical professional can prevent many hospitalizations in elderly. Cost of skilled outpatient care is prohibitive at scale. An loT solution which improves the relative odds of predicting a medical event can be used to triage outpatient care resources. This triage function multiplies the effective capacity of skilled nurses. Superior outcomes include decrease is morbidity associated with aging and decrease in costs related to hospitalization.

[0504] Case Example 2: Medical Insurance for healthy young population

[0505] Young people need minimal medical care except in the case of traumatic injury or unexpected disease. Demographic parameters determine insurance rates. But unknown physiologic risk factors include the abilities to usefully segment subgroups of different risk and incentivize screening of riskiest group and to identify stressed days where injury is more likely and incentivize customer to avoid unnecessary risk. Superior outcomes include: Reduction of morbidity associated with accidents and preventable disease and cost reduction for insurers and customers.

[0506] Case 3: Heat Stroke in Laborers

[0507] Working in hot conditions is risky, particularly agricultural or logistics. Most businesses have an all-or-nothing strategy. All workers labor until the temperature exceeds a threshold, then everyone stops. Identifying a individuals who are most at risk of heat stroke, will allow targeted work management. Superior outcomes include: less downtime and less cost from injury.

[0508] Fig. 30 illustrates an example objective of the disclosed technology: enable telemedicine call centers to be more efficient. As shown in the figure, it may be difficult to differentiate or distinguish between or among patients who are at risk, patients who are at a higher risk, patients who are in need of medical attention, and patients who are at not substantially at risk. The AI / ML (artificial intelligence / machine learning) algorithm implemented by the EMR (emergency room) can screen for patients who are at higher risk, and the AI / ML algorithm of Emerja can further identify patients who are in need of medical attention and send that information to the telemedicine call center. Thus, patients who are at highest risk and in need of medical attention can be correctly prioritized, allowing the telemedicine call center to run more efficiently.

[0509] Fig. 31 illustrates another example objective of the disclosed technology: enable more patients from general population to enter funnel. As shown in the figure, it may be difficult to tell apart patients who are at risk, at higher risk, in need of medical attention, or not at risk. The AI / ML algorithm implemented by the EMR and the AI / ML algorithm of Emeija can identify patients who are at highest risk and send that information to the telemedicine call center. Thus, only the small fraction of patients who are at highest risk get the medical attention, and a larger population can be screened in a given period of time.

[0510] Fig. 32 illustrates certain problems faced by telemedicine call centers. In particular, most high-risk patients are stable, and the actual number of patients who are at highest risk and in need of active care (e.g., hospitalization) is generally very low. Precious medical resources may be wasted if the telemedicine call center determines that all the high- risk patients are in need of active care, for example. Therefore, one objective of the disclosed technology is to enhance patient quality of care through reduction of avoidable admissions and emergency room utilization and increase care to an optimal level with primary care providers and appropriate specialists.

[0511] Fig. 33 illustrates a solution to problems faced by telemedicine call centers according to some embodiments of the disclosed technology: augmenting risk model with realtime triage. For example, after the health service provider identifies the patients at higher risk of hospitalization, the Emerja device wore by the patients may automatically triage those patients in real-time and identify only the fraction of patients at highest risk and requiring medical attention. Then, the Emerja device may send the information about the highest risk patients to health service provider for care manager evaluation.

[0512] Fig. 34 illustrates an example workflow consistent with the solution described in connection with Fig. 33. Health service provider’s patients may be screened by an EMR Al to identify patients at higher risk of hospitalization. Assuming that Eneiji (an Emeija device) data stream correlates to health service provider’s care manager call metrics, Eneiji may be used to further screen the patients at higher risk of hospitalization and send the screened information to health service provider for care manager evaluation, which produces call / triage metrics. Depending on the result of the care manager evaluation, some patients may be recommended to have a primary care physician (PCP) visit. Data generated in the PCP visit may be sent to health service provider for PCP evaluation, which produces economic metrics and clinical metrics. Enerji data stream may be used to improve clinical and economic outcomes. Furthermore, health service provider’s patients may be screened by Eneiji directly, and the screened information may be sent to health service provider for nurse practitioner (NP) triage, then for PCP evaluation. Thus, Enerji data stream can improve clinical and economic outcomes in a broader population.

[0513] In some aspects, the disclosed technology relates to a method of monitoring treatment effect. The method includes administering a first treatment to a subject while obtaining a heat flux pattern indicative of a health state of the subject over time identifying a heat flux signature of the subject over time based on the heat flux pattern; and monitoring the effect of the first treatment by comparing the heat flux signature before and after the administration of the first treatment. In certain embodiments, the heat flux pattern is obtained by a plurality of heat flux measurements on the subject. In certain embodiments, the heat flux pattern is measured by a wearable device worn by the subject. In certain embodiments, the effect of the first treatment is monitored over time. In certain embodiments, the method further comprises determining the pharmacokinetics / pharmacodynamics of the first treatment basedon the effect of the first treatment monitored over time. In certain embodiments, the method further comprises determining complication of the first treatment based on the heat flux pattern. In certain embodiments, the first treatment is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical therapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

[0514] Fig. 35 shows experimental data gathered from monitoring treatment effect on a fetal-maternal system. On the bottom-left panel, the y-axis shows the heat flux signature being monitored, and the x-axis shows time since admission of a pregnant woman. The pregnant woman was suffering from the HELLP (Hemolysis, Elevated Liver enzymes and Low Platelets) syndrome when she was admitted, and the measured heat flux signature was in the unhealthy state (i.e., red). The “Rx” box indicates the time period in which a drug was administered intravenously to the pregnant woman. It can be seen that the heat flux signature started to change towards the healthy state (i.e., green) since administration of the drug. “Failure” indicates when the drug administration method was switched from intravenous administration to oral administration. It can be seen that this causes the heat flux signature to change back towards the unhealthy state (i.e., red). After delivery of the baby, the heat flux signature mostly remains at a healthy state (i.e., green) even without any drug intervention.

[0515] In some aspects, the disclosed technology relates to a method of monitoring fetal-maternal well-being. The method includes obtaining a heat flux pattern indicative of a health state of a pregnant woman over time; identifying a heat flux signature of the pregnant woman over time based on the heat flux pattern; and monitoring the wellbeing of the fetus and the pregnant woman based on the heat flux signature over time. In certain embodiments, the heat flux pattern is obtained by a plurality of heat flux measurements on the pregnant woman. In certain embodiments, the heat flux pattern is measured by a wearable device worn by the pregnant woman.

[0516] Fig. 36A shows experimental data gathered from monitoring a healthy pregnant woman. On the bottom-left panel, the y-axis shows the heat flux signature being monitored, and the x-axis shows time since admission of the pregnant woman. As shown in the figure, the state of the heat flux signature is mostly green (i.e., healthy).

[0517] Fig. 36B shows experimental data gathered from monitoring a pregnant woman having the HELLP syndrome. On the bottom-left panel, the y-axis shows the heat flux signature being monitored, and the x-axis shows time since admission of the pregnant woman. As shown in the figure, compared to healthy pregnancy, the state of the heat flux signature of the woman having the HELLP syndrome is mostly red (i.e., unhealthy).

[0518] Case Example 4: Identification and Treatment of Sleep Disorders

[0519] Interrupted sleep and modified sleep patterns, often caused by sleep disorders, are a generalized health risk, particularly in overweight subjects. Current technology for monitoring sleep disorders (for example, sleep apnea) is cumbersome, often leading to lack of compliance, and is prone to error. Superior outcomes can be achieved by using systems and methods as disclosed herein to monitor heat flux signatures and / or patterns, and to identify heat flux patterns associated with sleep disorders; such pattern permit the reliable diagnosis, and treatment, of such disorders, particularly where such systems and methods facilitate patient compliance.

[0520] Figs. 37, 38, 39, 40 and 40A show experimental data gathered from monitoring a male subject, aged in his mid-40’ s, who uses a CPAP machine and has sleep disorders including apnea and a history of heart issues. As shown in Fig. 37, the bottom right time graph displays three time series subject data where the subject’s heat flux signature is shown. Each time series is equal to a circadian rhythm sequence. As shown in Figs. 38, 39, and 40, the bottom right time graph displays a time series of the subject’s heat flux measurements. The time series represented in each figure is equal to one circadian period. The data reflect the subject’s skin temperature dropping progressively overnight. By monitoring the subject over a complete circadian cycle, the subject’s sleep patterns can be better understood when the subj ect’ s awake patterns are monitored in addition to their asleep patterns. For example, the subject may be aware that he has a sleep disorder, however, monitoring the subject throughout a full circadian period, instead of only while the subject is asleep, can better determine the type and nature of the sleep disorder the subject has based on the patterns associated and recognized with the subject specifically. In this manner, the subject may receive better and / or more specialized or targeted treatment for its specific disorder. As shown in Fig. 40 A, the bottom right time graph displays a time series of the subject’s heat flux measurements during the daytime, while the subject is awake. The patterns recognized fromthe heat flux measurements may be used to help detect or identify a sleep disorder. As shown, the recognizable patterns may be similar to patterns associated with a subject receiving hemodialysis.

[0521] Figs. 41, 42, 43, 44, 45, 46, and 47 show experimental data gathered from monitoring a male subject, aged in his mid-40’ s, who uses a CPAP machine and has sleep disorders including sleep apnea and allergic episodes. As shown in Fig. 41, the bottom left time graph displays four time series subject data where the subject’s heat flux measurements are plotted against time. Each time series corresponds to a circadian period. As shown in Fig. 42, 43, 44, and 45, the bottom right time graph displays a time series of the subject’s heat flux signature. Each time series corresponds to a circadian rhythm sequence. Fig. 42 and 43 display heat flux measurement data from a circadian period occurring over a weekend. Fig. 42 displays heat flux measurement data measured from the subject when he had a heavy intake of alcohol during the circadian period. Fig. 43 displays heat flux measurement data measured from the subject when he had allergy symptoms during the circadian period. Fig. 44 and 45 display heat flux measurement data from a circadian period occurring over weekdays. Measuring the data throughout a circadian period can help with understanding how the subject’s awake activities or patterns can affect the subject’s asleep patterns. In this manner, the need for the subject’s use of the CPAP machine can be assessed and identified based on the patterns recognized from the heat flux measurements collected. In this manner, treatment provided to the subject can be tailored to the subject’s specific needs. Fig. 46 displays heat flux measurement data of the subject throughout a ninety minute sneezing sequence that occurred at 4:00am. As shown, there is a static and distinct thermal state of the subject during this. Due to the distinct thermal state displayed, it is possible the subject was misdiagnosed with allergies, when in reality, the subject may have a sleep disorder. In this manner, monitoring the subject can function as a prescreening diagnostic tool for diagnosing sleep disorders. Fig. 47 displays heat flux measurement data of the subject over a three hour period. As shown, the heat flux measurements are concentrated on the lower right portion of the time graph. This recognizable pattern may be associated with a sleep disorder.

[0522] Figs. 48, 49, 50, 51, 52, 53, and 54 show experimental data gathered from monitoring a male subject, aged in his mid 40’s, who uses a CPAP machine and has sleep disorders including sleep apnea involving approximately less than ten second pauses in heartrhythm. As shown in Fig. 48, the bottom right time graph displays a time series subject data where the subject’s heat flux measurements are plotted against time. The time series corresponds to four circadian periods. As shown in Figs. 49, 50, 51, and 52, the bottom right time graph displays a time series of the subject’s heat flux measurement data. Each time series corresponds to a circadian rhythm sequence. Figs. 49 and 50 display heat flux measurement data from a circadian period that included the subject performing a gym routine. As shown, the data displays a pattern that is representative of the subject having a tendency to experience a lower skin temperature during activity. Figs. 51 and 52 display heat flux measurement data from a circadian period that did not include the subject performing a gym routine. As shown, the subject’s thermoregulatory system experienced a greater range of heat flux measurements and lower skin temperatures when the circadian period included a gym routine compared to when the circadian period did not include a gym routine. This pattern may be associated with a sleep disorder. Fig. 53 and 54 further illustrate the ability of the subject’s thermoregulatory system to experience a greater range of heat flux measurements and lower skin temperatures when the subject performs a gym routine in its circadian period.

[0523] Fig. 55A illustrates an example embodiment of recognizable features detected by the system that includes a pair of sensors bilaterally symmetrically placed on the subject. As shown in Fig. 30A, the system has collected measurements regarding perfusion within the subject between the right index of the subject and the left index of the subject. The system can collect these measurements over any period of time. As shown in Fig. 55A, the system has collected these measurements over a 10 day period. The similarity and symmetry in perfusion between the right index and left index of the subject can be shown in Fig. 55 A.

[0524] With reference to Fig. 55A, Fig. 55B illustrates an example embodiment asymmetry detected by the system between the right index and left index of the subject. As shown in Fig. 30B, the system has analyzed the collected measurements shown in Fig. 30A regarding perfusion within the subject between the right index of the subject and the left index of the subject and determined the asymmetry between the right index of the subject and the left index of the subject. Based on the asymmetry, whether the perfusion was disrupted on either the right index of the subject or the left index of the subject can be determined and when the disruption occurred can be determined as well.

[0525] Fig. 56 illustrates an example embodiment of a wearable device that is able to collect a plurality of data indicative of at least one emergent factor of a user. As shown in Fig. 31, the device can include a first side and a second side. A first sensor is located on the first side of the device and a second sensor is located on the second side of the device. As shown, the device further includes a circuit board located in between the first side and the second side. The device further includes at least one additional internal temperature sensor located internally to the device and in between the first sensor and the second sensor. The at least one additional internal temperature sensor can be located at location in between the first sensor and the second sensor.

[0526] Fig. 57A illustrates a physical parameter plotted as a function of time. As shown, a set of redundant sensor measurements with slightly different response characteristics are combined to create a synthetic measurement that is a more accurate and precise representation of the counterfactual measurement.

[0527] Fig. 57B illustrates a counterfactual gap plotted as a function of time. As shown, a second of redundant sensor measurements with slightly different response characteristics are combined to create a synthetic measurement that is a more accurate and precise representation of the counterfactual measurement.

[0528] In certain embodiments, wound healing is monitored by detecting heat flux, including the detection of patterns (such as a circadian pattern) in heat flux. Wound healing is affected by an inflammatory cascade, and thus monitoring of heat flux will monitor changes in inflammation, hence monitor changes in wound healing. For example, when there is a known wound on an extremity, at a known or unknown stag of healing, the disclosed system and method can be used to detect inflammation and wound healing by comparing two separate but symmetrical extremities, and alternatively can monitor wound healing by monitoring inflammation in a single location at the wound, or positioned relative to the wound.

[0529] In certain embodiments, fetal-maternal mismatches or complications of pregnancy, such as for example gestational diabetes, preeclampsia, preterm labor, depression and anxiety, miscarriage, stillbirth, iron-deficiency anemia, and infections, may be monitored by detecting heat flux from the mother alone. Such monitoring is non-invasive particularly with respect with current methods for monitoring such conditions. The fetus’ own metabolism is an independent heat generator inside the mother, but the fetus’ heat dissipation is totallydependent on the mother’s heat dissipation mechanisms. Therefore, the mother’s heat signature will vary based on changes in the fetus’ heat flux. Anomalies in the mother’s heat signature are a signal of fetal-maternal mismatches and metabolic anomalies in the fetal-maternal combinationEXAMPLE 1

[0530] A system is configured to identify at least one recognizable feature associated with wound healing. The system includes at least one sensor placed on a biological compartment of a human, the sensor being configured to measure a plurality of heat flux measurements of a wound on or near the biological compartment in a non-invasive manner, and to generate data streams for the wound over one or more time periods, The system further includes a processor configured to receive the data streams and analyze the data streams to recognize at least one recognizable feature associated with wound healing processes based at least on a relationship between the data streams. In one embodiment, where the wound is not healing, the system recognizes at least one recognizable feature similar to those observed in continued inflammation. If the wound is healing, the system recognizes at least one recognizable feature similar to those observed in improved inflammation. The healing processes of different types of wounds (either associated with infection or not associated with infection), such as puncture wounds, shunt wounds, surgical wounds, suture wounds, and incision wounds, or wounds associated with thermal, chemical or electric burns, bites and stings, gunshot wounds, abrasions, lacerations, skin tears, ulcers, contusions, seroma, hematoma, crush injuries, generate different recognizable features.EXAMPLE 2

[0531] A system is configured to identify at least one recognizable feature associated with at least one metabolic anomaly in a fetal-maternal combination. The system includes at least one sensor placed on a biological compartment of the mother, the sensor being configured to measure a plurality of heat flux measurements of the biological compartment in a non-invasive manner, and to generate data streams for the biological compartment over one or more time periods, The system further includes a processor configured to receive the data streams and analyze the data streams to recognize at least one recognizable feature associatedwith certain metabolic anomalies in the fetal-maternal combination based at least on a relationship between the data streams. For example, where the mother develops gestational diabetes, the system recognizes at least one recognizable feature similar to those observed in diabetes patients. If the mother develops preeclampsia, the system recognizes at least one recognizable feature similar to those observed in patients having hypertension.

[0532] The embodiments described are examples. Various changes could be made in the above devices and methods without departing from the scope of the invention. All subject matter described in this disclosure, including the accompanying figures, is illustrative and not limiting.

[0533] All patents, patent applications, and other publications, including all sequences disclosed within these references, referred to herein are expressly incorporated herein by reference, to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference. All documents cited are, in relevant part incorporated herein by reference in their entireties for the purposes indicated by the context of their citation herein. However, the citation of any document is not to be construed as an admission that it is prior art with respect to the present disclosure.

Claims

WHAT IS CLAIMED IS:

1. A system configured to identify at least one recognizable feature associated with a health state of a subject, the system comprising: at least one pair of sensors placed symmetrically about an axis of symmetry of the subject on biological compartments of the subject, the at least one pair of sensors being configured to measure a plurality of heat flux measurements of said biological compartments; and a processor configured to receive said plurality of heat flux measurements and recognize said at least one recognizable feature associated with said health state based at least on a relationship between said plurality of heat flux measurements.

2. The system of Claim 1, wherein each of the sensors of the at least one pair of sensors comprises at least one sensor array for substantially simultaneously measuring heat elimination by said subject and an environmental temperature proximal to said subject.

3. The system of Claim 1, wherein the plurality of heat flux measurements comprise a plurality of heat elimination measurements expressed as a function of environmental temperature.

4. The system of Claim 1 further comprising at least one transition state corresponding to the at least one recognizable feature of each plurality of heat flux measurements.

5. The system of Claim 1, wherein the at least one recognizable feature associated with a health state corresponds to a biological response.

6. The system of Claim 5, wherein the at least one recognizable feature has a duration less than one hour.

7. The system of Claim 5, wherein the at least one recognizable feature has a duration of one hour or more.

8. The system of Claim 1 wherein the at least one recognizable feature corresponds to a homeostatic state of the subject.

9. The system of Claim 1, wherein the at least one pair of sensors generates a data stream comprising said plurality of heat flux measurements and at least one work quantification associated with said plurality of heat flux measurements.

10. The system of Claim 9, wherein the at least one work quantification corresponds to an amount of energy a user expends to enter at least one transition state corresponding to the at least one recognizable feature of each data stream.

11. The system of Claim 10, wherein the at least one work quantification further corresponds to an amount of energy the subject expends between a first transition state and a second transition state.

12. The system of Claim 9, wherein said at least one work quantification corresponds to a motion of said subject.

13. The system of Claim 1 further configured to identify at least one first motif from a first plurality of heat flux measurements of a first sensor of the at least one pair of sensors and at least one second motif from a second plurality of heat flux measurements of a second sensor of the at least one pair of sensors.

14. The system of Claim 13, wherein there is at least one transition state between a first motif and a second motif of the at least one first motif and a first motif and a second motif of the at least one second motif.

15. The system of Claim 1 further configured to identify at least one discord between a first plurality of heat flux measurements of a first sensor of the at least one pair of sensors and a second plurality of heat flux measurements of a second sensor of the at least one pair of sensors.

16. The system of Claim 1, wherein one or more recognizable features is indicative of thermal regulation.

17. The system of Claim 16, wherein the one or more recognizable features comprises one or more identifiable patterns, wherein the one or more identifiable patterns is at least one of: a thermal range; and a thermal trend.

18. The system of Claim 17, wherein the one or more identifiable patterns are indicative of a health transition.

19. The system of Claim 1 further configured to identify a difference between a first heat flux measurement of the plurality of heat flux measurements and a second heat flux measurement of the plurality of heat flux measurements.

20. The system of Claim 4, wherein the at least one transition state is indicative of a change in homeostasis of a subject.

21. The system of Claim 1, wherein the processor forecasts a plurality of future plurality of heat flux measurements based on the at least one recognizable feature identified.

22. The system of Claim 1, wherein the processor forecasts a future recognizable feature based on the at least one recognizable feature identified.

23. The system of Claim 1, wherein the processor determines the subject is an anomaly of a population based on the at least one recognizable feature.

24. The system of Claim 23, wherein the population comprises one or more subjects with a health status substantially similar to a health status of the subject.

25. The system of Claim 23, wherein the population comprises one or more subjects with a health transition substantially similar to a health statement of the subject.

26. The system of Claim 1, wherein the processor generates a recommended counter action based on the at least one recognizable feature.

27. A method of identifying at least one recognizable feature associated with a biological response of a subject, the method comprising: measuring heat flux of a first biological compartment of a subject; measuring heat flux at a pair of location about an axis of symmetry of the subject, on biological compartments of the subject, using sensors (i) placed at the pair of locations and (ii) configured to measure a plurality of heat flux measurements of said biological compartments; and generating a data stream for the biological compartments, wherein said data stream includes a plurality of heat flux measurements; and recognizes at least one recognizable feature associated with said biological response based at least on a relationship between said plurality of heat flux measurements.

28. The method of Claim 27, further comprising comparing analysis of the at least one recognizable feature recognized from a first data stream of the generated data streams associated with the biological compartment to the analysis of the at least one recognizable feature recognized from a second data stream of the generated data streams associated with a symmetric biological compartment.

29. A method of assessing resilience of thermoregulation in a subject and generating a recommendation to engage in an intervention as to the subject based on the assessed resilience of thermoregulation, the method comprising: generating a thermoregulation resilience score for the subject over a predetermined time period, wherein the thermoregulation resilience score is based on a comparative analysis between a first data stream comprising a plurality of thermoregulation measurements of a first biological compartment of the subject and a second data stream comprising a plurality of thermoregulation measurements of a second biological compartment of the subject, wherein the first biological compartment and the second biological compartment are symmetric, with respect to an axis of symmetry of the subject; determining whether a need for an intervention as to the subject is above a predetermined threshold of urgency; and generating a recommendation to engage in the intervention as to subject.

30. The method of Claim 29, wherein the pre-determined time period is at least two days.

31. The method of Claim 29, wherein the pre-determined time period is at least one day.

32. The method of Claim 29, wherein the pre-determined time period is approximately one day.

33. The method of Claim 29, wherein the pre-determined time period is less than one day.

34. The method of Claim 29, wherein the pre-determined time period is approximately 8 hours.

35. The method of Claim 29, wherein the thermoregulation resilience score is of a homeostatic robustness of a thermoregulation system of the subject.

36. The method of Claim 35, wherein the homeostatic robustness of the subject is based on the resilience of the thermoregulation system in a transitory phase.

37. The method of Claim 36, wherein an ability of the subject to regulate the thermoregulation system of the subject is based on a health status of the subject.

38. The method of Claim 36, wherein the transitory phase includes a plurality of thermoregulation measurements inconsistent with the plurality of thermoregulation measurements that make up a normal state of the thermoregulation system of the subject.

39. The method of Claim 38, wherein the transitory phase persists for an extended period of time greater than a predetermined period of time.

40. The method of Claim 29, further comprising alerting a medical practitioner that the recommendation has been generated.

41. The method of Claim 39, wherein a non-extended period of time of the transitory phase indicates a resilient thermoregulation system of the subject.

42. The method of Claim 41, wherein the extended period of time of the transitory phase indicates a non-resilient thermoregulation system of the subject.

43. The method of Claim 41, wherein the thermoregulation resilience score generated is greater than a predetermined threshold.

44. The method of Claim 41, wherein the thermoregulation resilience score generated is less than a predetermined threshold.

45. The method of Claim 29, wherein the thermoregulation resilience score falls below a predetermined threshold for a predetermined pre-determined persistence period.

46. The method of Claim 45, further comprising alerting a medical practitioner that a recommendation has been generated.

47. The method of Claim 29, wherein the thermoregulation resilience score correlates with one or more interventions.

48. The method of Claim 29 further comprising analyzing a variation in the plurality of thermoregulation measurements of the subject to determine a characterization of the variation.

49. The method of Claim 48, wherein the plurality of thermoregulation measurements of the biological compartment of the subject and the plurality of thermoregulation measurements of the symmetric biological compartment of the subject includes a plurality of heat flux measurements.

50. The method of Claim 48, wherein the characterization of the variation is a pattern.

51. A method of identifying a subject in need of urgent intervention, the method comprising:measuring a plurality of thermoregulation measurements of a pair of biological compartments of the subject, wherein the pair of biological compartments are substantially symmetric with respect to an axis of symmetry of the subject; generating a data stream for each pair of biological compartments, over a time period, wherein at least one data stream includes a plurality of thermoregulation measurements; comparing analysis of the data stream associated with the pair of biological compartments; generating a score, for the time period, based on the comparative analysis; identifying whether the subject is in urgent need of intervention; and sending an alert relating to the urgent need for intervention.

52. The method of Claim 51, wherein the score is less than a predetermined score.

53. The method of Claim 52, wherein the score indicates a thermoregulation system of the subject is non-resilient.

54. An alert system configured to generate an alert to intervene as to the well-being of a subject, the alert system comprising: at least one pair of sensors, located on a pair of biological compartments that are substantially symmetric with respect to an axis of symmetry of the subject, for generating a plurality of thermoregulation measurements of the subject; a communication means for generating a data stream of thermoregulation measurements of the biological compartments, wherein the data stream includes a plurality of thermoregulation measurements over a pre-determined time period; and a processor configured to receive the data stream and generate a score, associated with the pre-determined time period, based on a resilience measurement.

55. The alert system of Claim 54, wherein the score is a resilient score when the score is greater than a predetermined score threshold.

56. The alert system of Claim 54 further comprising an alarm configured to alert a user to intervene the subject when the score is less than a predetermined score threshold.

57. The alert system of Claim 56, wherein the score is a non-resilient score that indicates the thermoregulatory system of the subject is non-resilient.

58. A method of identifying a subject in need of an intervention and alerting to another relating to the need for intervention, the method comprising: measuring a plurality of thermoregulation measurements of a biological compartment of the subject; substantially simultaneously measuring a plurality of thermoregulation measurements of a symmetric biological compartment of the subject; generating a data stream for each of the biological compartment of the subject and the symmetric biological compartment of the subject, over a time period, wherein each data stream includes a plurality of thermoregulation measurements; comparing analysis of the data stream associated with the biological compartment and the data stream associated with the symmetric biological compartment; generating a score, for the time period, based on the comparative analysis; grouping the score and a plurality of other biological measurements into a data set; identifying whether the subject is in need of intervention based at least in part on the score; and sending an alert relating to the need for intervention.

59. The method of Claim 58, wherein the plurality of other biological measurements includes at least one of: heart rate; blood pressure; or skin temperature.

60. The method of Claim 58, wherein identifying whether the need for intervention is urgent.

61. The method of Claim 58, wherein the subject is in urgent need of intervention is based primarily on the score.

62. A method of identifying a patient having a health risk, the method comprising: measuring a plurality of thermoregulation measurements of a pair of biological compartments of the patient, wherein the pair of biological compartments are substantially symmetric with respect to an axis of symmetry of the patient;generating separate data streams for each of the pair of biological compartments, over a time period, wherein each data stream includes a plurality of thermoregulation measurements; comparing analysis of the data streams associated with the pair of biological compartments; generating a score, for the time period, based on the comparative analysis; grouping the score and a plurality of other biological measurements into a data set; and identifying whether the patient has a health risk.

63. The method of Claim 62, wherein identifying whether the patient has a health risk is used to determine at least one of: medical insurance risk of the patient; dialysis complications of the patient; or heat stroke threshold of the patient.

64. A device configured to measure a plurality of data indicative of at least one emergent factor of a user, the device comprising: a housing; a circuit board located within the housing, the circuit board having a first side of the circuit board opposite a second side of the circuit board; a pair of sensors comprising: a first sensor located on the first side of the circuit board; and a second sensor located on the second side of the circuit board; and at least one additional sensor having a slower equilibration time compared to an equilibration time of the pair of sensors; wherein the first sensor and the second sensor are decoupled.

65. The device of Claim 64, wherein the at least one additional sensor is configured to measure an internal heat source of the device.

66. The device of Claim 65, wherein a value is calculated based on the at least one additional sensor that is representative of a counterfactual gap.

67. The device of Claim 65, wherein the at least one additional sensor is placed in between the pair of sensors.

68. The device of Claim 67, wherein the at least one additional sensor includes a series of sensors placed in between the pair of sensors.

69. The device of Claim 68, wherein the first sensor is a heat sensor and the second sensor is a heat sensor.

70. The device of Claim 69, wherein the series of sensors is configured to measure a gradient of temperatures across the device.

71. The device of Claim 70, wherein a value is calculated based on the series of sensors that is representative of a counterfactual gap.

72. The device of Claim 71, wherein the gradient of temperatures minimizes the counterfactual gap.

73. The device of Claim 72, wherein the first sensor and the second sensor are configured to measure data indicative of the at least one emergent factor of the user.

74. The device of Claim 64, further comprising a processor and a firmware.

75. A method of measuring a plurality of data indicative of at least one emergent factor of a user, the method comprising: measuring heat flux at a pair of sensors; measuring a temperature of an internal heat source; generating a value, based on an additional sensor measuring the temperature of the internal heat source, representative of a counterfactual gap; and generating a data stream for the pair of sensors, wherein the data stream includes a plurality of heat flux measurements minus the counterfactual gap.

76. The method of Claim 75, wherein the pair of sensors includes a first sensor and a second sensor.

77. The method of Claim 76, wherein the first sensor and the second sensor are decoupled.

78. The method of Claim 77, wherein the first sensor measures ambient temperature of the user and the second sensor measures skin temperature of the user.

79. The method of Claim 75, wherein the additional sensor measures the temperature of the internal heat source as a function of time.

80. The method of claim 79, wherein the additional sensor measures temperature with a slower equilibration time compared to an equilibration time of the heat flux measured at the pair of sensors.

81. The method of Claim 80, further comprising: estimating heat elimination of a biological system over time based heat flux; estimating heat production of the biological system over time based heat flux; and estimating basal metabolic status of the biological system based on temporal alignment of heat elimination and heat production.

82. The method of Claim 80, further comprising: obtaining a quasiperiodic rhythm of a biological system based on heat flux, wherein the quasiperiodic rhythm is of seconds-timescale, of minutes-timescale, ultradian, circadian, circalunar, or of yearly timescale.

83. The method of claim 82, further comprising: obtaining a variability of the quasiperiodic rhythm across a predetermined amount of time; and determining a health capacity based on the variability of the quasiperiodic rhythm.

84. The method of claim 82, further comprising: estimating heat elimination of a biological system over time based on heat flux; estimating heat production of the biological system over time based on heat flux; estimating a basal metabolic status of the biological system based on temporal alignment of heat elimination and heat production; and determining a health capacity by applying a time-dependent function to the estimated basal metabolic status, wherein the time-dependent function is derived from the quasiperiodic rhythm of the biological system.

85. The method of Claim 80, wherein: the plurality of data is heat flux data; at least one health capacity is a basal metabolic status; andat least one emergent factor is a temporal alignment of heat production and heat elimination.

86. The method of Claim 85, wherein the temporal alignment is related to at least one quasiperiodic rhythm of a biological system.

87. The method of Claim 86, wherein the at least one quasiperiodic rhythm is a circadian rhythm.

88. A device configured to measure a plurality of data indicative of at least one emergent factor of a user, the device comprising: a housing; a circuit board located within the housing, the circuit board having a first side of the circuit board opposite a second side of the circuit board; a first sensor located on the first side of the circuit board; a second sensor located on the second side of the circuit board; at least one additional sensor having a slower equilibration time compared to an equilibration time of the first sensor and the second sensor; and a battery positioned between the first sensor and the second sensor, wherein a first isolation material thermally decouples the first sensor and / or the second sensor from the circuit board and the battery.

89. The device of Claim 88, wherein the first sensor is coupled to a first thermal window.

90. The device of Claim 89, wherein the second sensor is coupled to a second thermal window.

91. The device of Claim 90, wherein the first thermal window and the second thermal window are decoupled.

92. The device of Claim 91, wherein the first thermal window contacts the user and the second thermal window contacts ambient air.

93. The device of Claim 92, wherein the at least one additional sensor is located between the first sensor and the second sensor.

94. The device of Claim 93, wherein the at least one additional sensor includes a series of sensors placed in between the first sensor and the second sensor.

95. The device of Claim 94, wherein the first sensor is a heat sensor and the second sensor is a heat sensor.

96. The device of Claim 94, wherein the first sensor and the second sensor are thermally decoupled.

97. The device of Claim 96, wherein the series of sensors is configured to measure a gradient of temperatures across the device.

98. The device of Claim 97, wherein a value is calculated based on the series of sensors that is representative of a counterfactual gap.

99. The device of Claim 98, wherein the gradient of temperatures minimizes the counterfactual gap.

100. The device of Claim 99, wherein the first sensor measures skin temperature.

101. The device of Claim 100, wherein the second sensor measures ambient temperature.

102. The device of Claim 101, wherein the first sensor and the second sensor are configured to measure data indicative of the at least one emergent factor of the user.

103. The device of Claim 88, further comprising a processor and a firmware.

104. A system configured to identify at least one recognizable feature associated with a health state of a subject, the system comprising: at least one pair of sensors, the at least one pair of sensors being configured to measure a plurality of heat flux measurements of the subject; at least one additional sensor having a slower equilibration time compared to an equilibration time of the at least one pair of sensors; and a processor configured to receive said plurality of heat flux measurements and recognize said at least one recognizable feature associated with said health state based at least on a relationship between said plurality of heat flux measurements.

105. The system of Claim 104, wherein the at least one additional sensor is configured to measure an internal heat source of the system.

106. The system of Claim 105, wherein a value is calculated based on the at least one additional sensor that is representative of a counterfactual gap.

107. The system of Claim 106, wherein the at least one additional sensor is placed in between the at least one pair of sensors.

108. The system of Claim 107, wherein the at least one additional sensor includes a series of sensors placed in between the at least one pair of sensors.

109. The system of Claim 108, wherein the series of sensors is configured to measure a gradient of temperatures across the system.

110. The system of Claim 109, wherein a value is calculated based on the series of sensors that is representative of a counterfactual gap.

111. The system of Claim 110, wherein the gradient of temperatures minimizes the counterfactual gap.

112. The system of Claim 104, wherein the at least one pair of sensors is placed symmetrically about an axis of symmetry of the subject on biological compartments of the subject.

113. A method of identifying at least one recognizable feature associated with a biological response of a subject, the method comprising: measuring heat flux of a biological compartment of a subject using a pair of sensors configured to measure a plurality of heat flux measurements of the biological compartment; measuring a temperature of an internal heat source; generating a value, based on an additional sensor measuring the temperature of the internal heat source, representative of a counterfactual gap; and generating a data stream for the biological compartment, wherein said data stream includes a plurality of heat flux measurements minus the counterfactual gap; and recognizes at least one recognizable feature associated with said biological response based at least on a relationship between said plurality of heat flux measurements.

114. The method of claim 113, wherein the temperature of the internal heat source is measured as a function of time.

115. The method of claim 114, wherein the temperature of the internal heat source is measured with a slower equilibration time compared to an equilibration time of the heat flux measured at the pair of locations.

116. A method of determining a representative value of a counterfactual measurement, the method comprising:measuring heat flux of a biological compartment of a subject using a pair of sensors configured to measure a plurality of heat flux measurements of said biological compartment; measuring a temperature of an internal heat source; generating a value, based on an additional sensor measuring the temperature of the internal heat source, representative of a counterfactual gap; and generating the representative value of the counterfactual measurement.

117. The method of claim 116, wherein the temperature of the internal heat source is measured as a function of time.

118. The method of claim 117, wherein the temperature of the internal heat source is measured with a slower equilibration time compared to an equilibration time of the heat flux measured at the pair of locations.

119. A system configured to determine at least one health metric of a subject, the system comprising: at least one sensor for measuring at least one physiologic metric corresponding to a patterned stress experienced by the subject and generating a data stream therefrom, wherein the data stream includes a plurality of biometric measurements; and a processor configured to receive the data stream and determine a health capacity of the subject.

120. The system of Claim 119, wherein the patterned stress is a known stress.

121. The system of Claim 120, wherein the known stress is a physical activity performed by the subject.

122. The system of Claim 119, wherein the patterned stress arises from physical exertion by the subject.

123. The system of Claim 120, wherein a pattern of the patterned stress is based on a habit, distance or a time.

124. The system of Claim 123, wherein the time is an interval of time.

125. The system of Claim 123, wherein the distance is a fixed distance.

126. The system of Claim 123, wherein the habit is daily, weekly, monthly, or yearly.

127. The system of Claim 119, wherein the physiologic metric cycles with the patterned stress.

128. The system of Claim 127, wherein the physiologic metric includes any one of the following: heart rate; carbon dioxide levels; oxygen saturation; work; temperature; power; or heat flux.

129. The system of Claim 128, wherein the physiologic metric is related to the health capacity of the subject.

130. The system of Claim 119, wherein the processor is configured to analyze the data stream.

131. A method of determining at least one health metric of a subject, the method comprising: measuring at least one physiologic metric corresponding to a patterned stress experienced by the subject; generating a data stream that includes the at least one physiologic metric of the subject; receiving the data stream at a processor; and determining a health capacity of the subject.

132. The method of Claim 131, wherein the patterned stress is a known stress.

133. The method of Claim 132, wherein the known stress is a physical activity performed by the subject.

134. The method of Claim 131, wherein the patterned stress arises from physical exertion by the subject.

135. The method of Claim 132, wherein a pattern of the patterned stress is based on a habit, distance or a time.

136. The method of Claim 135, wherein the time is an interval of time.

137. The method of Claim 135, wherein the distance is a fixed distance.

138. The method of Claim 135, wherein the habit is daily, weekly, monthly, or yearly.

139. The method of Claim 132, wherein the physiologic metric cycles with the patterned stress.

140. The method of Claim 139, wherein the physiologic metric includes any one of the following: heart rate; carbon dioxide levels; oxygen saturation; work; temperature; power; or heat flux.

141. The method of Claim 140, wherein the physiologic metric is related to the health capacity of the subject.

142. The method of Claim 131, further comprising analyzing the data stream.

143. The method of Claim 142, further comprising generating a recommendation to improve the health capacity of the subject by performing an action as to the subject.

144. A method of detecting a health risk, comprising: obtaining a data pattern for a subject indicative of a first health state of the subject; transmitting, based on the data pattern, subject-related information to a health services provider; receiving an indicia of a health outcome for the subject; updating a model, designed to identify health risks based on data patterns, by training the model based at least in part on the received indicia of a health outcome, wherein the training comprises correlating the data pattern with the health outcome; and using the updated model to detect a health risk associated with the health outcome.

145. The method of claim 144, wherein the data pattern is a heat flux pattern based on a plurality of heat flux measurements of the subject.

146. The method of claim 145, wherein the heat flux pattern is based, at least in part, on an categorical analysis of heat flux measurements of the subject.

147. The method of claim 145, wherein the heat flux pattern is based, at least in part, on a time series analysis of a plurality of heat flux measurements of the subject.

148. The method of claim 144, wherein health outcome is selected from the group consisting of (i) a health state of the subject, (ii) a health service applicable to the subject, and (iii) a second heat flux pattern of the subject.

149. The method of claim 144, wherein the indicia of a health outcome is obtained from the heath service provider.

150. The method of claim 149, wherein the indicia of a health outcome is a CPT code.

151. The method of claim 150, wherein the CPT code is associated with a diagnosis of a second health state.

152. The method of claim 150, wherein the CPT code is associated with treatment of a second health state.

153. The method of claim 151 or claim 152, wherein the first health state and the second health state are the same.

154. The method of claim 151 or claim 152, wherein the first health state and the second health state are different.

155. The method of claim 144, the indicia of a health outcome is a second heat flux pattern for the subject that is indicative of a second health state of the subject.

156. The method of claim 155, wherein the first health state and the second health state are the same.

157. The method of claim 155, wherein the first health state and the second health state are different.

158. The method of claim 144, wherein the health service provider is selected from the group comprising (i) a call center, (ii) a medical services provider, (iii) a pharmaceuticals provider, (iv) a nutrition products merchant, (v) a consumables merchant, and (vi) an exercise services provider.

159. The method of claim 144, wherein the health service is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical therapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

160. A method of improving the ability to detect a health risk, comprising: obtaining a heat flux pattern for a subject indicative of a first health state of the subject; transmitting, based on the heat flux pattern, subject-related information to a health services provider; receiving an indicia of a health outcome for the subject; improving a model, designed to identify health risks based on data patterns, by training the model based at least in part on the received indicia of a health outcome, wherein the training comprises correlating the heat flux pattern with the health outcome; and using the improved model to detect a health risk associated with the health outcome.

161. The method of claim 160, wherein the health services provider is a call center.

162. The method of claim 161, wherein the health outcome relates to operational data applicable to the call center.

163. The method of claim 162, wherein the operational data relates to the duration of a phone call between the subject and the call center.

164. The method of claim 163, wherein the operational data relate to the success rate of attempts by the call center to engage in a call with the subject.

165. The method of claim 160, wherein the health services provider is a medical services provider.

166. The method of claim 165, wherein the health outcome relates to operational data applicable to the health service provider.

167. The method of claim 166, wherein the operational data relates to a length of the subject’s visit to the heath service provider.

168. The method of claim 144, further comprising generating a recommendation to engage in an intervention or an alert regarding a health risk for the subject, based on the trained model.

169. The method of claim 168, wherein the health risk for the subject is a probabilistic measure.

170. The method of claim 144, wherein the health service provider is selected from the group comprising a nutrition products merchant, a consumables merchant, and an exercise services provider.

171. The method of claim 170, further comprising a step of recommending products or services to the subject.

172. The method of claim 170, further comprising a step of receiving a percentage of revenue generated by the health service provider attributable to the recommended products or services purchased made by the subject.

173. The method of either claim 144 or claim 160, wherein the model is a linear regression model or a deep learning model.

174. The method of either claim 144 of claim 160, further comprising a step of generating a recommendation to engage in an intervention regarding the health risk.

175. A method of lowering a health risk, comprising: obtaining a heat flux data pattern for a subject indicative of a first health state of the subject; transmitting, based on the heat flux data pattern, subject-related information to a health services provider; receiving an indicia of a health risk for the subject; improving a model, designed to identify health risks based on heat flux patterns, by training the model based at least in part on the received indicia of a health risk, wherein the training comprises correlating the heat flux data pattern with the health risk; and using the improved model to detect of the health risk; recommending, for the subject, an intervention to lower the health risk.

176. The method of claim 175, wherein the intervention is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical therapy,(iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

177. The method of claim 175 or claim 176, wherein the recommendation is implemented, thereby lowering the health risk.

178. The method of claim 144 or claim 160, wherein the indicia of a health outcome is based on subject-related information.

179. The method of claim 175, wherein the indicia of a health risk is based on subject-related information.

180. A method of monitoring fetal-maternal well-being, comprising: obtaining a heat flux pattern indicative of a health state of a pregnant woman over time; identifying a heat flux signature of the pregnant woman over time based on the heat flux pattern; and monitoring the wellbeing of the fetus and the pregnant woman based on the heat flux signature over time.

181. The method of claim 180, wherein the heat flux pattern is obtained by a plurality of heat flux measurements on the pregnant woman.

182. The method of claim 180, wherein the heat flux pattern is measured by a wearable device worn by the pregnant woman.

183. A method of monitoring a subject’s compliance to a treatment, comprising: obtaining a heat flux pattern indicative of a health state of the subject over time; identifying a heat flux signature of the subject over time based on the heat flux pattern; and determining whether the subject is compliant to the treatment by detecting a change in the heat flux signature consistent with administration of the treatment.

184. The method of claim 183, wherein the heat flux pattern is obtained by a plurality of heat flux measurements on the subject.

185. The method of claim 183, wherein the heat flux pattern is measured by a wearable device worn by the subject.

186. The method of claim 183, wherein the subject’s compliance to the treatment is monitored periodically.

187. The method of claim 183, wherein the treatment is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical therapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

188. A method of monitoring treatment effect, comprising: administering a first treatment to a subject while obtaining a heat flux pattern indicative of a health state of the subject over time; identifying a heat flux signature of the subject over time based on the heat flux pattern; and monitoring the effect of the first treatment by comparing the heat flux signature before and after the administration of the first treatment.

189. The method of claim 188 , wherein the heat flux pattern is obtained by a plurality of heat flux measurements on the subject.

190. The method of claim 188, wherein the heat flux pattern is measured by a wearable device worn by the subject.

191. The method of claim 188, wherein the effect of the first treatment is monitored over time.

192. The method of claim 191, further comprising determining pharmacokinetics / pharmacodynamics of the first treatment based on the effect of the first treatment monitored over time.

193. The method of claim 188, further comprising determining complication of the first treatment based on the heat flux pattern.

194. The method of claim 188, further comprising administering a second treatment to the subject at a later time compared to the administration of the first treatment, and monitoring the effect of the combined treatments after the administration of the second treatment.

195. The method of claim 194, further comprising determining the interaction between the first treatment and the second treatment based on the monitored effect of the combined treatments.

196. The method of claim 194, wherein the second treatment is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical-MO-therapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

197. The method of claim 188, wherein the first treatment is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical therapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

198. A method of conducting a clinical trial, comprising: providing a treatment to a subject; obtaining a heat flux pattern indicative of a health state of the subject over time; identifying a heat flux signature of the subject over time based on the heat flux pattern; and monitoring the effect of the treatment based on how the heat flux signature changes as a function of time.

199. The method of claim 198, wherein the heat flux pattern is obtained by a plurality of heat flux measurements on the subject.

200. The method of claim 198, wherein the heat flux pattern is measured by a wearable device worn by the subject.

201. The method of claim 198, wherein the treatment is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical therapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

202. A method of conducting a clinical trial, comprising: providing a wearable device to a subject; measuring a heat flux pattern of the subject over time by the wearable device worn by the subject; identifying a heat flux signature of the subject over time based on the heat flux pattern; and monitoring the effect of a treatment based on how the heat flux signature changes after administering the treatment to the subject.

203. The method of claim 202, wherein the effect of the treatment is monitored over time.

204. The method of claim 203, further comprising determining pharmacokinetics / pharmacodynamics of the treatment based on the effect of the treatment monitored over time.

205. The method of claim 202, wherein the treatment is selected from the group consisting of (i) a surgical treatment, (ii) a pharmaceutical regimen, (iii) physical therapy, (iv) an exercise regimen, (v) a dietary regimen, (vi) a sleep schedule, (vii) a lifestyle change, and (viii) a change of environmental conditions.

206. A system configured to detect a sleep disorder, the system comprising: at least one pair of sensors, the at least one pair of sensors configured to measure a plurality of heat flux measurements of a subject; and a processor configured to receive said plurality of heat flux measurements and recognize at least one recognizable feature associated with said sleep disorder based on at least one relationship between said plurality of heat flux measurements.

207. The system of claim 206, wherein the at least one pair of sensors is configured to measure the plurality of heat flux measurements of the subject taken throughout a circadian period.

208. The system of claim 207, wherein the processor is further configured to determine an efficacy of a treatment of the sleep disorder provided to said subject.

209. The system of claim 206, wherein the sleep disorder is sleep apnea.

210. The system of claim 206, wherein the sleep disorder is insomnia.

211. A system configured to treat a sleep disorder, the system comprising: at least one pair of sensors, the at least one pair of sensors is configured to measure a plurality of heat flux measurements of a subject over a period of time; and a processor configured to receive said plurality of heat flux measurements and generate instructions based on the plurality of heat flux measurements, said instructions for configuring a ventilation device for treatment of said subject.

212. The system of claim 211, further comprising a ventilation device operatively connected to the processor, wherein the ventilation device is configured to (i) received instructions from the processor and (ii) provide treatment to the subject.

213. The system of claim 211, wherein the period of time is wakefulness.

214. The system of claim 211, wherein the period of time is asleep.

215. The system of claim 211, wherein the processor is further configured to determine an efficacy of a treatment of the sleep disorder provided to said subject.

216. The system of claim 215, wherein the processor is further configured to recognize at least one recognizable feature associated with said sleep disorder based at least on a relationship between said plurality of heat flux measurements.

217. The system of claim 216, wherein the sleep disorder is sleep apnea.

218. The system of claim 216, wherein the sleep disorder is insomnia.

219. The system of claim 211, wherein the ventilation device is a CPAP machine.

220. The system of claim 211, wherein the ventilation device is a BiPAP machine.

221. A system configured to modify a treatment modality of a sleep disorder, the system comprising: a device configured to provide treatment to a subject over a period of time; at least one pair of sensors, the at least one pair of sensors is configured to measure a plurality of heat flux measurements of the subject over the period of time; and a processor configured to dynamically receive said plurality of heat flux measurements and generate instructions based on the plurality of heat flux measurements; and a ventilation device operatively connected to the processor, the ventilation device configured to receive instructions from the processor and provide treatment to a subject over a period of time.

222. The system of claim 221, wherein the processor communicates with the device to modify the treatment provided to the subject based on the instructions.

223. The system of claim 221, wherein the processor is further configured recognize at least one recognizable feature associated with said sleep disorder based at least on a relationship between said plurality of heat flux measurements.

224. The system of claim 221, wherein the period of time is wakefulness.

225. The system of claim 221, wherein the period of time is asleep.

226. The system of claim 221, wherein the sleep disorder is sleep apnea.

227. The system of claim 221, wherein the sleep disorder is insomnia.

228. A system configured to identify at least one subject afflicted with a sleep disorder, the system comprising: at least one pair of sensors, the at least one pair of sensors is configured to measure a plurality of heat flux measurements of a subject over a period of time; and a processor configured to receive said plurality of heat flux measurements and recognize at least one recognizable feature associated with said sleep disorder based at least on a relationship between said plurality of heat flux measurements.

229. The system of claim 228 wherein the at least one pair of sensors includes at least two skin temperature sensors placed axially symmetric on the subject.

230. The system of claim 228 or 229, wherein the sleep disorder is sleep apnea.

231. The system of claim 228 or 229, wherein the sleep disorder is insomnia.

232. A system configured to determine a level of compliance of a subject, wherein the subject is provided a treatment for a sleep disorder, the system comprising: at least one pair of sensors, the at least one pair of sensors configured to measure a plurality of heat flux measurements of the subject over a period of time; a processor configured to receive said plurality of heat flux measurements and determine the level of compliance; and a ventilation device operatively connected to the processor.

233. The system of claim 232, wherein the level of compliance is based on a subject- by-subject basis.

234. The system of claim 233, wherein compliance is based on use of the ventilation device.

235. The system of claim 234, wherein the ventilation device is a CPAP machine.

236. The system of claim 234, wherein the ventilation device is a BiPAP machine.

237. A method for effectively treating a systemic disorder, the method comprising: measuring heat flux of a biological compartment of a subject using a pair of sensors configured to measure a plurality of heat flux measurements of the biological compartment; receiving the plurality of heat flux measurements at a processor; generating instructions based on the plurality of heat flux measurements; transmitting instructions to a ventilation device; andproviding a treatment to the subject based on the instructions.

238. A method for determining a level of compliance of a subject, wherein the subject is provided a treatment for a sleep disorder, the method comprising: measuring heat flux of a biological compartment of a subject using a pair of sensors configured to measure a plurality of heat flux measurements of the biological compartment; receiving the plurality of heat flux measurements at a processor; and determining the level of compliance of the subject.

239. The method of claim 238, wherein the level of compliance is based on a subject- by-subject basis.

240. The method of claim 239, wherein compliance is based on use of a ventilation device.

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