Sweat sensor data analysis

Abstract

A system and method for physiological analysis and feedback includes multiple sensors worn on the user's skin and configured to measure sweat data in real time, indicating the levels of several different sweat components, and one or more processors configured to receive the sweat data in real time. The processor applies the sweat data to a biomarker correlation model trained to correlate an input, which includes several sweat components along with user-specific data, including age, weight, and sex, with an output, which includes several physiological parameters. Two of the several physiological parameters may be oxygen consumption (VO2) and carbon dioxide emissions (VCO2). Additionally, the risk of coronary artery occlusion can be calculated by applying the calculated VO2 levels to a cardiac disease risk correlation model trained to correlate VO2 levels with levels of coronary artery occlusion risk.

Description

Technical Field

[0001] The present invention generally relates to the field of external measurement of body fluids or tissues and related medical conditions.

Background Art

[0002] The popularity of wearable biosensor devices has been continuously expanding because these devices have come to play a major role in the medical and sports monitoring industries. These fitness tracking devices are provided in many shapes and forms. They can measure heart rate, the number of steps taken, and sleep parameters. They can also connect data to applications for goal setting, diet tracking, and other health issues.

[0003] Many sports players use wearable technology to optimize their performance and collect data on their training. Additionally, people on a diet use such devices to track their energy consumption (e.g., calories burned) as well as calorie intake. However, while wearable technology has existed for nearly 10 years and has made great progress in the past five years, today's range of fitness trackers only provides basic and elementary biological data.

[0004] In many cases, such devices are designed to be linked to a smartphone or incorporate an internal app, such as a smartwatch application, that provides the user with health and fitness-related feedback. Health and fitness tracking devices can monitor basic physiological parameters such as heart rate, number of steps, sleep stages, etc.

[0005] U.S. Patent Publication 2018 / 0263539 to Javey et al. describes how sodium [Na+] in sweat "may potentially serve as an important biomarker for dehydration monitoring." According to Javey, a wearable device "may be configured to determine from sensor measurements whether the wearer is dehydrated, hyponatremia, hypokalemia, muscle cramps, ischemia, and / or pressure ulcers, and may provide corresponding alerts and reports."

[0006] Akutagawa and Myslinski's U.S. Patent No. 11,030,708, titled "Method of and device for implementing contagious illness analysis and tracking," describes a wearable device for sensing "biomarkers (e.g., cytokines such as interleukin-6)" to obtain signs of "stress, disease, malnutrition, injury, infection, cancer, and other conditions."

[0007] High serum potassium levels are associated with a high risk of many diseases and physiological impairments, and potassium levels are known to fluctuate during unstable cardiovascular conditions. Another physiological parameter with significant health implications is oxygen consumption (VO2), also known as oxygen uptake, particularly peak VO2 during physical activity, such as training exercise. As described in the paper by Malhotra et al., "Cardiopulmonary Exercise Testing in Heart Failure" (JACC: Heart Failure, Vol. 4, no. 8, 2016), VO2 measured during cardiopulmonary exercise testing (CPET) indicates functional capacity. The parameters of carbon dioxide output (VCO2) and ventilation (VE), along with VO2, can also indicate maladaptive responses during exercise. [Overview of the project]

[0008] Embodiments of the present invention provide a system and method for generating user health measurements. Providing users with real-time feedback of physiological parameters during exercise can improve user wellness and reduce health risks.

[0009] For a better understanding of the various embodiments of the present invention and to show how they can be put into practice, the accompanying drawings are referenced as examples. The structural details of the present invention are shown to provide a basic understanding of the invention, and the description taken together with the drawings will make it clear to those skilled in the art how some forms of the present invention can actually be embodied. [Brief explanation of the drawing]

[0010] [Figure 1] This is a schematic diagram of a system for generating and reporting user health measurements according to an embodiment of the present invention.

[0011] [Figure 2] This is a block diagram of the components of a system that implements a process for generating and reporting user health measurements according to an embodiment of the present invention.

[0012] [Figure 3] This is a flowchart of a process for generating and reporting user health measurements according to an embodiment of the present invention.

[0013] [Figure 4] This is a schematic diagram of a neural network model that correlates sensor data with physiological parameters according to an embodiment of the present invention.

[0014] [Figure 5] This graph, according to an embodiment of the present invention, shows the correlation between sensor data of potassium electrolytes in sweat and oxygen consumption (VO2) over time.

[0015] [Figure 6] This graph shows the oxygen consumption (VO2) during exercise over a period of multiple breaths by the user, with each graph showing the measured VO2 and the VO2 predicted by the biomarker correlation model. [Modes for carrying out the invention]

[0016] Embodiments of the present invention provide a system and method for generating and reporting user health measurements. These user health measurements can also be applied to generate wellness recommendations.

[0017] Figure 1 is a schematic diagram of a system 100 for generating and reporting user health measurements according to an embodiment of the present invention. The system 100 includes a wearable sensor device 102, which may include a skin sensor for measuring the composition of sweat, as well as additional sensors that may be both sweat and non-sweat sensors. The sensor device 102 may be configured as a plurality of wearable devices, which may be wearable as an armband, headband, chest strap, ring, or headphones, i.e., at one or more positions on the user's body that are exposed to skin sweating.

[0018] The sensor of the sensor device 102 includes at least one sensor for measuring the electrolyte component of sweat, which is either potassium (i.e., K+) or sodium (i.e., Na+), or both. That is, the sensor of device 102 may include electrolyte sensors for both potassium and sodium, and may include one or more additional electrolyte sensors, such as sensors for magnesium, chloride, ammonia, and calcium.

[0019] The sensors of device 102 may also include sensors for detecting levels of metabolites, such as glucose, lactate, ethanol, and uric acid. Additionally or alternatively, device 102 may include sensors for detecting additional sweat components, such as zinc, caffeine, levodopa, zinc, copper, cortisol, neuropeptides, interleukin-6 (IL-6), tyrosine, cytokines, hormones, or hormone precursors. Device 102 may also include at least one non-sweat sensor, such as a heart rate sensor, a body temperature sensor, a pH sensor, an accelerometer, a gyroscope, and a blood glucose monitor.

[0020] As further described below in this specification, it is incorporated into the sensor device 102 or the mobile computing device 104 (this is, A processor, which may be incorporated into a device (for example, a smartphone or a smartwatch), receives signals generated by sensors in device 102 and processes the signals to determine the levels of sweat components indicated by the signals, as well as physiological parameters that correlate with the signals. In some embodiments, the processor is also configured to present indicators of physiological parameters to the user, typically on the screen of a mobile computing device 104. Alternatively or additionally, the processor may transmit the physiological parameters to a remote server 106 to record a history of the user's data for further processing, for example, to determine trend information that can be correlated with further health parameters, including wellness parameters. In further embodiments, biomarker correlation models and wellness correlation models, which are further described below herein, are implemented by the server, the biomarker correlation model is trained to correlate sensor data ("input" data) with physiological parameters, and the wellness correlation model is trained to correlate physiological parameters with wellness guidelines.

[0021] Figure 2 is a block diagram of system 100, showing the components of sensor device 102 and mobile computing device 104. Sensor device 102 includes a sweat sensor 202 for measuring components of a user's sweat, such as potassium and sodium. Additional sweat sensors 202 may include electrolyte sensors for, for example, magnesium, chloride, ammonia, and calcium. The electrolyte sensors may be configured for a particular electrolyte and may include any one or more of an electrochemical sensor, an ion-selective sensor, a membrane sensor, a conductivity sensor, a galvanic skin response (GSR) sensor, and a potentiometric ion sensor.

[0022] The sensors of device 102 may also include sensors for detecting levels of metabolites, such as glucose, lactate, ethanol, and uric acid. Additionally or alternatively, device 102 may include sensors for detecting additional sweat components, such as, for example, zinc, caffeine, levodopa, zinc, copper, cortisol, neuropeptides, interleukin 6 (IL-6), tyrosine, or cytokines.

[0023] Device 102 may also include at least one non-sweat sensor 204, such as, for example, a body temperature sensor, a pH sensor, an accelerometer, a gyroscope, and a blood glucose monitor.

[0024] Signals from the sensors are typically generated as analog voltages and may be acquired in sensor processor 206 and transmitted to mobile computing device 104 using transmission protocols known in the art, such as, for example, Bluetooth or radio frequency identification (RFID) transmission protocols.

[0025] In some embodiments, the sensor processor 206 may also execute applications stored in the processor memory, such as the biomarker correlation model 220a and the wellness correlation model 230a. Alternatively, these applications may be executed by the mobile device processor 210, and these applications in alternative implementations are referred to as the biomarker correlation model 220b and the wellness correlation model 230b (hereinafter, the biomarker correlation model in alternative implementations will be referred to as the biomarker correlation model 220a / b, and alternative means of the wellness correlation model will be referred to as the wellness correlation model 230a / b).

[0026] The biomarker correlation model 220a / b can be trained using multiple data instances to correlate input data with an “output” that typically includes multiple physiological parameters. The input data may include signals from sweat sensors, which indicate the levels of each sweat component. The input data may also include signals from non-sweating sensors, which indicate the levels of their respective biomarkers. The input data may also include user-specific data 222a / b, such as the user’s age, weight, sex, and height. The system may also be configured to continuously update the biomarker correlation model as new user-specific data is acquired. Training the biomarker correlation model involves: This can be based on known machine learning (i.e., artificial intelligence) methods. The model can also provide real-time anomaly detection and be trained to identify anomalous physiological parameters and notify the user.

[0027] The input data is typically correlated by a biomarker correlation model with at least two physiological parameters that are particularly relevant during exercise training, and these two physiological parameters are measurements of oxygen consumption (VO2) and carbon dioxide emissions (VCO2). Additional physiological parameters that can be correlated by a biomarker correlation model may include: calories burned, carbohydrates burned, fats and proteins, blood potassium concentration, sodium loss in the body, lactate accumulation, blood glucose concentration, blood insulin concentration, bone health, stress levels, likelihood of infection (e.g., COVID-19), likelihood of kidney stones, likelihood of type 2 diabetes, likelihood of congestive heart failure, risk of myocardial infarction, risk of hypertension, risk of stroke, likelihood of adrenal insufficiency, heart rate variability (HRV), and risk of liver disease.

[0028] The sensor processor may also be configured to calculate the respiratory exchange ratio (RER) level from the VCO2 / VO2 ratio. In a further embodiment, a subset of biomarker correlation models is a cardiac disease risk correlation model trained to correlate RER and VO2 levels with levels of coronary occlusion risk. The processor may apply VCO2 and VO2 values ​​to calculate the RER, which is then applied to the cardiac disease risk correlation model along with the VO2 value (i.e., level) to estimate the risk of coronary occlusion.

[0029] Wellness correlation models 230a / b, also referred to herein as wellness models, are typically trained to correlate physiological parameters with health and wellness guidelines. Correlated health and wellness guidelines may include, for example, warning alerts. Alerts may indicate that certain physiological parameters predicted by the biomarker correlation model indicate that the user is at risk. Alerts may also provide recommendations regarding actions the user should take immediately, which may include measures such as reducing activity levels, resting, consuming carbohydrates, and / or drinking water. Health and wellness guidelines may also include exercise recommendations for users to change their workout type and / or exercise form. Health and wellness guidelines may also include nutritional recommendations, such as foods to consume during current training exercises, foods to consume before and / or after specific exercises, and / or recommended calorie intakes.

[0030] After correlated health and wellness guidelines are determined, they can be transmitted to the user interface as further indicators of the user's physiological parameters. Similar to biomarker models, inputs to wellness models may also include accumulated data, such as trend data, which can be stored on a mobile computing device or on a server (i.e., a remote computing device such as a cloud computing device). In some embodiments, the wellness model is implemented on a server, which receives inputs of estimated physiological parameters from the mobile computing device.

[0031] As described above, the user may use the sensor device during exercise with sensor measurements being taken in real time so that the user can receive real-time feedback. In the mobile computing device 104, transmissions from the sensor device, which are typically wireless, may be received by the mobile device processor 210. If the sensor processor 206 is not configured to run the application described above, the sensor processor may simply send sensor data to the mobile device processor, and the application (i.e., the model) may be executed on the mobile device processor.

[0032] Typically, a mobile device processor transmits one or more indicators of values ​​for several physiological parameters to a user interface, such as a computing device screen 224. The presentation of physiological parameters on the screen, also referred to herein as “user feedback,” is performed in real time, i.e., while sensor data is being received. Hereinafter, “real-time feedback” is understood to mean feedback at intervals of up to 15 minutes.

[0033] Physiological parameters presented as user feedback may also include calculated levels of RER. These physiological parameters may also be stored in memory storage, for example, on a mobile computing device or a remote server, for subsequent analysis and reporting to the user. User-specific data 222a / b may also typically be stored in such memory storage and entered by the user from the computing device screen 224, or accessed by the mobile computing device from a remote source such as a remote server 106. The mobile computing device 104 also typically includes a data storage module for securely storing and managing user-specific data and physiological measurements. In addition, data encryption and secure communication protocols are typically employed to protect user privacy and data integrity.

[0034] Figure 3 is a flowchart of the main steps of a process 300 for generating and reporting user health measurements according to an embodiment of the present invention. In the first step 312, a biomarker correlation model is trained with training data to correlate "input" data with physiological parameters. The input data includes sweat sensor data, user-specific data, and optionally non-sweat sensor data.

[0035] In operation, typically while the user is exercising, the mobile computing device acquires data from sweat sensors and optionally from non-sweat sensors in step 314. Then, in step 316, the acquired data, along with stored user-specific data, is applied to a biomarker correlation model to determine (i.e., predict / estimate) the values ​​of physiological parameters. As described above, the physiological parameters may include many biomarkers, but typically include at least the levels of VCO2 and VO2.

[0036] In step 318, the processor may selectively correlate physiological parameters with wellness guidelines, as described above.

[0037] In step 320, the processor then transmits an index of physiological parameters, including optionally predicted wellness guidelines, to the user interface to provide real-time user feedback. Figure 4 is a schematic diagram of a biomarker correlation model 400 configured as a neural network. The model correlates sensor data with physiological parameters according to embodiments of the present invention. Embodiments of the present invention include multiple correlation models as described above, which may be configured by methods known in the art, for example, by a neural network or a random forest algorithm. The biomarker correlation model is typically trained using a large dataset that covers a diverse range of demographic information, where “large” refers to a dataset taken from at least 10, more commonly hundreds, or more users. The “diverse” range of demographic information includes data from users of both sexes and at least two different classifications of age and / or race.

[0038] In the exemplary biomarker correlation model 400, T n and T n+1 Two incremental stages of a neural network are shown, as follows. Both stages have the same layers, as follows:

[0039] The model accepts input 402, which typically includes inputs from one or more sodium sensors, i.e., one or more input "channels" (typically two, to reduce noise), one or more potassium channels (likewise, typically two), additionally, in some embodiments, additional sensor inputs as described above, and user-specific inputs, e.g., age, weight, and sex. Additional user-specific inputs may include factors known to correlate with the target physiological parameters, e.g., race, height, and other personal data. Outputs generated from output layer 404 include physiological parameters, e.g., VO2 and VCO2, which the biomarker correlation model is trained to associate with the inputs. Additional outputs may include additional physiological parameters as described above (health and wellness guidelines are typically output by separately trained models).

[0040] As shown by Model 400, the first layer of the model is designed as a “Long Short-Term Memory” (LSTM) network 406, where the “memory” input is derived in each incremental time step by the LSTM layer of the previous time step.

[0041] The output of the LSTM layer is fed to the feedforward layer 408, which generates an output. An additional node, such as a "soft plus activation" layer 410, may also exist between the LSTM and the feedforward layer. The soft plus layer uses the activation function Y=log(l+e x By applying this, the output is guaranteed to always be positive (function Y=log(l+e) x (Note that this is a smooth, continuous version of the neural network reluLayer.) Testing the exemplary model with real-time sensor data demonstrated its usefulness by generating values ​​for predicted VO2 that closely matched the values ​​measured by the respiratory apparatus.

[0042] Figure 5 is a graph showing the correlation between sensor data of potassium electrolyte (referred to herein as potassium) in sweat and oxygen consumption (VO2) over time, according to an embodiment of the present invention. As shown, VO2 increases as the measured potassium decreases, exhibiting an inverse correlation that is incorporated into the correlation model.

[0043] Figures 6A and 6B are graphs showing oxygen consumption (VO2) during exercise, illustrating the measured VO2 and the VO2 predicted by the exemplary biomarker correlation model shown in Figure 4, as described above, over a period of multiple breaths by the user. As shown, the predicted VO2 closely matches the actual value, demonstrating the usefulness of the exemplary model.

[0044] It should be understood that the processing elements shown or described herein are preferably implemented by one or more computers in computer hardware and / or computer software embodied in a non-temporary, computer-readable medium, in accordance with prior art, such as employing computer processors, memory, I / O devices, and network interfaces coupled via a computer bus or alternative connection arrangement.

[0045] Unless otherwise stated, the terms “processor” and “device” are intended to include any processing device, e.g., a CPU (Central Processing Unit) and / or other processing circuits (e.g., a GPU), and may refer to multiple processing devices. Various elements related to a processing device may be shared by other processing devices.

[0046] As used herein, the term “memory” is intended to include memory associated with a processor or CPU, such as RAM, ROM, fixed memory devices (e.g., hard drives), removable memory devices (e.g., diskettes, tapes), and flash memory. Such memory may be considered computer-readable storage media.

[0047] In addition, the terms “input / output device” or “I / O device” may include one or more input devices for inputting data into a processing unit (e.g., a keyboard, mouse, scanner, HUD, etc.) and / or one or more output devices for presenting results related to the processing unit (e.g., a speaker, display, printer, HUD, AR, VR, etc.).

[0048] Embodiments of the present invention may include systems, methods, and / or computer program products. A computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to perform aspects of the present invention.

[0049] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by instruction-executing devices. Computer-readable storage media can be, for example, but are not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. A non-exhaustive list of more specific examples of computer-readable storage media includes: portable computer diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital multipurpose disks (DVDs), Blu-rays, magnetic tapes, holographic memory, memory sticks, floppy disks, mechanically encoded devices on which instructions are recorded, such as punched cards or grooved raised structures, and any suitable combination thereof. As used herein, a computer-readable storage medium should not be construed as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses passing through fiber optic cables), or electrical signals transmitted through wires.

[0050] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to each computing / processing device, or to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. A network adapter card or network interface in each computing / processing device may receive computer-readable program instructions from the network and transfer them for storage in a computer-readable storage medium within each computing / processing device.

[0051] The computer-readable program instructions for performing the operations of the present invention may be assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Smalltalk, C++ or similar, and conventional procedural programming languages ​​such as the "C" programming language or similar. The computer-readable program instructions may run entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or wide area network (WAN), or the connection may be to an external computer (e.g., via the Internet using an Internet Service Provider). In some embodiments, for example, an electronic circuit including a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA) may execute computer-readable program instructions by utilizing state information of computer-readable program instructions to personalize the electronic circuit in order to perform an aspect of the present invention.

[0052] When aspects of the present invention are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention, it will be understood that each block in the flowchart and / or block diagram, as well as combinations of blocks in the flowchart and / or block diagram, can be implemented by computer-readable program instructions.

[0053] These computer-readable program instructions may be provided to the processor of a general-purpose computer, a dedicated computer, or other programmable data processing device for manufacturing a machine, and as a result, instructions executed via the processor of the computer or other programmable data processing device create means for implementing functions / processes specified in the blocks of a flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that can instruct a computer, a programmable data processing device, and / or other device to function in a particular manner, and as a result, a computer-readable storage medium having instructions stored therein comprises a product containing instructions that implement embodiments of functions / processes specified in the blocks of a flowchart and / or block diagram.

[0054] Computer-readable program instructions can also be loaded into a computer, other programmable device, or other device to cause a series of actions to be performed on the computer, other programmable device, or other device in order to generate a computer implementation process, and as a result, the instructions executed on the computer, other programmable device, or other device implement the functions / processes specified in one or more blocks of a flowchart and / or block diagram.

[0055] Any flowcharts and block diagrams included herein illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which may contain one or more executable instructions for implementing a specified logical function(s). In some alternative implementations, the functions described in a block may occur out of the order shown herein. For example, two blocks shown consecutively may actually be executed substantially simultaneously, or blocks may sometimes be executed in reverse order depending on the functionality involved. It should also be noted that each block in a block diagram and / or flowchart illustration, as well as any combination of blocks in a block diagram and / or flowchart illustration, may be implemented by a dedicated hardware-based system that performs a specified function or action, or a combination of dedicated hardware and computer instructions.

[0056] The descriptions of various embodiments of the present invention are presented for illustrative purposes only and are not intended to be exhaustive or limitful to the embodiments disclosed. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the embodiments described. The terms used herein have been chosen to best describe the principles, practical applications, or technical improvements to the technologies available on the market, or to enable those else skilled in the art to understand the embodiments disclosed herein.

[0057] Examples

[0058] Embodiments of the present invention may include the following configurations.

[0059] Example 1 is a system for physiological analysis and feedback having multiple sensors worn on a user's skin and configured to measure sweat data indicating the levels of multiple respective sweat components. One of the sweat components may be potassium. In addition to the multiple sensors, the system includes one or more processors having a memory containing instructions that, when executed, implement processing steps including: a) receiving sweat data; b) applying a biomarker correlation model to the sweat data, wherein the biomarker correlation model is trained to correlate an input including multiple sweat components with an output including multiple physiological parameters, wherein two of the multiple physiological parameters are oxygen consumption (VO2) and carbon dioxide emissions (VCO2); c) determining values ​​of the multiple physiological parameters, including the values ​​of VO2 and VCO2, by the biomarker correlation model; d) calculating the level of the respiratory exchange ratio (RER) from the VCO2 / VO2 ratio; and e) transmitting one or more indicators of the values ​​of the multiple physiological parameters to a user interface in real time, wherein the indicators include the calculated level of RER.

[0060] Example 2 is a system comprising the features of Example 1, wherein one or more processors are further configured to perform the step of calculating the risk of coronary artery occlusion by applying the calculated VO2 levels to a cardiac disease risk correlation model trained to correlate VO2 levels with the level of risk of coronary artery occlusion.

[0061] Example 3 is a system comprising the features of Example 1, with or without any of the additional features of the examples listed above, further comprising determining that the risk exceeds a pre-set threshold and sending an alert to the user to consult a medical professional.

[0062] Example 4 is a system comprising the features of Example 1, with or without any of the additional features listed above, wherein the sweat data includes sodium in addition to potassium.

[0063] Example 5 is a system comprising the features of Example 1, with or without any of the additional features of the examples listed above, and is configured to transmit metrics to a user interface in real time.

[0064] Example 6 is a system that includes the features of Example 1, with or without any of the additional features of the examples listed above, wherein the biomarker correlation model is trained to correlate multiple sweat components with outputs that include multiple physiological parameters, along with user-specific data.

[0065] Example 7 is a system that includes the features of Example 6, with or without any of the additional features of the examples listed above, wherein user-specific data includes the user's age, weight, and gender.

[0066] Example 8 is a system that includes the features of Example 1, with or without any of the features of Example 2, wherein the multiple sensors include two sodium sensors and two potassium sensors, and a biomarker correlation model is trained on inputs including data from the sodium sensors and the potassium sensors.

[0067] Example 9 is a system comprising the features of Example 1, with or without any of the additional features of the examples listed above, wherein several physiological parameters include the user's blood potassium level.

[0068] Example 10 is a system that includes features of any of the above examples, wherein the multiple sweat components include potassium and sodium, in addition to at least one electrolyte from among magnesium, chloride, ammonia, and calcium.

[0069] Example 11 is a system that includes any of the features of the above examples, wherein the multiple sweating components include at least one metabolite from among the metabolites of glucose, lactic acid, ethanol, and uric acid.

[0070] Example 12 is a system comprising features of any of the above examples, wherein the multiple sweating components include at least one of zinc, caffeine, levodopa, zinc, copper, cortisol, neuropeptides, interleukin-6 (IL-6), tyrosine, or cytokines.

[0071] Example 13 is a system comprising features of any of the above embodiments, and further comprising at least one non-sweating sensor that generates non-sweating data, wherein one or more processors are further configured to apply the sweating and non-sweating data, along with user-specific data, to a biomarker correlation model to determine a plurality of physiological parameters, wherein the biomarker correlation model is trained to correlate the sweating and user-specific data, along with the non-sweating data, with a plurality of physiological parameters.

[0072] Example 14 is a system that includes the features of Example 13, wherein at least one non-sweating sensor is at least one of a body temperature sensor, a pH sensor, an accelerometer, a gyroscope, and a blood glucose monitor.

[0073] Example 15 is a system that includes features of any of the above examples, and the biomarker correlation model is an artificial intelligence model, such as a neural network or a random forest classifier. Example 16 is a system in which the biomarker correlation model is trained on a large dataset covering a diverse range of demographic information. Example 17 includes any of the above features and is configured to continuously update the biomarker correlation model as new user-specific input data is received.

[0074] Example 18 is a system that includes any of the features of the above examples, in which sweat data is received at intervals of up to once every 15 minutes.

[0075] Example 19 is a system that includes any of the features of the above examples, wherein the user interface is either a smartwatch or a smartphone user interface.

[0076] Example 20 is a system comprising features of any of the above embodiments, and further includes a wearable device having one or more processors and at least one of a plurality of sensors.

[0077] Example 21 is a system that includes features of any of the above examples, in which multiple sensors are integrated into a wearable device configured as an armband, headband, chest strap, ring, headphones, or tattoo sticker.

[0078] Example 22 is a system that includes features of any of the above examples and is further configured to apply values ​​of physiological parameters to a wellness correlation model that correlates the physiological parameters with a set of health and wellness guidelines in order to determine correlated health and wellness guidelines, and to provide the user with personalized recommendations to optimize the user's performance and well-being in accordance with the correlated health and wellness guidelines (where the correlated health and wellness guidelines are one of several indicators of the values ​​of several physiological parameters sent to the user interface).

[0079] Example 23 is a system that incorporates the features of Example 22, and the correlated health and wellness guidelines include warning alerts to prompt the user to take at least one of the following actions: reduce activity level, rest, consume carbohydrates, and drink water.

[0080] Example 24 is a system that includes the features of Example 22, and the correlated health and wellness guidelines include exercise instructions for the user to change workout type and / or exercise form.

[0081] Example 25 is a system that includes the features of Example 22, and the correlated health and wellness guidelines include at least one of the following nutritional indicators: 1) food to be consumed during the current training exercise, 2) food to be consumed before and / or after a particular exercise, and 3) recommended calorie intake.

[0082] Example 26 is a system that includes the features of Example 1, wherein one or more processors include a distributed processing system comprising a local processor and a remote processor, wherein the local processor receives sweating data and determines one or more physiological parameters, and the remote processor is configured to receive the determined values ​​of the physiological parameters from the local processor and to apply a wellness correlation model to determine correlated health and wellness guidelines.

[0083] Example 27 is a method for physiological analysis and feedback implemented by one or more processors having associated memory with instructions that, when executed, implement the following steps: receiving sweat data; applying a biomarker correlation model to the sweat data, wherein the biomarker correlation model is trained to correlate multiple sweat components with multiple physiological parameters, wherein two of the multiple physiological parameters are oxygen consumption (VO2) and carbon dioxide emissions (VCO2); determining values ​​of the multiple physiological parameters, including the values ​​of VO2 and VCO2, by the correlation model; calculating the level of the respiratory exchange ratio (RER) from the ratio VCO2 / VO2; and transmitting one or more indicators of the determined physiological parameter values ​​to a user interface, wherein the indicators include the calculated level of RER.

[0084] Further embodiments of the present invention include the features of the method of Example 28 and any additional features of Examples 2 to 27.

Claims

1. A system for physiological analysis and feedback, The system comprises a plurality of sensors worn by the user on the user's skin and configured to measure sweat data in real time, indicating the levels of multiple respective sweat components, and one or more processors having a memory that, when executed, implements the following steps: Here, at least one of the sweat components is potassium, The above process is, a) Receiving the sweat data, b) Applying a biomarker correlation model to the sweat data, wherein the biomarker correlation model is trained to correlate the input data, which includes the plurality of sweat components, with an output, which includes a plurality of physiological parameters, where two of the plurality of physiological parameters are oxygen consumption (VO2). ２ ) and carbon dioxide emissions (VCO ２ ) to apply, c) According to the biomarker correlation model, the VO ２ and VCO ２ Determining the values ​​of the plurality of physiological parameters, including the value of d) Ratio VCO ２ / VO ２ Calculating the respiratory exchange ratio (RER) level from this, e) A system that transmits one or more indicators of the values ​​of the plurality of physiological parameters to a user interface, wherein the indicators include the calculated level of RER.

2. The one or more processors VO ２ The calculated VO is used in a cardiac disease risk correlation model trained to correlate the level with the risk level of coronary artery occlusion. ２ The system according to claim 1, further configured to perform the step of calculating the risk of coronary artery occlusion by applying a level.

3. The system according to claim 2, further comprising determining that the risk exceeds a predetermined threshold and sending an alert to the user to consult with a medical professional.

4. The system according to claim 1, wherein the sweat data includes sodium.

5. The system according to claim 1, wherein the indicator is transmitted to the user interface in real time.

6. The system according to claim 1, wherein the biomarker correlation model is trained to correlate the plurality of sweat components with the output, which includes a plurality of physiological parameters, together with user-specific data.

7. The system according to claim 6, wherein the user-specific data includes the user's age, weight, and gender.

8. The system according to claim 1, wherein the plurality of sensors include two sodium sensors and two potassium sensors, wherein the biomarker correlation model is trained on inputs including data from the two sodium sensors and the two potassium sensors.

9. The system according to claim 1, wherein the plurality of physiological parameters include the user's blood potassium level.

10. The system according to claim 1, wherein the plurality of sweat-inducing components include at least one electrolyte selected from among magnesium, chloride, ammonia, and calcium.

11. The system according to claim 1, wherein the plurality of sweat-inducing components include at least one metabolite selected from the metabolites of glucose, lactic acid, ethanol, and uric acid.

12. The system according to claim 1, wherein the plurality of sweat components include at least one of zinc, caffeine, levodopa, zinc, copper, cortisol, neuropeptides, interleukin-6 (IL-6), tyrosine, hormones, hormone precursors, or cytokines.

13. The system according to claim 1, further comprising at least one non-sweating sensor that generates non-sweating data, wherein one or more processors are further configured to apply the sweating and non-sweating data, together with the user-specific data, to a biomarker correlation model in order to determine the plurality of physiological parameters, wherein the biomarker correlation model is trained to correlate the sweating and user-specific data, together with the non-sweating data, with the plurality of physiological parameters.

14. The system according to claim 13, wherein the at least one non-sweating sensor is at least one of a body temperature sensor, a pH sensor, an accelerometer, a gyroscope, a heart rate monitor that measures heart rate variability (HRV), and a blood glucose monitor.

15. The system according to claim 1, wherein the biomarker correlation model is an artificial intelligence model.

16. The system according to claim 1, wherein the biomarker correlation model is trained on a large dataset that covers a diverse range of demographic information.

17. The system according to claim 1, wherein one or more processors are further configured to continuously update the biomarker correlation model as new user-specific input data is received.

18. The system according to claim 1, wherein the sweat data is received at intervals of up to once every 15 minutes.

19. The system according to claim 1, wherein the user interface is the user interface of either a smartwatch or a smartphone.

20. The system according to claim 1, further comprising a wearable device having one or more processors and at least one of the plurality of sensors.

21. The system according to claim 1, wherein the plurality of sensors are integrated into a wearable device configured as an armband, headband, chest strap, ring, headphones, or tattoo sticker.

22. The system according to claim 1, wherein one or more processors are further configured to apply the values ​​of the physiological parameters to a wellness correlation model that correlates the physiological parameters with a set of health and wellness guidelines in order to determine correlated health and wellness guidelines, and to provide the user with personalized recommendations to optimize the user's performance and well-being in accordance with the correlated health and wellness guidelines.

23. The system according to claim 22, wherein the correlated health and wellness guidelines include warning alerts for the user to take at least one of the following actions: reduce activity level, rest, consume carbohydrates, and drink water.

24. The system according to claim 22, wherein the set of health and wellness guidelines includes exercise commands for the user to change workout types and / or exercise forms.

25. The system according to claim 22, wherein the set of health and wellness guidelines includes at least one of nutritional indicators, one or more of the following: 1) foods to be consumed during the current training exercise; 2) foods to be consumed before and / or after a particular exercise; and 3) recommended calorie intake.

26. The system according to claim 1, wherein one or more processors are further configured to provide a historical analysis of the user's physiological parameters over time in the user interface.

27. The system according to claim 1, wherein the user interface integrates with a social network or online platform to enable data sharing and engagement with other users.

28. The system according to claim 1, wherein the user interface includes audio or visual feedback to guide the user in achieving optimal physiological parameters.

29. The system according to claim 1, wherein the plurality of sensors are removable and can be repositioned on different areas of the user's body.

30. The system according to claim 1, further comprising a data storage module for securely storing and managing user-specific data and physiological measurements.

31. The system according to claim 1, wherein one or more processors employ data encryption and secure communication protocols to protect user privacy and data integrity.

32. The system according to claim 1, wherein one or more processors perform real-time anomaly detection to identify an abnormal physiological pattern and notify the user.

33. The system according to claim 1, wherein the one or more processors comprises a distributed processor including a local processor and a remote processor, wherein the local processor receives the sweating data and determines the one or more physiological parameters, and the remote processor is configured to receive the determined values ​​of the physiological parameters from the local processor and to apply the wellness correlation model to determine the correlated health and wellness guidelines.

34. A method for physiological analysis and feedback, implemented by one or more processors having associated memory containing instructions that, when executed, implement the following steps, the steps being: a) Receiving sweat data, b) Applying a biomarker correlation model to the sweating data, wherein the biomarker correlation model is trained to correlate a plurality of sweating components, obtained together with the sweating data and including at least a measure of potassium, with a plurality of physiological parameters, wherein two of the plurality of physiological parameters are oxygen consumption (VO ２ ), and carbon dioxide excretion (VCO ２ ), and applying; c) According to the correlation model, the VO ２ and VCO ２ Determining the values ​​of the plurality of physiological parameters, including the value of d) Ratio VCO ２ / VO ２ Calculating the respiratory exchange ratio (RER) level from this, e) A method comprising transmitting, in real time, one or more indicators of the determined physiological parameter values ​​to a user interface, wherein the indicators include the calculated level of RER.

35. The method according to claim 34, further comprising receiving non-sweating data from at least one non-sweating sensor, wherein the correlation model correlates the sweating and non-sweating data to a plurality of physiological parameters.

36. The method according to claim 35, wherein receiving the non-sweating data from at least one non-sweating sensor includes receiving the non-sweating data from at least one of a body temperature sensor, a pH sensor, an accelerometer, a gyroscope, and a blood glucose monitor.

37. The method according to claim 34, wherein the plurality of physiological parameters include the user's blood potassium level.

38. The method according to claim 34, wherein the plurality of physiological parameters include the risk of coronary artery occlusion.

39. The method according to claim 39, further comprising determining that the risk exceeds a predetermined threshold and sending an alert to the user to consult a medical professional.

40. The method according to claim 34, wherein the indicator is transmitted to the user interface in real time.

41. The method according to claim 34, wherein the biomarker correlation model is trained to correlate the plurality of sweat components with an output comprising a plurality of physiological parameters, together with user-specific data.

42. The method according to claim 34, wherein the user-specific data includes the user's age, weight, and gender.

43. The method according to claim 34, wherein the plurality of sweat components include at least one electrolyte from a list of electrolytes, wherein the list of electrolytes includes at least sodium, magnesium, chloride, ammonia, and calcium.

44. The method according to claim 34, wherein the plurality of sweat-inducing components include at least one metabolite selected from the metabolites of glucose, lactic acid, ethanol, and uric acid.

45. The method according to claim 34, wherein the plurality of sweat-inducing components include at least one of zinc, caffeine, levodopa, zinc, copper, cortisol, neuropeptides, interleukin-6 (IL-6), tyrosine, or cytokines.

46. The method according to claim 34, wherein the step further comprises applying a wellness correlation model to the determined values ​​of the physiological parameters to correlate the physiological parameters with a set of health and wellness guidelines, wherein the correlated health and wellness guidelines are one of the indices of the values ​​of the one or more determined physiological parameters transmitted to the user interface.

47. The method according to claim 46, wherein the correlated health and wellness guidelines include warning alerts for the user to take at least one of the following actions: reduce activity level, rest, consume carbohydrates, and drink water.

48. The method according to claim 46, wherein the set of health and wellness guidelines includes exercise instructions for the user to change workout types and / or exercise forms.

49. The method according to claim 46, wherein the set of health and wellness guidelines includes at least one nutrition indicator comprising one or more of the following: 1) foods to be consumed during the current exercise; 2) foods to be consumed before and / or after a particular exercise; and 3) recommended calorie intake.

50. The method according to claim 34, wherein the processor is a distributed processor comprising a local processor and a remote processor, wherein the local processor receives the sweating data and determines one or more physiological parameters, and wherein the remote processor receives the determined values ​​of the physiological parameters from the local processor and applies the wellness correlation model to determine the correlated health and wellness guidelines.

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