Systems and techniques for monitoring a medical condition of a user using physiological sensors

Abstract

To provide a medical monitoring system.SOLUTION: An example medical monitoring system includes a sensor apparatus comprising a cardiac sensor and an acceleration sensor, and an electronic device communicatively coupled to the sensor apparatus. The electronic device is configured to cause the sensor apparatus to obtain heart data for the user during a first time period and acceleration data for the user during the first time period. Further, the electronic device is configured to determine, based on the data, first physiological data representing cardiac activity of the user during the first time period and second physiological data representing changes in the acceleration data during the time period. Further, the electronic device is configured to determine a biomarker representative of a health condition of the user based on the first physiological data and the second physiological data, and store a data structure representative of the biomarker.SELECTED DRAWING: Figure 8

Description

[Technical Field]

[0001] FIELD OF THE INVENTION The present specification relates generally to systems and methods for monitoring a medical condition of a user using physiological sensors. [Background technology]

[0002] Generally, a user's health status can be assessed by measuring one or more physiological characteristics of the user and comparing the measured physiological characteristics to a standard. For example, a user having a physiological characteristic that meets or exceeds a particular standard may be in good health, while a user having a physiological characteristic that does not meet the standard may be in poor health. Summary of the Invention

[0003] Generally, medical monitoring systems can be used to monitor the health status of a user and facilitate treatment for the user.

[0004] In one example implementation, a medical monitoring system includes a sensor device configured to acquire sensor data representative of one or more physiological characteristics of a user, and one or more processor modules (e.g., a computer processor) configured to process the sensor data and determine one or more biomarkers representative of a medical condition of the user.

[0005] For example, the medical monitoring system can acquire sensor data while the user is engaged in physical exertion, such as while the user is walking continuously for a period of time (e.g., six minutes). The sensor data can include measurements of the user's cardiac activity, such as the user's heart rate and / or heart rate variability (HRV) during the period of time (e.g., by acquiring one or more cardiac sensors). Additionally, the sensor data can include measurements of the user's movement during the period of time (e.g., acceleration data acquired using one or more acceleration sensors). Based on these measurements, the medical monitoring system can determine biomarkers indicative of the user's physical health, such as the user's functional capacity and / or cardiopulmonary status. Additionally, the medical monitoring system can present the biomarkers to the user or another user (e.g., a healthcare provider) to facilitate the user's treatment and / or monitoring of the user's health over time.

[0006] In some implementations, the biomarker can be determined, at least in part, by determining the ratio between (i) the user's heart rate over a period of time (e.g., 6 minutes) and (ii) a movement metric that represents a change in the user's movement over that period of time. Additionally, the value of the biomarker can represent the user's relative health. For example, within a particular range of values ​​for the biomarker, a lower value can indicate a relatively higher health state for the user, and a higher value for the biomarker can indicate a relatively lower health state for the user.

[0007] Additionally, in at least some implementations, biomarkers acquired by the medical monitoring system can be used as estimates or approximations of other biomarkers indicative of the user's health status that would otherwise require manual input from the user or a third-party observer to calculate.

[0008] For example, in a traditional "6-minute walk test" (6MWT), a user walks continuously for a 6-minute time period. At the end of the time period, an observer (e.g., a healthcare provider in a clinical setting) measures the distance the user walked during that time period and uses the measured distance as a biomarker representing the user's health status. For example, if the user walked a long distance during that time period, the biomarker value will be high, representing a relatively high level of health for the user. If the user walked a short distance during that time period, the biomarker value will be low, representing a relatively low level of health for the user.

[0009] However, the traditional "6-minute walk test" can be difficult to perform in practice and can be subject to variability. For example, trained observers may not be readily available to administer the test. In the absence of trained observers, users may perform the test incorrectly (e.g., by measuring the wrong distance or walking for the wrong time), which can negatively impact the accuracy and reliability of the results.

[0010] In contrast, biomarkers obtained by the medical monitoring system described herein can approximate those generated by the "6-minute walk test," but do not require manual input by a trained observer or the user themselves. Thus, users can use the medical monitoring system (e.g., in a home environment, without the supervision of a healthcare provider) to more easily and accurately monitor their health status.

[0011] The embodiments described herein provide various technical advantages. As one example, the embodiments described herein enable a computer system to automatically determine a user's health status based on sensor data (e.g., cardiac data, acceleration data, etc.) without requiring manual feedback from a human. Furthermore, medical monitoring systems can provide this functionality by performing computer-specific operations on input data in an objective manner (and in a manner that is infeasible for a human to perform) rather than relying on subjective human interpretation of the input data, which may be poorly suited for computer implementation.

[0012] As another example, the embodiments described herein can be used to facilitate monitoring and treatment of a user, thereby improving the user's health. For example, a medical monitoring system can generate biomarkers representative of the user's health and provide the biomarkers to the user or another user (e.g., a healthcare provider). Based on this information, the user or another user can take proactive steps to improve the user's health, such as conducting further tests to diagnose the user's medical condition, modifying the user's behavior, lifestyle, and / or diet, or any other action.

[0013] In one aspect, a medical monitoring system includes a sensor device configured to be attached to a user's body, the sensor device including one or more cardiac sensors and one or more acceleration sensors; and an electronic device communicatively coupled to the sensor device, the electronic device including one or more computer processors configured to: cause the sensor device to acquire cardiac data about the user during a first time period using the one or more cardiac sensors; cause the sensor device to acquire acceleration data about the user during the first time period using the one or more acceleration sensors; determine first physiological data representative of the user's cardiac activity during the first time period based on the cardiac data; determine second physiological data representative of changes in the acceleration data over the time period based on the acceleration data; determine biomarkers representative of the user's health status based on the first physiological data and the second physiological data; and store a data structure representative of the biomarkers.

[0014] Implementations of this aspect can include one or more of the following features.

[0015] In some implementations, the biomarkers can represent the functional capabilities of the user.

[0016] In some implementations, the biomarkers can represent the cardiopulmonary status of the user.

[0017] In some implementations, the sensor device can be configured to be attached to the chest of a user.

[0018] In some implementations, the first time period may be six minutes.

[0019] In some implementations, the one or more cardiac sensors may include an electrocardiogram (ECG) sensor.

[0020] In some implementations, the first physiological data may represent a heart rate of the user during a first period of time.

[0021] In some implementations, determining the first physiological data may include segmenting the cardiac data into a plurality of cardiac data segments, each of the cardiac data segments representing a respective different heartbeat of the user.

[0022] In some implementations, determining the first physiological data may include determining a heart rate of the user during the first time period based on the cardiac data segments.

[0023] In some implementations, the acceleration data may include multiple acceleration signals, each representing acceleration in a different spatial dimension.

[0024] In some implementations, determining the second physiological data may include segmenting the acceleration data into a plurality of acceleration data segments, each of the acceleration data segments representing movement of the user during a respective different epoch of the first time period; determining, for each of the epochs, a change in the acceleration data during that epoch; and determining the second physiological data based on the change in the acceleration data during each of the epochs.

[0025] In some implementations, each of the epochs can correspond to a different respective time window within the first time period.

[0026] In some implementations, each of the time windows may be a 15 second window.

[0027] In some implementations, determining the second physiological data may include determining, for each of the epochs, a mean amplitude deviation of the acceleration data during that epoch, and summing the mean amplitude deviations of the acceleration data during each of the epochs.

[0028] In some implementations, determining the second physiological data may include determining the logarithm of a sum of the mean amplitude deviation of the acceleration data during each of the epochs.

[0029] In some implementations, determining the second physiological data may include determining the second physiological data based on a regression model having as input the logarithm of the sum of the mean amplitude deviations of the acceleration data.

[0030] In some implementations, determining the biomarker can include determining a ratio of the first physiological data and the second physiological data.

[0031] In some implementations, causing the user to present the biomarker can include determining a plurality of numerical ranges, each of the numerical ranges corresponding to a respective different health rank, determining that the biomarker is within a first numerical range of the plurality of numerical ranges, the first numerical range corresponding to a first health rank, and causing the user to present the first health rank.

[0032] In some implementations, having the biomarkers presented to the user can include presenting a graphical user interface to the user using a display device, the graphical user interface including a graphical display element that presents the biomarkers.

[0033] In some implementations, causing the biomarker to be presented to the user may include presenting an audio notification to the user using an audio speaker, the audio notification indicating the biomarker.

[0034] In some implementations, the one or more computer processors can be configured to determine, based on the biomarkers, one or more recommendations for improving the health of the user, and to present the one or more recommendations to the user.

[0035] In some implementations, the one or more computer processors can be further configured to instruct the user to walk during the first time period using at least one of the display screen or the audio speaker.

[0036] In some implementations, the one or more computer processors may be further configured to cause the sensor device to continuously acquire cardiac data and acceleration data during a second time period, where the first time period is a subset of the second time period, and to select the first time period based on at least one of the cardiac data or the acceleration data.

[0037] In some implementations, the one or more computer processors can be further configured to cause the biomarkers to be presented to at least one of the users, or to additional users.

[0038] In another aspect, a method includes using an electronic device to cause one or more cardiac sensors of a sensor device to acquire cardiac data about a user during a first time period; using the electronic device to cause one or more acceleration sensors of the sensor device to acquire acceleration data about the user during the first time period; determining, by the electronic device, first physiological data representative of cardiac activity of the user during the first time period based on the cardiac data; determining, by the electronic device, second physiological data representative of changes in the acceleration data over the time period based on the acceleration data; determining, by the electronic device, biomarkers representative of a health status of the user based on the first physiological data and the second physiological data; and storing, by the electronic device, a data structure representing the biomarkers.

[0039] Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions or operations described herein. One or more computer systems can be configured to perform particular actions by installing software, firmware, hardware, or combinations thereof on the system that cause the system to perform the actions during operation. One or more computer programs can be configured to perform particular actions by including instructions that, when executed by a data processing device, cause the device to perform the actions.

[0040] The details of one or more embodiments of the subject matter herein are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, drawings, and claims. [Brief explanation of the drawings]

[0041] [Figure 1] FIG. 1 illustrates an exemplary medical monitoring system. [Figure 2] FIG. 1 illustrates an exemplary operation of a medical monitoring system. [Figure 3] 10A-10C show plots representing the results of experimental studies performed to evaluate the performance of exemplary motion metrics. [Figure 4] 10A-10C show plots representing the results of experimental studies performed to evaluate the performance of exemplary motion metrics. [Figure 5] 10A-10C show plots representing the results of experimental studies performed to evaluate the performance of exemplary motion metrics. [Figure 6] 10A-10C show plots representing the results of experimental studies performed to evaluate the performance of exemplary motion metrics. [Figure 7] 10A-10C show plots representing the results of experimental studies performed to evaluate the performance of exemplary motion metrics. [Figure 8] FIG. 1 is a flowchart diagram of an exemplary process for monitoring a user's health status. [Figure 9] FIG. 1 is a diagram of an exemplary computer system. DETAILED DESCRIPTION OF THE INVENTION

[0042] Like reference numbers and designations in the various drawings indicate like elements.

[0043] 1 illustrates an exemplary medical monitoring system 100 for automatically monitoring the health status of a user. In general, a user may be any entity or individual for whom medical information is obtained (e.g., for personal use and / or to assist in medical treatment or research).

[0044] The medical monitoring system 100 includes a sensor device 110 and an electronic device 120 communicatively connected to each other (e.g., via one or more wired or wireless communication links 150). Generally, the medical monitoring system 100 acquires sensor data about a user using the sensor device 110 and processes the sensor data using the electronic device 120 to determine one or more biomarkers indicative of the user's medical condition.

[0045] The sensor device 110 includes one or more sensors configured to obtain measurements related to the physiology of the user, the behavior of the user, and / or any other characteristic of the user.

[0046] 1 , the sensor device 110 may include one or more cardiac sensors 112 configured to acquire sensor data representative of a user's cardiac activity. In some implementations, the cardiac sensors 112 may include one or more electrocardiogram (ECG) sensors and / or one or more PPG sensors. In some implementations, the cardiac sensors 112 may acquire sensor data at 250 Hz (or approximately 250 Hz).

[0047] Additionally, the sensor device 110 may include one or more accelerometers 114 configured to acquire sensor data representative of a user's movements or motion in one or more directions. For example, at least some of the accelerometers 114 may be triaxial accelerometers configured to measure acceleration in three directions (e.g., x, y, and z directions on a Cartesian coordinate system). In some implementations, the accelerometers 114 may acquire sensor data at 32 Hz (or approximately 32 Hz).

[0048] Additionally, the sensor device 110 includes a communication module 116 configured to transmit data to and / or receive data from the electronic device 120. By way of example, the communication module 116 may include one or more receivers, transmitters, and / or transceivers. In some implementations, the communication module 116 may communicate with the electronic device 120 via one or more wireless links (e.g., serial links, Ethernet links, etc.) and / or wireless links (e.g., Wi-Fi links, Bluetooth links, etc.).

[0049] Further, the sensor device 110 is configured to be worn by a user. For example, as shown in FIG. 1 , the sensor device 110 may include a substrate 118 configured to be secured to a user's body (e.g., via an adhesive). Further, the cardiac sensor 112, the accelerometer 114, and the communication module 116 may be attached to the substrate such that they are secured to the user's body. In some implementations, at least a portion of the substrate 118 may be composed of a compliant material such as silicone, rubber, elastomer, and / or any other flexible material. In some implementations, at least a portion of the compliant substrate 118 may be composed of a rigid material such as a hard metal, a hard plastic, or the like.

[0050] Generally, electronic device 120 is configured to receive sensor data acquired by sensor apparatus 110 and process the sensor data to determine one or more biomarkers indicative of a medical condition of the user. Further, electronic device 120 is configured to present information regarding the biomarkers and any other information to the user and / or another user (e.g., a healthcare provider).

[0051] Generally, electronic device 120 may include any number of devices configured to receive, process, and transmit data. Examples of electronic device 120 include client computing devices (e.g., desktop computers or notebook computers), server computing devices (e.g., server computers or cloud computing systems), mobile computing devices (e.g., cellular phones, smartphones, tablets, personal digital assistants, network-enabled notebook computers), wearable computing devices (e.g., smartphones or headsets), and other computing devices capable of receiving, processing, and transmitting data. In some implementations, electronic device 120 may include computing devices that operate using one or more operating systems (e.g., Microsoft Windows, Apple macOS, Linux, Unix, Google Android, and Apple iOS, among others) and one or more architectures (e.g., x86, PowerPC, and ARM, among others).

[0052] 1, electronic device 120 is depicted as a single component. However, in practice, electronic device 120 may be implemented in one or more computing devices (e.g., each computing device includes at least one processor, such as a microprocessor or microcontroller). As an example, electronic device 120 may be a single computing device, such as a single smartphone. As another example, electronic device 120 may include multiple computing devices connected via a network (e.g., the Internet, a local area network, etc.), and components of electronic device 120 may be maintained and operated on some or all of these computing devices. For example, electronic device 120 may include multiple computing devices, and components of electronic device 120 may be distributed across one or more of these computing devices.

[0053] 1 , electronic device 120 includes a database module 122, a communications module 124, a processing module 126, and a user interface module 128. The operational modules may be provided as one or more computer-executable software modules, hardware modules, or a combination thereof. For example, one or more of the operational modules may be implemented as blocks of software code including instructions that cause one or more processors to perform the operations described herein. Additionally or alternatively, one or more of the operational modules may be implemented in an electronic circuit, such as, for example, a programmable logic circuit, a field programmable logic array (FPGA), or an application-specific integrated circuit (ASIC).

[0054] The communications module 124 is configured to transmit data to and / or receive data from the sensor device 110. By way of example, the communications module 124 may include one or more receivers, transmitters, and / or transceivers. In some implementations, the communications module 124 may communicate with the sensor device 110 (e.g., via the communications module 116) over one or more wireless links (e.g., serial links, Ethernet links, etc.) and / or wireless links (e.g., Wi-Fi links, Bluetooth links, etc.).

[0055] The database module 122 maintains information related to the operation of the medical monitoring system 100 .

[0056] As an example, the database module 122 may store input data 122a used as input for determining one or more biomarkers representative of a user's health status. For example, the input data 122a may include at least some of the sensor data generated by the sensor device 110 (e.g., cardiac data, acceleration data, etc.).

[0057] As another example, the database module 122 can store output data 122b generated by the electronic device 120. As an example, the output data 122b can include one or more metrics or biomarkers generated by the electronic device 120 based on the input data 122a.

[0058] Additionally, the database module 122 may store processing rules 122c that specify how the data in the database module 122 is processed to perform the operations described herein.

[0059] As an example, the processing rules 122c may include one or more rules that specify how the input data 122a is formatted, parsed, and processed to determine one or more corresponding metrics or biomarkers for the user.

[0060] As another example, the processing rules 122c may include one or more rules that specify the conditions under which data is presented to a user (e.g., using the user interface module 128) and how the data is presented.

[0061] As another example, the processing rules 122c may include one or more rules that specify how data is to be stored for future retrieval and / or processing (eg, using the database module 122).

[0062] As another example, the processing rules 122c may include one or more rules for generating and / or modifying the input data 112a to have a specific, predetermined format. This may be beneficial, for example, in improving the functionality of an electronic device 120 equipped with multiple different types of sensors (e.g., each of which may output data having a different format). As an example, the processing rules 122c may specify that sensor data be processed so that each specific sensor data type, column name, and sample frequency resolution is converted into a common data format. Using a common data format allows data acquired from multiple sensors and / or sensor types to be processed in a consistent manner. Furthermore, the processing rules 122c may include rules for correcting data errors, removing noise, handling (e.g., removing) data outliers, and determining whether the data is suitable for further analysis. Furthermore, the processing rules 122c may include rules for generating a log of data cleaning activities, including actions taken and problems encountered. This log can be used to generate a report outlining the data cleaning process and its results.

[0063] Exemplary data processing techniques are described in further detail below.

[0064] The processing module 126 processes data stored on or otherwise accessible to the electronic device 120. For example, the processing module 126 may be used to perform one or more of the operations described herein (e.g., by executing the processing rule 122c on the input data 112a to generate the output data 122b).

[0065] The user interface module 128 is configured to present information to a user and / or receive input from a user. As an example, the user interface module 128 may include one or more display devices (e.g., display screens, touch screens, etc.) configured to present a user interface (e.g., a graphical user interface, GUI) that allows a user to interact with the electronic device 120 and / or the sensor device 110. Exemplary interactions include viewing data, transmitting data from one component to another, and / or issuing commands to the electronic device 120 and / or the sensor device 110. Commands may include, for example, any user instructions to one or more of the electronic device 120 and / or the sensor device 110 to perform a particular operation or task. In some implementations, the user interface module may also present information to the user audibly (e.g., using one or more speakers) and / or via haptic feedback (e.g., using one or more haptic generators, such as vibration generation).

[0066] In some implementations, software applications can be used to facilitate the performance of the tasks described herein. As an example, an application can be installed on electronic device 120. Additionally, a user can interact with the application to input data and / or commands to electronic device 120 and to review data generated by calling electronic device 120.

[0067] FIG. 2 illustrates an exemplary implementation and operation of the medical monitoring system 100.

[0068] In this example, sensor device 110 is worn by user 202 (e.g., attached to the user's skin, such as the user's chest). Furthermore, user 202 continuously walks over a time period. For example, user 202 may repeatedly walk between point 204a and point 204b from the start of the time period to the end of the time period. As user 202 walks, sensor device 110 acquires sensor data (e.g., cardiac data, acceleration data, etc.) about user 202 and transmits at least a portion of the sensor data to electronic device 120 for further processing.

[0069] In at least some implementations, the user 202 may walk for a six-minute time period. This may be beneficial, for example, in ensuring that the user 202 exerts a sufficient amount of physical effort, and sensor data obtained during that time period may be used to accurately and reliably ascertain the health status of the user 202. However, in some implementations, other time periods (e.g., one minute, two minutes, three minutes, etc., or any other time period) may also be used.

[0070] Further, in at least some implementations, the distance between point 204a and point 204b can be approximately 30 feet. This can be beneficial, for example, because it provides enough space for user 202 to walk continuously for a period of time before changing direction. Furthermore, this space is compact and therefore easy to find in many common environments (e.g., a user's home, office, etc.). However, in some implementations, other distances (e.g., 20 feet, 40 feet, 50 feet, etc., or any other distance) can also be used.

[0071] The electronic device 120 receives the sensor data and processes the sensor data to determine biomarkers indicative of the user's physical health. For example, the electronic device 120 can determine biomarkers indicative of the user's functional capacity (e.g., the user's functional capacity in various situations, e.g., associated with physical activity) and / or cardiopulmonary status (e.g., the health of the user's heart, lungs, circulatory system, etc.). Furthermore, the electronic device 120 can present the biomarkers to the user 202 or another user (e.g., a healthcare provider) to facilitate treatment of the user 202 and / or monitoring of the user's health over time.

[0072] In some implementations, the medical monitoring system 100 can instruct the user on how to perform a test using the medical monitoring system 100. For example, the medical monitoring system 100 can instruct the user to place the sensor device 110 on their body (e.g., using a display screen, audio speaker, etc.), select two points a specific distance apart (e.g., 30 feet), and walk continuously between the two points for a specific period of time (e.g., 6 minutes). As mentioned above, the medical monitoring system 100 can determine biomarkers based on the sensor data.

[0073] In some implementations, the medical monitoring system 100 can continuously obtain sensor data about the user (e.g., throughout the day) and automatically select a time period for determining biomarkers. For example, the medical monitoring system 100 can determine, based on the sensor data, that the user has been walking continuously during a particular time period (e.g., a continuous six-minute window). The medical monitoring system 100 can select sensor data from that time period and determine biomarkers based on the selected sensor data. This can be beneficial, for example, because it allows the medical monitoring system 100 to unobtrusively monitor the user's health over time (e.g., without the user having to manually initiate tests and / or perform prescribed physical activities).

[0074] In some implementations, the biomarker X may be determined, at least in part, by determining the ratio between (i) the user's heart rate over a period of time (e.g., 6 minutes) and (ii) a movement metric that represents a change in the user's movement over that period of time. By way of example, the biomarker X may be determined using the following relationship:

[0075]

number

[0076] In some implementations, the heart rate can be determined by dividing the cardiac data (e.g., ECG data) into multiple segments, with each segment corresponding to a different single heartbeat. For example, the cardiac data can be segmented by identifying features of the cardiac data (e.g., peaks, valleys, etc.) that are indicative of a heartbeat. Further, the number of segments can be counted to determine the total number of heartbeats.

[0077] In general, the motion metric may be determined based on the mean amplitude deviation (MAD) of acceleration data (e.g., acquired by accelerometer 114) over a particular period of time. Exemplary techniques for determining the motion metric are described in further detail below.

[0078] Additionally, the value of a biomarker may represent the relative health of a user, for example, within a particular range of values ​​for a biomarker, a lower value may indicate a relatively better health state for the user, and a higher value for the biomarker may indicate a relatively worse health state for the user.

[0079] As an illustrative example, during a time period, a first user and a second user walk in a similar manner. Therefore, the motion metrics representing changes in the users' motion will be the same. However, in this example, the first user's heart rate during the time period is higher than the second user's heart rate, which may indicate that the first user's functional capacity and / or cardiopulmonary fitness is poorer than that of the second user. This difference is reflected in the two users' biomarkers, with the first user's biomarker values ​​being higher than the second user's biomarker values.

[0080] As another example, during a time period, a first user and a second user have the same heart rate. However, the first user's physical activity during the time period was greater than the second user's physical activity (e.g., reflected by a greater degree of change in the first user's movement compared to the change in the second user's movement). This may indicate that the first user has better functional capacity and / or cardiopulmonary fitness than the second user. This difference is reflected in the two users' biomarkers, with the first user's biomarker values ​​being lower than the second user's biomarker values.

[0081] In some implementations, the medical monitoring system 100 can present the biomarkers to the user and / or another user (e.g., a healthcare provider). For example, the medical monitoring system 100 can present the biomarkers to the user and / or another user (e.g., graphically, audibly, tactilely, etc.) using the user interface module 128. As another example, the medical monitoring system 100 can transmit data representing the biomarkers to another device (e.g., a remote computer system, a cloud computing platform, etc.), which can cause the biomarkers to be presented to the user and / or another user (e.g., graphically, audibly, tactilely, etc.).

[0082] In some implementations, the medical monitoring system 100 can present a biomarker value (e.g., a calculated ratio between (i) a user's heart rate over a period of time and (ii) a movement metric representing a change in the user's movement over that period of time). For example, the biomarker value can be presented as a graphical display element on a GUI (e.g., as presented on a display device) and / or as an audio notification (e.g., as presented using an audio speaker).

[0083] In some implementations, the medical monitoring system 100 can determine a descriptive health category for the user based on the values ​​of the biomarkers. For example, the medical monitoring system 100 can have access to multiple health categories (e.g., "good," "fair," "poor," etc.), each associated with a different range of values. The medical monitoring system 100 can determine that the value of the biomarker falls within a particular range and determine that the user's health status corresponds to the category associated with that range. This can be beneficial, for example, because a descriptive health category may be easier for a user to understand compared to a specific value of the biomarker.

[0084] In some implementations, the medical monitoring system 100 can also determine additional information about the user's physiology. For example, the medical monitoring system 100 can determine cardiac metrics, such as the user's resting and peak heart rate and heart rate recovery after walking, based on the cardiac data. As another example, the medical monitoring system 100 can determine gait metrics related to the user's walking pattern based on the acceleration data. This additional information can also be presented to the user and / or another user (e.g., graphically, audibly, tactilely, etc.).

[0085] In some implementations, the medical monitoring system 100 can be used to facilitate treatment for the user 202, thereby improving the user's health. For example, the medical monitoring system 100 can generate biomarkers representative of the user's health and provide the biomarkers to the user 202 or another user (e.g., a healthcare provider). Based on this information, the user 202 or another user can take proactive steps to improve the user's health, such as conducting further tests to diagnose the user's medical condition, changing the user's behavior, lifestyle, and / or diet, or any other action.

[0086] In some implementations, the medical monitoring system 100 can determine one or more recommendations for improving the user's health based on the biomarkers and present the recommendations to the user.

[0087] In some implementations, the medical monitoring system 100 can also provide instructions to the user 202 on how to operate the medical monitoring system 100. For example, the medical monitoring system 100 (e.g., using the electronic device 120) can provide instructions to the user 202 to attach the sensor device 110 to a specific location on the user's body (e.g., on the user's chest) and walk back and forth between two points for a specific period of time (e.g., six minutes). Furthermore, once the user has completed their walk, the medical monitoring system 100 can provide the user 202 with feedback regarding the collected sensor data and biomarkers generated based on the sensor data. This can be beneficial, for example, in assisting the user 202 in performing a physical health test, even if the user 202 has no prior experience performing such a test. In some embodiments, the instructions may be provided by the medical monitoring system 100 graphically (e.g., using one or more display screens) and / or audibly (e.g., using one or more audio speakers).

[0088] Exemplary Techniques for Computing Motion Metrics Exemplary techniques for calculating motion metrics are described in further detail below.

[0089] In some implementations, the motion metric may represent the mean amplitude deviation (MAD) of acceleration data (e.g., acquired by accelerometer 114) over a particular period of time. As an example, in at least some implementations, the MAD may be the average distance of data points around the average of three axes of accelerometer data over a certain period of time according to the following relationship:

[0090]

number

number

number

[0091] In at least some implementations, the MAD can be used as an objective measure of a user's total activity during a period of time (eg, during a 6-minute walk).

[0092] Generally, MAD can be used to estimate the distance a user has walked during a specific time period. For example, in a traditional "6-minute walk test," a user walks continuously for a 6-minute time period. At the end of that time period, an observer (e.g., a healthcare provider) measures the distance the user has walked and uses the measured distance as a biomarker of the user's health status. The measured distance is sometimes referred to as the 6-minute walk distance (6MWD).

[0093] The MAD can be used to estimate the 6MWD without the need for the user to manually measure the distance walked. In particular, the MAD can be used to estimate the 6MWD (6MWD) according to the following relationship: estimated ) can be used to calculate

[0094]

number

number

number

number

[0095] where n is the total number of samples in each epoch, i is the index of each sample within the epoch, and x, y, z are the raw values ​​of the three axes from the accelerometer data.

[0096] The raw acceleration data is collected while the user walks for six minutes. The raw acceleration data is further segmented into epochs, each spanning a particular period of time. For example, the raw acceleration data may be segmented into 24 epochs, each spanning 15 seconds. This may be particularly suitable for use in measuring a user's physical movement as they walk a distance of approximately 30 feet (e.g., it is unlikely that a user will change direction multiple times during a particular epoch, which may skew or distort the results). In practice, however, the raw acceleration data may be segmented into a different number of epochs, each spanning a different length of time.

[0097] Additionally, the MAD is calculated for each epoch (eg, using Equations 4-6 above).

[0098] Furthermore, M.A.D. epoch The sum of is calculated over all epochs (e.g., using Equation 7 above), and the feature MAD of the 6-minute data for each walk is SUM results.

[0099] Exemplary Experimental Studies As described in more detail below, experimental studies were conducted to demonstrate the accuracy, reproducibility, reliability, and hardware independence of the MAD-based functional capacity biomarker.

[0100] Sensor-derived 6-minute walk distance estimation (MWD) estimated Metric) MAD SUM was calculated for each of the multiple walks by the user group. SUM was plotted and compared with the actual distance walked by the user (e.g., as measured by an observer in a clinic), and the relationship showed a quadratic curve.

[0101] Furthermore, to convert this quadratic relationship into a linear one, we use the MAD SUMThe logarithm of is calculated (e.g., Log(MAD SUM )).

[0102] Additionally, the logarithm of gait (MAD) derived from accelerometer data SUM A regression model was developed to predict 6MWD given ). 6MWD estimated = m*log(MAD SUM )+c(formula 7)

[0103] Using data from both long-distance and short-distance walking within the clinic, the slope m and intercept c of the above equation were derived as follows: m=226.277, and c=89.15 The coefficient of determination was 0.845 and the mean square error was 3194.260. A coefficient of determination of approximately 0.85 indicates that approximately 15% of the variation in the data is not explained by the fitted model.

[0104] The results of the regression model development are shown in Figure 3. In particular, Figure 3 shows the MAD SUM Scatter plots (plot 300a) between the mean and 6MWD (e.g., as measured by an observer in the clinic) and logarithm (MAD SUM ) and 6MWD (plot 300b).

[0105] To further evaluate the above regression model, k-fold cross-validation was performed with k = 10. Figure 3 shows the slope (plot 300c), intercept (plot 300d), and MSE (plot 300e) for various iterations when evaluated using k-fold cross-validation. The mean squared error from k-fold cross-validation was 3238.9 units (plot 300e), while the mean squared error from the regression fit was 3194.26 units. Furthermore, the mean slope from k-fold cross-validation was 226.27 (plot 300c), while the slope from the regression fit was 226.27. Furthermore, the mean intercept from k-fold cross-validation was 89.15 (plot 300d), while the intercept from the regression fit was 89.15. The consistency between the mean squared error obtained from k-fold cross-validation and the mean squared error obtained from the regression fit indicates the stability and reliability of the model. Furthermore, the closeness of the slope and intercept values ​​derived from both methods highlights the robustness of the model parameters. Such consistency suggests that the model's performance is not overly sensitive to the particular data split used during validation, and it can be expected to perform similarly on new, unseen data.

[0106] To assess the agreement between the predicted values ​​obtained after regression and the true values, two evaluation techniques were performed.

[0107] First, the Pearson correlation coefficient was calculated to measure the linear relationship between the two sets of values. As shown in Figure 3, the scatter plot 400a shows the 6MWD scores assessed across n=394 walks. estimated The results show that there is a high correlation between the 6MWD ("true") and the 6MWD ("true"). The correlation coefficient obtained was 0.91, indicating a strong linear correlation.

[0108] Second, a Bland-Altman plot was constructed to visualize the agreement between the two measurements by plotting the percentage difference between the predicted and true values ​​against their means (plot 400b). The observed mean difference was 0.52%, with limits of agreement ranging from +25% to -25% and a significant difference from the predicted values ​​(6MWD estimated) and the actual value (6MWD, or "true").

[0109] Reproducibility over the length of a short walking course Additionally, experiments were conducted using sensor technology for remote 6-minute walk test (6MWT) assessment to determine whether the 6MWT could be adapted for out-of-clinic environments.

[0110] Although the American Thoracic Society (ATS) 6MWT guidelines recommend a long (e.g., 100 ft), flat, straight, and obstacle-free hallway to perform the test, this specified testing environment is often poorly suited to the home setting, as hallways of this length are uncommon in a typical home. Therefore, the 6MWT estimated The independence of the metric from course length was investigated. In these experiments, a shorter course length of 30 feet was chosen and data were collected within the 6MWT clinic (e.g., including measurements of walking distance by a clinical observer).

[0111] First, we compared (i) the Pearson correlation coefficient between the 90-foot walkway and the shorter 30-foot walkway for the traditional 6MWD metric reported by clinicians in the field and (ii) the 6MWD derived from the MAD of acceleration data. estimated The correlation coefficients between the two trail courses were compared.

[0112] The results of this comparison are shown in Figure 5. In particular, Figure 5 shows the comparison of the traditional 6MWD metric with the 6MWD metric at two different footpath lengths. estimated Comparisons with metrics are shown, including (i) Pearson correlation coefficients (plots 500a and 500b), and (ii) Bland-Altman plots visualizing agreement and bias (plots 500c and 500d).

[0113] As shown in Figure 5, the Pearson correlation coefficient of the traditional 6MWD metric is 0.96. estimatedThe Pearson correlation coefficient for the metric is 0.98, which indicates a significant difference between the 6MWD across various course lengths. estimated It emphasizes that metrics are consistent.

[0114] Additionally, Bland-Altman plots were constructed for both the traditional 6MWD and the estimated 6MWD to visualize the agreement across two trail lengths (90 feet and 30 feet). For the traditional 6MWD metric, the average bias was 10.66%, and the limits of agreement ranged from 32% to -11% (plot 500c). In comparison, the 6MWD estimated The metric showed closer agreement with a mean bias of 8% and limits of agreement ranging from 24% to -8% (plot 500d). These results are consistent with the 6MWD estimated It demonstrates the robustness of the metric and highlights its usefulness as an accurate and reliable tool for functional assessment across various trail lengths.

[0115] Reproducibility across supervised in-clinic visits Traditional 6MWD metric and 6MWD estimated To assess the reproducibility of both metrics over time, measurements from two separate visits ("baseline" and "week 8") were analyzed.

[0116] The results of this study are shown in Figure 6. In particular, Figure 6 shows the Pearson correlation coefficient and Bland-Altman plot for the standard trail (plots 600a and 600b). Additionally, Figure 6 shows the corresponding plots for shorter trail lengths (plots 600c and 600d). The analysis shows that the correlation coefficient for the 6MWD is consistent across all trail lengths, regardless of the number of visits or trail length. estimated It highlights the excellent reproducibility of the metric.

[0117] Specifically, for a standard trail length of 90 feet (plot 600a, left), the 6MWD metric exhibited a Pearson correlation coefficient between visits of 0.903, while the same Bland-Altman plot (plot 500a, right) revealed a mean bias of -4.53%, with limits of agreement ranging from 28.4% to -37.48%. In contrast, the 6MWD for a standard trail estimated The metric showed excellent reproducibility with a correlation coefficient of 0.93 (plot 600b, left) and a significantly lower mean bias of -4.53% from Bland-Altman, with limits of agreement ranging from 22.8% to -25% (plot 600b, right).

[0118] Similar trends persisted when assessed over shorter course lengths. The traditional 6MWD metric produced a Pearson coefficient of 0.92 (plot 600c, left) and a Bland-Altman bias of -1.28% (plot 600c, right), with limits of agreement ranging from 28.69% to -31.27%. In contrast, the 6MWD estimated The metrics again showed greater reproducibility, with Pearson coefficients of 0.954 (plot 600d, left) and Bland-Altman biases of -1.83% (plot 600d, right), and limits of agreement ranging from 16.68% to -20.35%. Taken together, these findings suggest that the 6MWD over time, regardless of course length, is significantly more predictable. estimated The improved reproducibility of the metric is highlighted, supporting its usefulness in consistent functional assessment.

[0119] 6MWD in unsupervised home visits estimated Metric reproducibility Extending the assessment to home-based settings and 6MWD estimated Metric reproducibility was assessed across two separate visits.

[0120] The results of this evaluation are shown in Figure 7. In particular, Figure 7 shows:

[0121] (Left) Pearson correlation coefficient showing linear consistency between visits. (Right) Bland-Altman plot visualizing agreement and bias in two home-based measures. The results highlight the consistent performance of the metric in the home setting.

[0122] In this more informal and patient-friendly setting, the metric continues to show promising consistency. The Pearson correlation coefficient between visits was 0.961, suggesting high reproducibility (e.g., linear consistency) between visits (Figure 7, plot 700a). Further insight was gained from the Bland-Altman plot (Figure 7, plot 700b), which showed a mean bias of 6.45% with limits of agreement between 35% and -22.5%. The robust reproducibility observed across the changing environment of the home supports the validity of the 6MWD in distributed functional assessment. estimated It highlights the adaptability and potential of metrics.

[0123] Example Process 8 shows an example process 800 for monitoring a medical condition of a user using a physiological sensor. In some implementations, process 800 can be performed by the medical monitoring system 100 described in this disclosure.

[0124] In process 800, the system causes one or more cardiac sensors of a sensor device to acquire cardiac data about a user during a first time period (802). In some implementations, the first time period may be six minutes. In some implementations, the one or more cardiac sensors may include an electrocardiogram (ECG) sensor.

[0125] Additionally, the system causes one or more acceleration sensors of the sensor device to acquire acceleration data about the user during the first time period 804. In some implementations, the acceleration data can include multiple acceleration signals, each representing acceleration in a respective different spatial dimension.

[0126] In some implementations, the sensor device can be configured to be attached to the chest of a user.

[0127] In some implementations, the system can prompt the user to walk during the first period of time using at least one of a display screen or an audio speaker.

[0128] In some implementations, the system can cause the sensor device to continuously acquire cardiac data and acceleration data during a second time period, the first time period being a subset of the second time period, and the system can select the first time period based on at least one of the cardiac data or the acceleration data.

[0129] Further, the system determines first physiological data representative of the user's cardiac activity during the first time period based on the cardiac data (806). In some implementations, the first physiological data may represent the user's heart rate during the first time period.

[0130] In some implementations, determining the first physiological data may include segmenting the cardiac data into a plurality of cardiac data segments, each of the cardiac data segments representing a respective different heartbeat of the user.

[0131] In some implementations, determining the first physiological data may include determining a heart rate of the user during the first time period based on the cardiac data segments.

[0132] Additionally, the system determines (808) second physiological data based on the acceleration data, the second physiological data representing changes in the acceleration data over the time period.

[0133] In some implementations, determining the second physiological data may include segmenting the acceleration data into a plurality of acceleration data segments, each of the acceleration data segments representing movement of the user during a respective different epoch of the first time period; determining, for each of the epochs, a change in the acceleration data during that epoch; and determining the second physiological data based on the change in the acceleration data during each of the epochs.

[0134] In some implementations, each of the epochs can correspond to a different respective time window within the first time period.

[0135] In some implementations, each of the time windows may be a 15 second window.

[0136] In some implementations, determining the second physiological data may include determining, for each of the epochs, a mean amplitude deviation of the acceleration data during that epoch, and summing the mean amplitude deviations of the acceleration data during each of the epochs.

[0137] In some implementations, determining the second physiological data may include determining the logarithm of a sum of the mean amplitude deviation of the acceleration data during each of the epochs.

[0138] In some implementations, determining the second physiological data may include determining the second physiological data based on a regression model having as input the logarithm of the sum of the mean amplitude deviations of the acceleration data.

[0139] Further, the system determines 810 biomarkers representative of the user's health status based on the first physiological data and the second physiological data. In some embodiments, the biomarkers can represent the user's functional capacity. For example, the biomarkers can represent the user's cardiopulmonary status.

[0140] In some implementations, determining the biomarker can include determining a ratio of the first physiological data and the second physiological data.

[0141] Additionally, the system stores (812) a data structure representing the biomarkers.

[0142] In some implementations, the system may also allow biomarkers to be submitted to the user and / or additional users.

[0143] For example, the system may use a display device to present a graphical user interface to the user, the graphical user interface including a graphical display element that presents the biomarkers. In another example, the system may use an audio speaker to present an audio notification to the user, the audio notification indicating the biomarkers.

[0144] In some implementations, the system can determine a plurality of numerical ranges, each corresponding to a different health rank. Further, the system can determine that the biomarker is within a first numerical range of the plurality of numerical ranges, the first numerical range corresponding to a first health rank, and cause the first health rank to be presented to the user.

[0145] In some implementations, the system can determine one or more recommendations for improving the user's health based on the biomarkers and present the one or more recommendations to the user.

[0146] Exemplary Computer System 9 illustrates an exemplary computing system according to an implementation of the present disclosure. System 900 may be used for any of the operations described with respect to the various implementations discussed herein. System 900 may include one or more processors 910, memory 920, one or more storage devices 930, and one or more input / output (I / O) devices 960 controllable via one or more I / O interfaces 940. The various components 910, 920, 930, 940, or 960 may be interconnected through at least one system bus 950, which may enable data transfer between the various modules and components of system 900.

[0147] The processor 910 may be configured to process instructions for execution within the system 900. The processor 910 may include a single-threaded processor, a multi-threaded processor, or both. The processor 910 may be configured to process instructions stored in the memory 920 or the storage device 930. The processor 910 may include hardware-based processors, each including one or more cores. The processor 910 may include a general-purpose processor, a special-purpose processor, or both.

[0148] The memory 920 may store information within the system 900. In some implementations, the memory 920 includes one or more computer-readable media. The memory 920 may include any number of volatile memory units, any number of non-volatile memory units, or both volatile and non-volatile memory units. The memory 920 may include read-only memory, random access memory, or both. In some examples, the memory 920 may be used as active or physical memory by one or more executing software modules.

[0149] The storage device 930 may be configured to provide (e.g., persistent) mass storage for the system 900. In some implementations, the storage device 930 may include one or more computer-readable media. For example, the storage device 930 may include a floppy disk device, a hard disk device, an optical disk device, or a tape device. The storage device 930 may include read-only memory, random access memory, or both. The storage device 930 may include one or more of an internal hard drive, an external hard drive, or a removable drive.

[0150] One or both of memory 920 or storage device(s) 930 may include one or more computer-readable storage media (CRSM). The CRSM may include one or more of electronic storage media, magnetic storage media, optical storage media, magneto-optical storage media, quantum storage media, mechanical computer storage media, etc. The CRSM may provide storage of computer-readable instructions describing data structures, processes, applications, programs, other modules, or other data for operation of system 900. In some implementations, the CRSM may include a data store providing non-transitory storage of computer-readable instructions or other information. The CRSM may be incorporated into system 900 or may be external to system 900. The CRSM may include read-only memory, random-access memory, or both. One or more CRSMs suitable for tangibly embodying computer program instructions and data may include any type of non-volatile memory, including, but not limited to, semiconductor memory devices such as EPROM, EEPROM, flash memory devices, magnetic disks such as internal hard disks and removable disks, magneto-optical disks, and CD-ROM and DVD-ROM disks. In some examples, processor 910 and memory 920 may be supplemented by, or incorporated in, one or more application-specific integrated circuits (ASICs).

[0151] System 900 may include one or more I / O devices 960. I / O devices 960 may include one or more input devices such as a keyboard, mouse, pen, game controller, touch input device, audio input device (e.g., microphone), gesture input device, haptic input device, image or video capture device (e.g., camera), or other device. In some examples, I / O devices 960 may also include one or more output devices such as a display, LEDs, audio output device (e.g., speaker), printer, haptic output device, etc. I / O devices 960 may be physically incorporated into one or more computing devices of system 900 or may be external to one or more computing devices of system 900.

[0152] The system 900 may include one or more I / O interfaces 940 to enable components or modules of the system 900 to control, interface with, or otherwise communicate with I / O devices 960. The I / O interfaces 940 may enable the transfer of information within, outside, or between components of the system 900 through serial, parallel, or other types of communication. For example, the I / O interfaces 940 may conform to a version of the RS-232 standard for serial ports or a version of the IEEE 1284 standard for parallel ports. As another example, the I / O interfaces 940 may be configured to provide connections via Universal Serial Bus (USB) or Ethernet. In some examples, the I / O interfaces 940 may be configured to provide serial connections conforming to a version of the IEEE 1394 standard.

[0153] I / O interface 940 may also include one or more network interfaces that enable communication between computing devices within system 900 or between system 900 and other computing systems connected to a network. A network interface may include one or more network interface controllers (NICs) or other types of transceiver devices configured to send and receive communications over one or more networks using any network protocol.

[0154] The computing devices of system 900 may communicate with each other and with other computing devices using one or more networks. Such networks may include a public network such as the Internet, a private network such as an organizational or personal intranet, or any combination of private and public networks. The networks may include any type of wired or wireless network, including, but not limited to, a local area network (LAN), a wide area network (WAN), a wireless WAN (WWAN), a wireless LAN (WLAN), a mobile communications network (e.g., 3G, 4G, edge, etc.), etc. In some implementations, communications between computing devices may be encrypted or otherwise secured. For example, communications may use one or more public or private encryption keys, ciphers, digital certificates, or other credentials supported by a security protocol such as the Secure Sockets Layer (SSL) or any version of the Transport Layer Security (TLS) protocol.

[0155] System 900 may include any number of any type of computing devices. Computing devices may include, but are not limited to, personal computers, smartphones, tablet computers, wearable computers, embedded computers, mobile gaming devices, e-readers, in-vehicle computers, desktop computers, laptop computers, notebook computers, game consoles, home entertainment devices, network computers, server computers, mainframe computers, distributed computing devices (e.g., cloud computing devices), microcomputers, systems-on-chips (SoCs), systems-in-packages (SiPs), etc. Although examples herein describe computing devices as physical devices, implementations are not limited in this respect. In some examples, a computing device may include one or more virtual computing environments, hypervisors, emulations, or virtual machines running on one or more physical computing devices. In some examples, two or more computing devices may include a cluster, cloud, farm, or other group of multiple devices that coordinate operations to provide load balancing, failover support, parallel processing capabilities, shared storage resources, shared network capabilities, or other aspects.

[0156] The term "configured" is used herein in connection with systems and computer program components. A system of one or more computers configured to perform a particular operation or action means that the system has installed thereon software, firmware, hardware, or a combination thereof that causes the system to perform the operation or action during operation. A computer program or programs configured to perform a particular operation or action means that the program or programs contain instructions that, when executed by a data processing device, cause the device to perform the operation or action.

[0157] Embodiments of the subject matter and functional operations described herein can be implemented in digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware, or one or more combinations thereof, including the structures disclosed herein and their structural equivalents. Embodiments of the subject matter described herein can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory storage medium for execution by or controlling the operation of a data processing apparatus. The computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or one or more combinations thereof. Alternatively, or additionally, the program instructions can be encoded in an artificially generated propagated signal, such as a machine-generated electrical, optical, or electromagnetic signal, generated to encode information for transmission to a suitable receiving device for execution by the data processing apparatus.

[0158] The term "data processing apparatus" refers to data processing hardware and encompasses all kinds of apparatus, devices, and machines for processing data, including, by way of example, a programmable processor, a computer, or multiple processors or computers. An apparatus may also be or include special-purpose logic circuitry, e.g., an FPGA (field-programmable gate array) or an ASIC (application-specific integrated circuit). In addition to hardware, an apparatus may optionally include code that creates an execution environment for a computer program, such as code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of these.

[0159] A computer program, which may also be referred to or described as a program, software, software application, app, module, software module, script, or code, can be written in any style of programming language, including compiled or interpreted, or declarative or procedural, and can be deployed in any style, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A program may, but need not, correspond to a file in a file system. A program can be stored as part of a file that holds other programs or data, e.g., in one or more scripts stored in a markup language document, in a single file dedicated to the program, or in multiple linked files, e.g., files that store one or more modules, subprograms, or portions of code. A computer program can be deployed to be executed on one computer or on multiple computers at one site or distributed across multiple sites and interconnected by a data communications network.

[0160] The term "database" is used broadly herein to refer to any collection of data. The data need not be structured in any particular way, or even structured at all, and can be stored on storage devices in one or more locations. Thus, for example, an index database can contain multiple collections of data, each of which can be organized and accessed in a different way.

[0161] Similarly, the term "engine" is used broadly herein to refer to a software-based system, subsystem, or process programmed to perform one or more specific functions. Typically, an engine is implemented as one or more software modules or components and installed on one or more computers in one or more locations. In some cases, one or more computers are dedicated to a particular engine, and in other cases, multiple engines may be installed and executed on the same computer or multiple identical computers.

[0162] The processes and logic flows described herein may be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows may also be performed by special purpose logic circuitry, e.g., an FPGA or an ASIC, or a combination of special purpose logic circuitry and one or more programmed computers.

[0163] A computer suitable for executing a computer program can be based on a general-purpose or special-purpose microprocessor, or both, or any other type of central processing unit. Typically, the central processing unit receives instructions and data from a read-only memory, a random-access memory, or both. The basic elements of a computer are a central processing unit for executing or carrying out instructions and one or more memory devices for storing instructions and data. The central processing unit and memory may be supplemented by, or incorporated in, special-purpose logic circuitry. Typically, a computer also includes one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or is operatively coupled to receive data from and / or transfer data to these storage devices. However, a computer need not necessarily include such devices. Furthermore, a computer may be incorporated into another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device, such as a universal serial bus (USB) flash drive, to name just a few.

[0164] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, by way of example, semiconductor memory devices such as EPROMs, EEPROMs, flash memory devices, magnetic disks such as internal hard disks or removable disks, magneto-optical disks, and CD-ROM and DVD-ROM disks.

[0165] To provide for user interaction, embodiments of the subject matter described herein can be implemented on a computer that includes a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user, and a keyboard and pointing device, e.g., a mouse or trackball, through which the user can provide input to the computer. Other types of devices can also be used to provide for user interaction; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback, and input from the user can be received in any form, including acoustic, speech, or tactile input. Furthermore, a computer can interact with a user by sending and receiving documents to and from a device used by the user, e.g., by sending a web page to a web browser on the user's device in response to a request received from the web browser. A computer can also interact with a user by sending text messages or other forms of messages to a personal device, e.g., a smartphone running a messaging application, and receiving a response message from the user in return.

[0166] Embodiments of the subject matter described herein can be implemented in a computing system including a back-end component, e.g., as a data server, or a middleware component, e.g., as an application server, or a front-end component, e.g., as a client computer having a graphical user interface, a web browser, or an app through which a user can interact with an implementation of the subject matter described herein, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communications network. Examples of communications networks include local area networks (LANs) and wide area networks (WANs), e.g., the Internet.

[0167] A computing system may include clients and servers. Clients and servers are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server acts as a client, sending data, e.g., HTML pages, to a user device for the purpose of displaying data to a user interacting with the device and receiving user input. Data generated at the user device, e.g., results of user interaction, can be received from the device at the server.

[0168] While this specification contains details of many specific implementations, these should not be construed as limiting the scope of any invention or what may be claimed, but rather as descriptions of features that may be specific to particular embodiments of a particular invention. Certain features described herein in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented in multiple embodiments individually or in any suitable subcombination. Furthermore, even if features may be described above as functioning in a particular combination and originally claimed as such, one or more features from a claimed combination may in some cases be deleted from the combination, and the claimed combination may be directed to a subcombination or variations of the subcombination.

[0169] Similarly, while the figures may depict operations in a particular order, and the claims may describe operations in a particular order, this should not be understood as requiring that such operations be performed in the particular order depicted, or in the sequential order depicted, or that all of the depicted operations be performed, to achieve desirable results. In certain situations, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems may generally be integrated into a single software product or packaged into multiple software products.

[0170] Specific embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. For example, the actions recited in the claims may be performed in a different order and still achieve desirable results. By way of example, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous. [Explanation of symbols]

[0171] 100 Medical Monitoring System 110 Sensor device 112 Heart Sensor 114 Accelerometer 116 Communication Module 118 PCB 120 Electronic Devices 122 Database Module 122a Input data 122b output data 122c Processing Rules 124 Communication Module 126 Processing Module 128 User Interface Module 150 wired or wireless communication links 202 users 204a Points 204b points 300a Plot 300b Plot 300c plot 300d plot 300e Plot 400a Scatter Plot 400b plot 500a plot 500b plot 500c Plot 500d plot 600a Plot 600b plot 600c Plot 600d plot 700a Plot 700b plot 800 processes 900 System 910 processor 920 memory 930 Storage Devices 940 I / O interface 950 System Bus 960 Input / Output (I / O) Devices

Claims

1. A sensor device configured to be attached to a user's body, one or more cardiac sensors; a sensor device comprising one or more acceleration sensors; an electronic device communicatively coupled to the sensor apparatus, causing the sensor device to acquire cardiac data regarding the user during a first period of time using the one or more cardiac sensors; causing the sensor device to obtain acceleration data regarding the user during the first time period using the one or more acceleration sensors; determining first physiological data representative of cardiac activity of the user during the first time period based on the cardiac data; determining second physiological data based on the acceleration data, the second physiological data representing changes in the acceleration data during the time period; determining a biomarker indicative of a health state of the user based on the first physiological data and the second physiological data; storing a data structure representing said biomarkers; an electronic device comprising one or more computer processors configured to cause A medical monitoring system comprising:

2. The medical monitoring system of claim 1 , wherein the biomarkers represent functional capabilities of the user.

3. The medical monitoring system of claim 1 , wherein the biomarkers represent a cardiopulmonary condition of the user.

4. The medical monitoring system of claim 1 , wherein the sensor device is configured to be attached to the user's chest.

5. The medical monitoring system of claim 1 , wherein the first period of time is six minutes.

6. The medical monitoring system of claim 1 , wherein the one or more cardiac sensors comprise an electrocardiogram (ECG) sensor.

7. The medical monitoring system of claim 1 , wherein the first physiological data represents the user's heart rate during the first time period.

8. Determining the first physiological data includes:

8. The medical monitoring system of claim 7, comprising segmenting the cardiac data into a plurality of cardiac data segments, each of the cardiac data segments representing a different respective heart beat of the user.

9. Determining the first physiological data includes: The medical monitoring system of claim 8 , further comprising determining the heart rate of the user during the first time period based on the cardiac data segments.

10. 10. The medical monitoring system of claim 1, wherein the acceleration data comprises a plurality of acceleration signals, each of the acceleration signals representing acceleration in a respective different spatial dimension.

11. Determining the second physiological data includes: segmenting the acceleration data into a plurality of acceleration data segments, each of the acceleration data segments representing movement of the user during a respective different epoch of the first time period; for each of the epochs, determining a change in the acceleration data during that epoch; determining the second physiological data based on the change in the acceleration data during each of the epochs; and The medical monitoring system of claim 10, comprising:

12. The medical monitoring system of claim 11 , wherein each of the epochs corresponds to a different respective time window within the first time period.

13. 13. The medical monitoring system of claim 12, wherein each of the time windows is a 15 second window.

14. Determining the second physiological data includes: for each of the epochs, determining a mean amplitude deviation of the acceleration data during that epoch; summing the mean amplitude deviations of the acceleration data during each of the epochs; The medical monitoring system of claim 11 , comprising:

15. Determining the second physiological data includes:

15. The medical monitoring system of claim 14, comprising determining the logarithm of the sum of the mean amplitude deviation of the acceleration data during each of the epochs.

16. Determining the second physiological data includes:

16. The medical monitoring system of claim 15, comprising determining the second physiological data based on a regression model having as input the logarithm of the sum of the mean amplitude deviation of the acceleration data.

17. The medical monitoring system of claim 1 , wherein determining the biomarker comprises determining a ratio of the first physiological data to the second physiological data.

18. causing the user to present the biomarkers; determining a plurality of ranges of values, each of the ranges of values ​​corresponding to a different health rank; determining that the biomarker is within a first range of the plurality of ranges, the first range corresponding to a first health rank; presenting the first health rank to the user; The medical monitoring system of claim 1 , comprising:

19. causing the user to present the biomarkers; 10. The medical monitoring system of claim 1, comprising presenting a graphical user interface to the user using a display device, the graphical user interface comprising a graphical display element presenting the biomarkers.

20. causing the user to present the biomarkers; The medical monitoring system of claim 1 , comprising presenting an audio notification to the user using the audio speaker, the audio notification indicating the biomarker.

21. the one or more computer processors: determining one or more recommendations for improving the user's health status based on the biomarkers; and causing the one or more recommendations to be presented to the user; and The medical monitoring system of claim 1 , further configured to:

22. the one or more computer processors:

10. The medical monitoring system of claim 1, further configured to prompt the user to walk during the first time period using at least one of a display screen or an audio speaker.

23. the one or more computer processors: causing the sensor device to continuously acquire the cardiac data and the acceleration data during a second time period, the first time period being a subset of the second time period; selecting the first time period based on at least one of the cardiac data or the acceleration data; The medical monitoring system of claim 1 , further configured to:

24. the one or more computer processors: The medical monitoring system of claim 1 , further configured to cause the biomarkers to be presented to at least one of the users or an additional user.

25. using the electronic device to cause one or more cardiac sensors of the sensor apparatus to acquire cardiac data about the user during a first period of time; using the electronic device to cause one or more acceleration sensors of the sensor apparatus to obtain acceleration data about the user during the first time period; determining, by the electronic device, first physiological data representative of cardiac activity of the user during the first time period based on the cardiac data; determining, by the electronic device, second physiological data based on the acceleration data, the second physiological data representing changes in the acceleration data during the time period; determining, by the electronic device, a biomarker representative of a health state of the user based on the first physiological data and the second physiological data; storing, by the electronic device, a data structure representing the biomarkers; A method comprising:

26. at least one processor; a memory communicatively connected to the at least one processor; 26. A system comprising: said memory storing instructions that, when executed by said at least one processor, cause said at least one processor to perform the method of claim 25.

27. 26. One or more non-transitory computer-readable media storing instructions that, when executed by at least one processor, cause the at least one processor to perform the method of claim 25.

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