Medical monitoring system with cardiac sensor and acceleration sensor, method and

Abstract

Medical monitoring systems, methods, and computer program products with cardiac sensors and acceleration sensors are provided. A medical monitoring system includes a sensor device configured to attach to a body of a user and an electronic device communicatively coupled to the sensor device. The sensor device includes a physiological sensor configured to obtain physiological data representative of cardiac activity of a user and an acceleration sensor configured to obtain acceleration data representative of movement of the user. The electronic apparatus comprises a processor configured to: (i) cause the sensor device to obtain physiological data and acceleration data during a time period; (ii) determining a biomarker representing the health of the user based on the physiological data and the acceleration data, wherein the biomarker represents a relationship between a speed of movement of the user and cardiac activity of the user during the time period; (iii) generating a data structure representing the biomarker, and (iv) storing the data structure in a hardware storage device.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. Patent Application No. 18 / 774,177, filed July 16, 2024, the entire contents of which are incorporated herein by reference for all purposes. Technical Field

[0003] This specification generally relates to systems and methods for monitoring a user's medical condition using physiological sensors. Background Technology

[0004] Typically, a user's health can be assessed by measuring one or more physiological characteristics and comparing those characteristics to health references. For example, a user with physiological characteristics that meet or exceed a specific health reference may be in good health, while a user with physiological characteristics that do not meet the reference may be in poor health. Summary of the Invention

[0005] Typically, medical monitoring systems can be used to monitor a user's health and facilitate their treatment.

[0006] In an example implementation, the medical monitoring system includes: a sensor device configured to acquire sensor data representing 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 representing the user's medical condition.

[0007] For example, a medical monitoring system can acquire sensor data while a user is engaged in physical activity, such as walking continuously for a period of time. The sensor data may include measurements of the user's cardiac activity, such as their heart rate during that time. Additionally, the sensor data may include measurements of the user's movement during that time (e.g., acceleration data obtained using one or more accelerometers). Based on these measurements, the medical monitoring system can identify biomarkers representing the user's physical health, such as their functional capacity and / or cardiopulmonary condition. Furthermore, the medical monitoring system can present these biomarkers to the user or another user (e.g., a healthcare provider) to facilitate user intervention and / or monitor the user's health over time.

[0008] In some implementations, the biomarker can be determined at least in part by: (i) determining a median value of an interbeat interval (IBI) of a heartbeat of the user over the period of time and acceleration data of movement of the user over the period of time, and (ii) combining the median value of the IBI and the acceleration data. For example, combining the median value of the IBI and the acceleration data can include multiplying the median value of the IBI and the acceleration data. Further, the value of the biomarker can represent a relative health of the user. For example, over a particular range of values of the biomarker, a higher value can indicate that the user is relatively healthy, while a lower value of the biomarker can indicate that the user is relatively less healthy.

[0009] Further, in at least some implementations, the biomarker obtained by the medical monitoring system can be used as an estimate or approximation of other biomarkers representing health of the user, where in other cases manual input from the user or a third-party observer would be needed to calculate the health of the user.

[0010] For example, in a traditional "six-minute walk test" (6MWT), a user walks continuously during a period of six minutes. Upon completion of the period of time, an observer (e.g., a health care provider in a clinical setting) measures a distance that the user has walked during the period of time, and uses the measured distance as a biomarker representing health of the user. For example, if the user walks a long distance during the period of time, the value of the biomarker is large and indicates that the user is relatively healthy. If the user walks a short distance during the period of time, the value of the biomarker is low and indicates that the user is relatively less healthy.

[0011] However, the traditional "six-minute walk test" can be difficult to perform in practice, and can be subject to variability. For example, a trained observer can not be readily available to administer the test. Without a trained observer, the user can incorrectly administer the test themselves (e.g., incorrectly measure the distance, walk an incorrect amount of time, etc.), which can adversely affect the accuracy and reliability of the results.

[0012] Further, the biomarker of the traditional "six-minute walk test" does not take into account or directly assess the physiological response (e.g., cardiovascular response, heart performance) of the user to the physical activity (e.g., walking) of the user. Rather, as described above, the biomarker of the traditional "six-minute walk test" uses a distance measured during a period of time in a particular setting as a biomarker representing health of the user.

[0013] In contrast, the biomarker obtained by the medical monitoring system described herein considers or directly evaluates a user's physiological response to the user's physical activity. For example, the biomarker considers a correlation between a user's heartbeats and walking speed by combining the user's heart data and acceleration data. For example, the biomarker can represent a walking speed for each heartbeat of the user. Moreover, the biomarker obtained by the medical monitoring system does not require a trained observer or manual input from the user themselves. Thus, a user can use the medical monitoring system to more easily and accurately monitor their health (e.g., in a home or external environment without the supervision of a health care provider).

[0014] The implementations described herein can provide various technical benefits. As an example, the implementations described herein allow a computer system to automatically determine a user's health based on sensor data (e.g., heart data, acceleration data, etc.) without requiring manual feedback from a human. Moreover, the medical monitoring system can provide this functionality by performing computer-specific operations on input data in an objective manner (and in a manner that is not possible for a human to perform), rather than relying on a human interpretation of the input data that can be less suitable for being performed by a computer. Furthermore, the biomarker determined or derived according to the implementations can be used to determine a user's health by directly evaluating a user's physiological response to the user's physical activity.

[0015] As another example, the implementations described herein can be used to facilitate monitoring and treatment of a user, thereby improving the user's health. For example, the medical monitoring system can generate a biomarker representing the user's health and provide the biomarker to the user or another user (e.g., a health care provider). Based on this information, the user or other user can take positive action to improve the user's health, such as conducting further testing to diagnose a medical condition of the user, changing the user's behavior, lifestyle, and / or diet, or any other action.

[0016] In an aspect, a method includes using an electronic device to (i) cause one or more physiological sensors of a sensor apparatus to obtain physiological data representing a user's heart activity during a time period; (ii) cause one or more acceleration sensors of the sensor apparatus to obtain acceleration data representing the user's movement during the time period; (iii) determine, based on the physiological data and the acceleration data, a biomarker representing the user's health, wherein the biomarker represents a relationship between a speed of the user's movement and the user's heart activity during the time period; (iv) generate a data structure representing the biomarker; and (v) store the data structure in a hardware storage device.

[0017] In some embodiments, determining the biomarker based on the physiological data and the acceleration data includes determining an IBI of a heart rate of the user during the time period based on the physiological data, and determining a median IBI of the IBIs during the time period.

[0018] In some embodiments, the one or more physiological sensors include at least one of a photoplethysmography (PPG) sensor and an electrocardiogram (ECG) sensor. For example, the physiological data can be obtained from at least one of a PPG sensor and an ECG sensor. Further, for example, the IBIs can be determined based on at least one of a PPG peak and an R-peak of the physiological data.

[0019] In some embodiments, determining the biomarker based on the physiological data and the acceleration data includes multiplying the median IBI by the acceleration data.

[0020] In some embodiments, generating the data structure includes combining the biomarker with other physiological data of the user. Further, in some embodiments, the electronic device can be operable to cause the biomarker to be presented to the user. For example, causing the biomarker to be presented to the user includes outputting the data structure on a display of a user interface.

[0021] 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. A computer system can be configured to perform particular actions by virtue of having software, firmware, hardware, or a combination thereof installed on the system that in operation

[0022] The particulars of one or more embodiments of the subject matter of this specification 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, the drawings, and the claims. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 FIG. 1 is a diagram of an example medical monitoring system.

[0024] Figure 2A and Figure 2B FIG. 2 is a diagram of an example operation of the medical monitoring system.

[0025] Figure 3 and Figure 4 FIG. 3 is a flowchart of an example process for monitoring health of a user.

[0026] Figure 5 is a diagram of an example computer system.

[0027] Like reference numbers and designations in various drawings indicate like elements. DETAILED DESCRIPTION

[0028] Figure 1 An example medical monitoring system 100 for automatically monitoring a user’s health is shown. The medical monitoring system 100 includes a sensor device 110 and an electronic device 120 communicatively coupled to each other (e.g., via one or more wired or wireless communication links 150). Generally, the medical monitoring system 100 uses the sensor device 110 to obtain sensor data about a user and uses the electronic device 120 to process the sensor data to determine one or more biomarkers indicative of a medical condition of the user.

[0029] The sensor device 110 includes one or more sensors configured to obtain measurements about the user’s physiology, the user’s behavior, and / or any other characteristic of the user.

[0030] For example, as shown, the sensor device 110 can include one or more heart sensors 112 configured to obtain sensor data indicative of the user’s heart activity. In some implementations, the heart sensors 112 can include one or more ECG sensors and / or one or more PPG sensors. In some implementations, the heart sensors 112 can obtain sensor data at 250 Hz (or approximately 250 Hz). Figure 1

[0031] In addition, the sensor device 110 can include one or more accelerometers 114 configured to obtain sensor data indicative of the user’s movement or motion in one or more directions. For example, at least some of the accelerometers 114 can be tri-axial accelerometers configured to measure acceleration in three directions (e.g., an x-direction, a y-direction, and a z-direction on a Cartesian coordinate system). In some implementations, the accelerometers 114 can obtain sensor data at 32 Hz (or approximately 32 Hz).

[0032] In addition, the sensor device 110 includes a communication module 116 configured to transmit data to and / or receive data from the electronic device 120. As an example, the communication module 116 can include one or more receivers, transmitters, and / or transceivers. In some implementations, the communication module 116 can communicate with the electronic device 120 via one or more wired links (e.g., serial links, Ethernet links, etc.) and / or wireless links (e.g., Wi-Fi links, Bluetooth links, etc.).

[0033] ​Further, the sensor device 110 is configured to be worn by a user. For example, as shown in Figure 1 the sensor device 110 can include a substrate 118 configured to be attached to the body of the user (e.g., via an adhesive). Further, the heart sensor 112, the accelerometer 114, and the communication module 116 can be attached to the substrate such that they are secured to the body of the user. In some implementations, at least a portion of the substrate 118 can 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 can be composed of a rigid material, such as a rigid metal, a rigid plastic, and / or the like. In some implementations, the sensor device 110 can be worn by the user without the substrate 118. For example, the sensor device 110 can be arranged on the body of the user by a wearable accessory or article, such as a wristband.

[0034] Generally, the electronic device 120 is configured to receive the sensor data obtained by the sensor device 110 and to process the sensor data to determine one or more biomarkers representative of a medical condition of the user. Further, the electronic device 120 is configured to present information about the biomarkers and any other information to the user and / or another user (e.g., a health care provider).

[0035] Generally, the electronic device 120 can include any number of devices configured to receive, process, and transmit data. Examples of the electronic device 120 include a client computing device (e.g., a desktop computer or a notebook computer), a server computing device (e.g., a server computer or a cloud computing system), a mobile computing device (e.g., a cellular phone, a smartphone, a tablet computer, a personal data assistant, a notebook computer with networking capabilities), a wearable computing device (e.g., a smartphone or a headset), and other computing devices capable of receiving, processing, and transmitting data. In some implementations, the electronic device 120 can include a computing device operating 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).

[0036] In Figure 1In this design, electronic device 120 is illustrated as a single component. However, in practice, electronic device 120 may be implemented on one or more computing devices (e.g., various computing devices including at least one processor (e.g., a microprocessor or microcontroller, etc.)). 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 several computing devices, and components of electronic device 120 may be distributed across one or more of these computing devices.

[0037] Furthermore, electronic device 120 is illustrated as a component separate from sensor device 110. However, while electronic device 120 may be a separate component from sensor device 110, electronic device 120 may also include sensor device 110, be coupled to sensor device 110, or (e.g., in a housing) adjacent to sensor device 110. For example, electronic device 120 may be a wearable device that includes sensor device 110, is coupled to sensor device 110, or is adjacent to sensor device 110.

[0038] like Figure 1 As shown, electronic device 120 includes a database module 122, a communication module 124, a processing module 126, and a user interface module 128. The operation modules can be provided as one or more computer-executable software modules, hardware modules, or a combination thereof. For example, one or more operation modules can be implemented as software code blocks having instructions that cause one or more processors to perform the operations described herein. Alternatively or concurrently, one or more operation modules can be implemented in electronic circuitry, such as programmable logic circuits, field-programmable arrays (FPGAs), or application-specific integrated circuits (ASICs).

[0039] Communication module 124 is used to transmit and / or receive data from sensor device 110. As an example, communication module 124 may include one or more receivers, transmitters, and / or transceivers. In some embodiments, communication module 124 may communicate with sensor device 110 (e.g., via communication module 116) via one or more wired links (e.g., serial links, Ethernet links, etc.) and / or wireless links (e.g., Wi-Fi links, Bluetooth links, etc.).

[0040] Database module 122 maintains information related to the operation of medical monitoring system 100.

[0041] As an example, the database module 122 can store input data 122a that is used as input for determining one or more biomarkers indicative of a user’s health. For example, the input data 122a can include at least some of the sensor data generated by the sensor device 110 (e.g., heart data, acceleration data, etc.).

[0042] 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.

[0043] Further, the database module 122 can store processing rules 122c that specify how data in the database module 122 can be processed for the operations described herein.

[0044] As an example, the processing rules 122c can include one or more rules that specify how to format, parse, and process the input data 122a to determine one or more corresponding metrics or biomarkers about a user.

[0045] As another example, the processing rules 122c can include one or more rules that specify conditions under which to present data to a user (e.g., using the user interface module 128) and the manner in which to present the data.

[0046] As another example, the processing rules 122c can include one or more rules that specify the manner in which to store data (e.g., using the database module 122) for future retrieval and / or processing.

[0047] Example data processing techniques are described in further detail below.

[0048] The processing module 126 processes data stored or otherwise accessible to the electronic device 120. For example, the processing module 126 can be used to perform one or more of the operations described herein (e.g., by executing the processing rules 122c on the input data 112a to generate the output data 122b).

[0049] 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 can 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 enables a user to interact with the electronic device 120 and / or the sensor apparatus 110. Example interactions include viewing data, transferring data from one component to another, and / or issuing commands to the electronic device 120 and / or the sensor apparatus 110. Commands can include, for example, any user instructions to perform a particular operation or task with one or more of the electronic device 120 and / or the sensor apparatus 110. In some embodiments, the user interface module can also present information to a user audibly (e.g., using one or more speakers) and / or via tactile feedback (e.g., using one or more haptic generators, such as vibration generation).

[0050] In some embodiments, a software application can be used to facilitate the performance of the tasks described herein. As an example, an application can be installed on the electronic device 120. Further, a user can interact with the application to input data and / or commands to the electronic device 120 and to review data generated by the electronic device 120.

[0051] Figure 2A and Figure 2B An example embodiment and operation of the medical monitoring system 100 is shown.

[0052] Referring to Figure 2A The sensor apparatus 110 is worn by the user 202 (e.g., attached to the user's skin, such as on the user's chest), for example. Further, the user 202 walks continuously for a period of time. For example, as the user 202 is walking, the sensor apparatus 110 obtains sensor data (e.g., cardiac data, acceleration data, etc.) regarding the user 202 at time intervals (e.g., 10 seconds, 30 seconds, 1 minute, etc.) and transmits at least some of the sensor data to the electronic device 120 for further processing. For example, the sensor data can be obtained at consistent, predefined time intervals, such as every 10 seconds, every 30 seconds, every minute, etc. On the other hand, for example, the sensor data can be obtained at variable time intervals, where the sensor data can be obtained more frequently during certain periods and less frequently during other periods.

[0053] The electronic device 120 receives the sensor data and processes the sensor data to determine a biomarker representative of the health of the user. For example, the electronic device 120 can determine a biomarker representative of the functional capacity of the user (e.g., the ability of the user to perform functions in various situations, such as the ability of the user to perform physical activity) and / or cardiopulmonary status (e.g., the health of the heart, lungs, circulatory system, etc. of the user). Further, the electronic device 120 can present the biomarker to the user 202 or another user (e.g., a health care provider) to facilitate treatment of the user 202 and / or monitoring of the health of the user over time.

[0054] In some implementations, with reference to Figure 2B The electronic device 120 can be a wearable wristband (e.g., a wristwatch, a smartwatch, etc.) that includes the sensor device 110, coupled with or proximate to (e.g., in the same housing as) the sensor device 110. For example, the electronic device 120 can be disposed at the wrist of the user, and the sensor device 110 can measure sensor data regarding the user 202 including cardiac data (e.g., PPG data, ECG data) and acceleration data.

[0055] In some implementations, the medical monitoring system 100 can instruct the user on how to 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 and / or select a particular duration (e.g., a time period or time interval for which a set of sensor data will be obtained to determine a biomarker) (e.g., using a display screen, an audio speaker, etc.). As described above, the medical monitoring system 100 can determine a biomarker based on the sensor data.

[0056] In some implementations, the medical monitoring system 100 can obtain sensor data without requiring a user to input a particular time period. For example, the medical monitoring system 100 can have a default duration of input, where a set of sensor data will be obtained for that default duration to determine a biomarker. Further, for example, multiple sets of sensor data can be obtained based on multiple time periods to determine a biomarker. Further, for example, each biomarker determined for each set of sensor data can be averaged to determine a biomarker for the multiple sets of sensor data. Further, in some implementations, the medical monitoring system 100 can continuously obtain sensor data about a user (e.g., during the course of a day) and automatically select a time period for which to determine a biomarker. For example, the medical monitoring system 100 can determine, based on the sensor data, that the user has been continuously walking during a particular time period. The medical monitoring system 100 can select sensor data from that time period and determine a biomarker based on the selected sensor data. This can be beneficial, for example, because it allows the medical monitoring system 100 to unobtrusively monitor the health of a user over time (e.g., without requiring the user to manually initiate a test and / or without requiring the user to perform a prescribed physical activity).

[0057] In some implementations, the biomarker can be determined at least in part by: (i) determining a median IBI of IBIs of heartbeats of the user over the time period (e.g., time interval) and acceleration data of movements of the user over the time period, and (ii) combining the median IBI and the acceleration data.

[0058] For example, heart data of the user can be obtained using the sensor device 110 over a span of one minute (or each one minute interval), and based on such heart data, R-peaks (e.g., for ECG data) or systolic peaks (e.g., for PPG data) can be extracted using the electronic device 120. Based on the R-peaks or systolic peaks, the number of IBIs and a median IBI of the IBIs can be determined using the electronic device 120. In some implementations, the heart data of the user obtained from the sensor device 110 can include the extracted R-peaks or systolic peaks.

[0059] Further, for example, combining the median IBI and the acceleration data can include multiplying the median IBI and the acceleration data. As an example, the biomarker X can be determined using the following relationship:

[0060] X = median IBI * acceleration (Equation 1),

[0061] where "median IBI" is the median of the plurality of IBIs of heartbeats measured during a period or time interval, and "acceleration" represents the magnitude of the acceleration vector in the x, y, z directions. For example, "acceleration" can be determined by squaring the x, y, and z components of the acceleration vector, and then taking the square root of their sum. Further, for example, "median IBI" can be expressed in units of time per heartbeat (e.g., seconds, milliseconds), and "acceleration" can be expressed in units of average acceleration change of the user's movement over the distance segment (e.g., meters) per time squared (e.g., seconds 2 ) per heartbeat. Further, for example, the biomarker can be expressed in units of speed per heartbeat (e.g., meters per (second * heartbeat)).

[0062] In some implementations, the number of heartbeats used to determine IBI and median IBI can be determined by dividing the cardiac data (e.g., ECG data, PPG data, etc.) into several segments, where each segment corresponds to a respective different single heartbeat. For example, the cardiac data can be segmented by identifying features (e.g., peaks, valleys, etc.) of the cardiac data that are indicative of heartbeats. Further, the number of segments can be counted to determine the total number of heartbeats.

[0063] Further, the value of the biomarker can be indicative of the relative health of the user. For example, over a particular range of values of the biomarker, a higher value can be indicative of the user's health being relatively high, while a lower value of the biomarker can be indicative of the user's health being relatively low. Such a biomarker can be beneficial, for example, and used as an indicator for various purposes in assessing the quality of health of a user, tracking disease progression, predicting outcomes of clinical trials, determining health-related statistics, etc.

[0064] As an illustrative example, during a period of time, a first user and a second user walk in a similar manner. The biomarker of the first user during the period of time is greater than the biomarker of the second user during the period of time, which can be indicative of the functional capacity and / or cardiorespiratory health of the first user being superior to the functional capacity and / or cardiorespiratory health of the second user. This difference is reflected in the biomarkers of the two users, where the value of the biomarker of the first user is greater than the value of the biomarker of the second user.

[0065] As another example, for a set of distances (e.g., 100 m), a first user and a second user walk at different speeds or at the same speed. If the biomarker of the first user during the period of time is greater than the biomarker of the second user during the period of time, this can be indicative of the functional capacity and / or cardiorespiratory health of the first user being superior to the functional capacity and / or cardiorespiratory health of the second user.

[0066] As another example, without being time bound or distance limited, a user walks at certain terrains (e.g., flat terrain, boardwalk, footpath). If the user’s biomarker is greater than a predetermined threshold or is at an average biomarker of multiple biomarker data of different people at similar terrains, this can indicate that the user’s functional capacity and / or cardiopulmonary health is better than average or better than an average of multiple biomarker data of different people in a sample data set. Further, the user’s biomarker can vary between different environmental conditions or controlled conditions (e.g., inclined surface, climbing a hill, specific activity), and the user’s biomarker can be compared to an average or predetermined value corresponding to these corresponding environmental conditions. In some implementations, the user can communicate with the electronic device 120 (e.g., through manual input by touch or voice) to input the environmental conditions or controlled conditions prior to walking or a specific exercise and initiating heartbeat and acceleration data measurement.

[0067] In some implementations, the medical monitoring system 100 can present the value of the biomarker. For example, the value of the biomarker can be presented as a graphical display element on a GUI (e.g., as presented on a display device) and / or an audio notification (e.g., as presented using an audio speaker).

[0068] In some implementations, the medical monitoring system 100 can determine a descriptive health category for the user based on the value of the biomarker. For example, the medical monitoring system 100 can access several health categories (e.g., “good,” “average,” “poor,” etc.), each health category associated with a respective different range of values. The medical monitoring system 100 can determine that the value of the biomarker falls within a particular range of the ranges, and determine that the health of the user corresponds to the category associated with that range. This can be beneficial, for example, because a descriptive health category can be more easily understood by the user than a particular value of the biomarker.

[0069] In some implementations, the medical monitoring system 100 can also determine additional information about the physiology of the user. For example, based on the heart data, the medical monitoring system 100 can determine heart metrics, such as the resting heart rate and peak heart rate of the user and heart rate recovery after walking. As another example, based on the acceleration data, the medical monitoring system 100 can determine gait metrics about the walking pattern of the user. This additional information can also be presented to the user and / or another user (e.g., graphically, auditorily, haptically, etc.).

[0070] In some implementations, the medical monitoring system 100 can be used to facilitate treatment of the user 202, thereby improving the user's health. For example, the medical monitoring system 100 can generate and provide to the user 202 or another user (e.g., a health care provider) a biomarker indicative of the user's health. Based on this information, the user 202 or the other user can take positive action to improve the user's health, such as conducting further testing to diagnose a medical condition of the user, changing the user's behavior, lifestyle, and / or diet, or any other action.

[0071] In some implementations, the medical monitoring system 100 can determine one or more recommendations for improving the user's health based on the biomarker, and cause the recommendations to be presented to the user.

[0072] 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 can provide instructions to the user 202 (e.g., using the electronic device 120) to attach the sensor device 110 to a particular location on the user's body (e.g., the user's chest), and to walk back and forth between two points for a particular length of time (e.g., six minutes). Further, upon completion of the user's walk, the medical monitoring system 100 can provide feedback to the user 202 regarding the sensor data obtained and the biomarker generated based on the sensor data. This can be beneficial, for example, in helping the user 202 conduct a test of their physical health, even if the user 202 does not have any prior experience conducting the test. In some implementations, the instructions can be provided graphically (e.g., using one or more display screens) and / or aurally (e.g., using one or more audio speakers) by the medical monitoring system 100.

[0073] Example Processes

[0074] Figure 3 is a flowchart of an example process 300 for monitoring a user's health using a physiological sensor. The process 300 can be implemented by a processor-based system, such as the medical monitoring system 100 and the system 500 described in the present disclosure.

[0075] At 302, physiological data representative of a user's heart activity is obtained. For example, the physiological data can be obtained by one or more heart sensors (e.g., heart sensors 112) while the user is walking or moving. For example, a processor-based electronic device (e.g., electronic device 120) in communication (e.g., wired or wireless communication) with the one or more heart sensors can be used to cause the one or more heart sensors to obtain the physiological data. In some implementations, the electronic device includes, is coupled with, or is proximate to (e.g., in the same housing as) the one or more heart sensors.

[0076] The physiological data can include or correspond to ECG data or PPG data. For example, the ECG data includes an ECG waveform that includes various peaks and valleys that reflect different stages of a cardiac cycle. In particular, the ECG data includes a QRS complex, and the R-peak of the QRS complex can be used to determine the user's heart rate. Further, for example, the PPG data includes a PPG waveform that includes various peaks and valleys that reflect different stages of a cardiac cycle. In particular, the PPG data includes a systolic peak, which can be used to determine the user's heart rate.

[0077] The physiological data can be obtained at a particular time interval (e.g., 10 seconds, 30 seconds, 1 minute, etc.), and the physiological data is transmitted to the electronic device for further processing. In some implementations, the physiological data can be obtained at a consistent, predefined time interval, such as every 10 seconds, every 30 seconds, every minute, etc. In some implementations, the physiological data can be obtained at a variable time interval, where the physiological data can be obtained more frequently during certain periods and less frequently during other periods. After the physiological data is obtained, the physiological data is transmitted to the electronic device for further processing.

[0078] For example, after receiving or in response to receiving the physiological data, the electronic device can extract R-peaks (in the case that the physiological data includes or corresponds to ECG data). Further, based on these extracted R-peaks, the electronic device can determine inter-beat intervals (IBIs) of the user’s heartbeats during the time intervals. In some embodiments, the R-peaks and / or IBIs can be extracted from one or more heart sensors, and the electronic device can receive the extracted R-peaks and / or IBIs of the user. In some embodiments, in the case that the physiological data includes or corresponds to PPG data, the electronic device can extract PPG peaks or systolic peaks of PPG peaks after receiving or in response to receiving the physiological data. Further, based on these extracted PPG peaks or systolic peaks of PPG peaks, the electronic device can determine IBIs of the user’s heartbeats during the time intervals. In some embodiments, the systolic peaks and / or IBIs can be extracted from one or more heart sensors, and the electronic device can receive the extracted systolic peaks and / or IBIs of the user.

[0079] For example, the processor (e.g., the one or more processors) of the electronic device described in Figure 1 may be configured to track the physiological data obtained at the predefined time intervals and determine R-peaks and / or IBIs over one or more of the predefined time intervals.

[0080] At 304, acceleration data representing movement of the user is obtained. For example, the acceleration data can be obtained by one or more accelerometers (e.g., the accelerometers 114) when the user is walking or moving. For example, an electronic device in communication (e.g., wired or wireless communication) with the one or more accelerometers can be used to cause the one or more accelerometers to obtain the acceleration data. In some embodiments, the electronic device includes, is coupled with, or is proximate to (e.g., in the same housing as) the one or more accelerometers.

[0081] For example, the acceleration data can include or represent an acceleration or acceleration vector of the user in three directions (e.g., x-direction, y-direction, and z-direction on a Cartesian coordinate system) when the user is walking or moving. For example, in a similar manner to the physiological data, the acceleration data can be obtained at certain time intervals (e.g., 10 seconds, 30 seconds, 1 minute, etc.) and transmitted to the electronic device for further processing. In some implementations, the acceleration data can be obtained at consistent, predefined time intervals, e.g., every 10 seconds, every 30 seconds, every minute, etc. In some implementations, the acceleration data can be obtained at variable time intervals, where the acceleration data can be obtained more frequently during certain periods and less frequently during other periods. After obtaining the physiological data, the physiological data is transmitted to the electronic device for further processing.

[0082] For example, after receiving or in response to receiving the acceleration data, the electronic device can determine an acceleration of the user by squaring the x, y, and z components of the acceleration data or vector and then taking the square root of their sum. In some implementations, the acceleration of the user can be determined at one or more accelerometers and the electronic device can receive data representing the acceleration of the user.

[0083] In some implementations, after receiving or in response to receiving the acceleration data, and prior to squaring the x, y, and z components of the acceleration data or vector, the electronic device can apply a bandpass filter independently to the received acceleration data or vector for each axis (e.g., x, y, z axis) to remove bias and isolate the acceleration components. After filtering, the x, y, and z components of the bandpass filtered acceleration data corresponding to the x, y, and z axes, respectively, can be squared, summed, and then the square root of their sum can be taken to determine the acceleration of the user.

[0084] At 306, a biomarker representing a health of the user is determined. Specifically, the biomarker can be determined based on the physiological data and the acceleration data. For example, determining the biomarker can include determining an IBI of a heart rate of the user during a time period (e.g., a time interval) based on the physiological data and by the electronic device, and determining a median IBI among the plurality of IBIs during the time period. For example, the IBI is determined based on the extracted R-peaks of the physiological data (in the case that the physiological data includes or corresponds to ECG data). In another example, the IBI is determined based on the PPG peaks or extracted systolic peaks of the physiological data (in the case that the physiological data includes or corresponds to PPG data).

[0085] Further, determining the biomarker can include multiplying the median IBI by the acceleration data. For example, determining the biomarker can include multiplying the median IBI by the acceleration of the user. For example, as described above with respect to the discussion of Equation 1, the acceleration can correspond to acceleration determined based on application of a bandpass filter.

[0086] As described above with respect to the discussion of Equation 1, for example, the biomarker can be expressed in units of speed per heartbeat (e.g., meters per (second * heartbeat)).

[0087] Further, as described above, the value of the biomarker can be indicative of the relative health of the user. For example, over a particular range of values of the biomarker, higher values can be indicative of the user being relatively healthy, while lower values of the biomarker can be indicative of the user being relatively less healthy.

[0088] In some implementations, multiple sets of sensor data (including physiological data and acceleration data) can be obtained to determine the biomarker. For example, multiple sets of sensor data can be obtained at multiple time intervals that cumulatively constitute or form a portion of a period of time, and a biomarker for the period of time can be determined. For example, each biomarker determined for a respective set of sensor data for a respective time interval can be averaged to determine a biomarker for the multiple sets of sensor data.

[0089] At 308, a data structure representing the biomarker is generated. For example, the electronic device can format the data into a standardized data format, such as a JavaScript Object Notation (JSON) or Extensible Markup Language (XML) format. The standardized data format can be other appropriate data format suitable for storage in a data store or hardware storage device.

[0090] At 310, the data structure is stored in a hardware storage device. For example, the data structure can be stored in one or more storage devices (e.g., one or more storage devices 530, a data store, etc.).

[0091] In some implementations, instead of or in addition to storing the data structure in a hardware storage device, the data structure can be output to a user interface (e.g., using user interface module 128). For example, in cases where the data structure is output for display to a user without storing the data structure, the data structure can be generated at step 308 in other formats suitable for display. For example, in cases where the data structure is output for display to a user after storing the data structure, the data structure can be converted from a storage format to an appropriate display format and output.

[0092] Figure 4is a flowchart of an example process 400 for monitoring a user's health using physiological sensors. Process 400 can be implemented by a processor-based system, such as medical monitoring system 100 and system 500, and can be viewed in conjunction with process 300.

[0093] At 402, physiological data representing a user's heart activity is obtained. For example, the physiological data can be obtained by one or more heart sensors (e.g., heart sensors 112) while the user is walking or moving. The techniques used in step 402 can be the same as the techniques used in step 302 of process 300, and thus are not repeated here. Figure 3

[0094] At 404, acceleration data representing the user's movement can be obtained. For example, the acceleration data can be obtained by one or more accelerometers (e.g., accelerometers 114) while the user is walking or moving. The techniques used in step 404 can be the same as the techniques used in step 304 of process 300, and thus are not repeated here. Figure 3

[0095] At 406, IBIs of the user's heartbeats are determined. For example, based on the physiological data (e.g., ECG data, PPG data) including a waveform containing various peaks and valleys reflecting different phases of a cardiac cycle, R-peaks (for ECG data) or systolic peaks (for PPG data) are extracted by an electronic device (e.g., electronic device 120). For example, based on the extracted R-peaks or systolic peaks, IBIs of the user's heartbeats over a particular time period are determined.

[0096] At 408, a median IBI of the determined (or extracted) IBIs is determined by the electronic device. In some implementations, instead of a median IBI, an average IBI of the IBIs can be determined.

[0097] At 410, the median IBI and the acceleration data are combined to determine a biomarker. For example, combining the median IBI and the acceleration data can include multiplying the median IBI by an acceleration of the acceleration data. For example, as described above with respect to Equation 1, the acceleration can be determined by squaring the x, y, and z components of the acceleration vector, and then taking the square root of their sum. Further, for example, the median IBI can be represented in units of time (e.g., seconds, milliseconds) per heartbeat, and the acceleration can be represented in units of average acceleration change (e.g., meters per second squared) over a distance segment (e.g., meters) of the user's movement. 2 Further, for example, the biomarker can be represented in units of speed (e.g., meters per second per heartbeat).

[0098] ​​At 412, a data structure representing the biomarker is generated. For example, the electronic device can format the data into a standardized data format, such as JavaScript Object Notation (JSON) or Extensible Markup Language (XML) format. The standardized data format can be other suitable data format suitable for storage in a data store.

[0099] At 414, the data structure is stored in a hardware storage device. For example, the data structure can be stored in one or more storage devices (e.g., one or more storage devices 530, a data store, etc.). Since the techniques used in step 414 can be the same as the techniques used in step 310 of FIG. 3, the techniques are not repeated here. Figure 3

[0100] Example Computer System

[0101] Figure 5 An example computing system in accordance with implementations of the present disclosure is depicted. The system 500 can be used for any of the operations described with respect to the various implementations discussed herein. The system 500 can be included in, used by, in communication with, or correspond to the electronic device 120. Further, the system 500 can include, be used by, or be in communication with the sensor device 110. The system 500 can include one or more processors 510, memory 520, one or more storage devices 530, and one or more input / output (I / O) devices 560, which can be controlled by one or more I / O interfaces 540. The various components 510, 520, 530, 540, or 560 can be interconnected by at least one system bus 550, which can enable communication of data among the various modules and components of the system 500.

[0102] The processor(s) 510 can be configured to process instructions for execution within the system 500. The processor(s) 510 can include single-threaded processor(s), multi-threaded processor(s), or both. The processor(s) 510 can be configured to process instructions stored in the memory 520 or on the storage device(s) 530. The processor(s) 510 can include hardware-based processor(s) each including one or more cores. The processor(s) 510 can include general-purpose processor(s), special-purpose processor(s), or both.

[0103] ​Memory 520 can store information within system 500. In some implementations, memory 520 includes one or more computer-readable media. Memory 520 can include any type of long-term, short-term, or both physical storage of information. In some examples, memory 520 can be used to store program instructions for execution by one or more of the processors 510. Memory 520 can include only memories, or only storage components, or both, and can take any

[0104] Storage device(s) 530 can be configured to provide mass storage for system 500. In some implementations, storage device(s) 530 can include one or more computer- readable media. For example, storage device(s) 530 can include a floppy disk device, a hard disk device, an optical disk device, or a tape device. Storage device(s) 530 can include memory, such as readonly memory, random-access memory, or both. Storage device(s) 530 can include one or more of an internal hard disk drive, an external hard disk drive, and a removable drive.

[0105] One or both of memory 520 or storage device(s) 530 can include one or more computer-readable storage media (CRSM). CRSM can include one or more of electronic storage media, magnetic storage media, optical storage media, quantum storage media, mechanical computer storage media, or the like. CRSM can provide storage of computer-readable instructions describing data structures, processes, applications, programs, other modules, or other data for system 500. In some implementations, CRSM can include data storage that provides storage of computer-readable instructions or other information in a non-transitory format. CRSM can be incorporated into system 500 or external to system 500. CRSM can include read-only memory, random-access memory, or both. One or more CRSM suitable for embodying computer program instructions and data can include any type of non-volatile storage, including, but not limited to: semiconductor storage, such as EPROM, EEPROM, and flash devices; magnetic storage, such as internal hard disks and removable disks; magneto-optical storage; and CD-ROM and DVD-ROM disks. In some examples, processor(s) 510 and memory 520 can be supplemented by, or incorporated into, one or more application-specific integrated circuits (ASICs).

[0106] System 500 can include one or more I / O devices 560. I / O device(s) 560 can include one or more input devices, such as a keyboard, a mouse, a pen, a game controller, a touch input device, an audio input device (e.g., a microphone), a gesture input device, a haptic input device, an image or video capture device (e.g., a camera), or other devices. In some examples, I / O device(s) 560 can also include one or more output devices, such as a display, LED(s), an audio output device (e.g., a speaker), a printer, and a haptic output device. I / O device(s) 560 can be physically incorporated into one or more computing devices of system 500, or can be external with respect to one or more computing devices of system 500.

[0107] System 500 can include one or more I / O interfaces 540 to enable components or modules of system 500 to control, interact with, or otherwise communicate with I / O device(s) 560. I / O interface(s) 540 can enable information to be communicated in or out of system 500 or between components of system 500 through serial, parallel, or other types of communication. For example, I / O interface(s) 540 can 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, I / O interface(s) 540 can be configured to provide connectivity through a universal serial bus (USB) or Ethernet. In some examples, I / O interface(s) 540 can be configured to provide serial connectivity conforming to a version of the IEEE 1394 standard.

[0108] I / O interface(s) 540 can also include one or more network interfaces that enable communication between computing devices in system 500, or between system 500 and other network-connected computing systems. Network interface(s) can 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 networking protocol.

[0109] The computing devices of system 500 can communicate with each other or with other computing devices using one or more networks. Such networks can include public networks such as the Internet, private networks such as an institutional or personal intranet, or any combination of private and public networks. Networks can include any type of wired or wireless network, including but not limited to local area networks (LANs), wide area networks (WANs), wireless local area networks (WWANs), wireless LANs (WLANs), and mobile telecommunications networks (e.g., 3G, 4G, Edge, etc.) and the like. In some embodiments, communications between computing devices can be encrypted or otherwise secured. For example, communications can employ one or more public or private encryption keys, passwords, digital certificates, or other credentials supported by a security protocol such as any version of the Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocol.

[0110] System 500 can include any number of any type of computing device. The computing device(s) can include, but are not limited to, personal computers, smart phones, tablet computers, wearable computers, implantable computers, mobile gaming devices, e-book readers, automobile 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, system on a chip (SoC), and system in a package (SiP), and the like. Although examples herein can describe the computing device(s) as physical device(s), implementations are not so limited. In some examples, a computing device can include one or more of a virtual computing environment, hypervisor, emulation, or virtual machine executing on one or more physical computing devices. In some examples, two or more computing devices can include a cluster, cloud, farm, or other grouping of multiple devices that coordinate operation to provide load balancing, failover support, parallel processing capability, shared storage resources, shared networking capability, or other aspects.

[0111] This specification uses the term“configured to” in connection with systems and computer program components. For a system of one or more computers to be configured to perform particular operations or actions, that system has installed on it software, firmware, hardware, or a combination of them that in operation cause the system to perform the operations or actions. For one or more computer programs to be configured to perform particular operations or actions, that one or more programs include instructions that in operation cause a data processing apparatus to perform the operations or actions.

[0112] Embodiments of the subject matter and the functional operations described in this specification can be implemented in digital electronic circuitry, in tangibly-embodied computer software or firmware, in computer hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification 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 to control the operation of, 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 a combination of one or more of them. Alternatively or additionally, the program instructions can be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus.

[0113] 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. The apparatus can also be, or further include, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). The apparatus can optionally include, in addition to hardware, code that creates an execution environment for computer programs, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.

[0114] A computer program (which can also be referred to or described as a program, software, a software application, an app, a module, a software module, a script, or code) can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages; and it can be deployed in any form, 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 in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and

[0115] In this specification, the term“database” is used broadly to refer to any collection of data: the data does not need to be structured in any particular way, or structured at all, and can be stored on storage devices in one or more locations. Thus, for example, an index database can include multiple collections of data, each of which can be organized and accessed differently.

[0116] Similarly, in this specification, the term“engine” is used broadly to refer to a software-based system, subsystem, or process that is programmed to perform one or more specific functions. Generally, an engine will be implemented as one or more software modules or components that are installed on one or more computers in one or more locations. In some cases, one or more computers will be dedicated to a particular engine; in other cases, multiple engines can be installed and running on the same computer or computers.

[0117] The processes and logic flows described in this specification can 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 can also be performed by special purpose logic circuitry, e.g., an FPGA or an ASIC, or by a combination of special purpose logic circuitry and one or more programmed computers.

[0118] Computers suitable for the execution of a computer program can be based on general or special purpose microprocessors or any other kind of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data. The central processing unit and the memory can be supplemented by, or incorporated in, special purpose logic circuitry. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, 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, e.g., a universal serial bus (USB) flash drive, to name just a few.

[0119] 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, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0120] To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having 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 a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; 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. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user’s device in response to requests received from the web browser. Also, a computer can interact with a user by sending text messages or other forms of message to a personal device, e.g., a smartphone that is running a messaging application, and receiving responsive messages from the user in return.

[0121] Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., 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 in this specification, 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 communication network. Examples of communication networks include a local area network (LAN) and a wide area network (WAN), e.g., the Internet.

[0122] The computing system can include clients and servers. A client and server are generally 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 transmits data, e.g., an HTML page, to a user device, e.g., for purposes of displaying data to and receiving user input from a user interacting with the device, which acts as a client. Data generated at the user device, e.g., a result of the user interaction, can be received at the server from the device.

[0123] While this specification contains many specific implementation details, these should not be construed as limitations on the scope or range of any invention or on what can be claimed, but rather as descriptions of particular embodiments of certain inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination with each other. Similarly, features described in the context of one

[0124] Similarly, while operations are depicted in the drawings and recited in the claims in a particular order, this should not be understood as requiring such order nor

[0125] Particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. For example, the acts recited in the claims can be performed in a different order and still achieve desirable results. As one example, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In some instances, multitasking and parallel processing can be advantageous.

Claims

1. A medical monitoring system, comprising: a sensor device configured to be attached to a body of a user, wherein the sensor device comprises: one or more physiological sensors configured to obtain physiological data representative of a heart activity of the user, and one or more acceleration sensors configured to obtain acceleration data representative of a movement of the user; and an electronic apparatus communicatively coupled to the sensor device, wherein the electronic apparatus comprises one or more processors configured to: cause the sensor device to obtain the physiological data and the acceleration data during a time period, determine, based on the physiological data and the acceleration data, a biomarker representative of a health of the user, wherein the biomarker is representative of a relationship between a speed of the movement of the user and the heart activity of the user during the time period, generate a data structure representative of the biomarker, and store the data structure in a hardware storage.

2. The medical monitoring system of claim 1, wherein, the biomarker is representative of a cardiopulmonary condition of the user.

3. The medical monitoring system of claim 1, wherein, the biomarker is related to a walking speed of the user and the heart activity of the user.

4. The medical monitoring system of claim 1, wherein, the one or more physiological sensors comprise an electrocardiogram sensor, ECG sensor.

5. The medical monitoring system of claim 1, wherein, the one or more physiological sensors are configured to be fixed to at least one of a chest of the user and a wrist of the user.

6. The medical monitoring system of claim 1, wherein, the one or more physiological sensors comprise a photoplethysmography sensor, PPG sensor.

7. The medical monitoring system of claim 1, wherein, the one or more acceleration sensors comprise a tri-axial accelerometer configured to measure acceleration in three dimensions.

8. The medical monitoring system of claim 1, wherein, determining the biomarker comprises: determining, based on the physiological data, a plurality of inter-beat intervals, IBIs, of a heart rate of the user during the time period; and determining a median IBI of the plurality of IBIs during the time period.

9. The medical monitoring system of claim 8, wherein, the one or more physiological sensors comprise at least one of a photoplethysmography sensor, PPG sensor, and an electrocardiogram sensor, ECG sensor, and wherein the plurality of IBIs are determined based on at least one of PPG peaks and R-peaks of the physiological data.

10. The medical monitoring system of claim 8, wherein, determining the biomarker comprises: multiplying the median IBI by the acceleration data.

11. The medical monitoring system of claim 1, wherein, the one or more processors are configured to: cause the biomarker to be presented to the user.

12. The medical monitoring system of claim 11, wherein, causing the biomarker to be presented to the user comprises: outputting the data structure on a display of a user interface.

13. The medical monitoring system of claim 1, wherein, generating the data structure comprises: combining the biomarker with other physiological data of the user.

14. A method for medical monitoring, comprising: causing, using an electronic apparatus, one or more physiological sensors of a sensor device to obtain physiological data representative of a heart activity of a user during a time period; causing, using the electronic apparatus, one or more acceleration sensors of the sensor device to obtain acceleration data representative of a movement of the user during the time period; determining, by the electronic device and based on the physiological data and the acceleration data, a biomarker representing a health of the user, wherein the biomarker represents a relationship between a speed of the movement of the user and the heart activity of the user during the time period; generating, by the electronic device, a data structure representing the biomarker; and storing, by the electronic device, the data structure in a hardware storage.

15. The method of claim 14, wherein, determining the biomarker based on the physiological data and the acceleration data comprises: determining, based on the physiological data, a plurality of interbeat intervals, IBIs, of a heart rate of the user during the time period; and determining a median IBI of the plurality of IBIs during the time period.

16. The method of claim 15, wherein, the one or more physiological sensors comprise at least one of a photoplethysmography sensor, PPG sensor, and an electrocardiogram sensor, ECG sensor, wherein the physiological data is obtained from at least one of the photoplethysmography sensor, PPG sensor, and the electrocardiogram sensor, ECG sensor, and wherein the plurality of IBIs is determined based on at least one of PPG peaks and R-peaks of the physiological data.

17. The method of claim 15, wherein, determining the biomarker based on the physiological data and the acceleration data comprises: multiplying the median IBI by the acceleration data.

18. The method of claim 14, wherein, generating the data structure comprises: combining the biomarker with other physiological data of the user.

19. The method of claim 14, further comprising: presenting, using the electronic device, the biomarker to the user.

20. The method of claim 19, wherein, presenting the biomarker to the user comprises: outputting the data structure on a display of a user interface.

21. A computer program product having stored instructions which, when executed by at least one processor, cause the at least one processor to perform the method of any one of claims 14-20.

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