Medical monitoring system with cardiac and acceleration sensors

Abstract

To provide a medical monitoring system.SOLUTION: The medical monitoring system includes a sensor apparatus configured to be attached to a body of a user and an electronic device communicatively coupled to the sensor apparatus. The sensor device includes a physiological sensor for acquiring physiological data representing cardiac activity of the user and an acceleration sensor for acquiring acceleration data representing motion of the user. The electronic device includes a processor configured to (i) cause the sensor arrangement to obtain physiological data and acceleration data during a time period, (ii) determine a biomarker representative of a health condition of the user based on the physiological data and the acceleration data, wherein the biomarker is representative of a relationship between a rate of movement of the user and a cardiac activity of the user during the time period, (iii) generate a data structure representative of the biomarker, and (iv) store the data structure in a hardware storage device.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS 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.

[0002] 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]

[0003] 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 health standards. For example, a user having physiological characteristics that meet or exceed a particular health standard may be in good health, while a user having physiological characteristics that do not meet the health standard may be in poor health. Summary of the Invention

[0004] Generally, medical monitoring systems can be used to monitor the health status of a user and facilitate treatment for the user.

[0005] 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.

[0006] For example, the medical monitoring system can acquire sensor data while the user is engaged in a physical effort, such as while the user is walking continuously for a period of time. The sensor data can include measurements of the user's cardiac activity, such as the user's heart rate during the period of time. 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 treatment of the user and / or monitoring of the user's health over time.

[0007] In some implementations, the biomarker can be determined, at least in part, by (i) determining a median interbeat interval (IBI) of a user's heartbeat over a time period and acceleration data of the user's movement over that time period, and (ii) combining the median IBI with the acceleration data. For example, combining the median IBI with the acceleration data can include multiplying the median IBI with the acceleration data. Furthermore, the value of the biomarker can represent the user's relative health status. For example, within a particular range of values ​​of the biomarker, a higher value can indicate a relatively higher health status of the user, and a lower value of the biomarker can indicate a relatively lower health status of the user.

[0008] 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.

[0009] 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.

[0010] 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.

[0011] Furthermore, conventional "6-minute walk test" biomarkers do not consider or directly assess a user's physiological response (e.g., cardiovascular response, cardiac function) to the user's physical exercise (e.g., walking). Rather, as discussed above, conventional "6-minute walk test" biomarkers use distance measured over a specific, fixed period of time as a biomarker representing the user's health status.

[0012] In contrast, the biomarkers acquired by the medical monitoring system described herein consider or directly evaluate a user's physiological response to the user's physical exercise. For example, the biomarkers consider the correlation between a user's heart rate and walking speed by combining the user's cardiac data and acceleration data. For example, the biomarkers can represent the user's walking speed per heart rate. Furthermore, the biomarkers acquired by the medical monitoring system do not require manual input by a trained observer or the user themselves. Therefore, users can use the medical monitoring system (e.g., at home or in an outdoor environment, without the supervision of a healthcare provider) to more easily and accurately monitor their health status.

[0013] 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 not feasible for a human to perform) rather than relying on human interpretation of the input data, which may be poorly suited for computer implementation. Furthermore, biomarkers derived or determined from implementations can be used to determine a user's health status by directly assessing the user's physiological response to the user's physical exertion.

[0014] 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.

[0015] In one aspect, a method includes using an electronic device to (i) cause one or more physiological sensors of a sensor device to acquire physiological data representative of a user's cardiac activity during a time period; (ii) cause one or more acceleration sensors of the sensor device to acquire acceleration data representative of the user's movement during the time period; (iii) determine biomarkers representative of the user's health status based on the physiological data and the acceleration data, wherein the biomarkers represent a relationship between the user's speed of movement and the user's cardiac activity during the time period; (iv) generate a data structure representative of the biomarkers; and (v) store the data structure in a hardware storage device.

[0016] In some implementations, determining the biomarker based on the physiological data and the acceleration data includes determining an IBI of the user's heart rate during the time period based on the physiological data, and determining a median IBI from among the IBIs during the time period.

[0017] In some implementations, the one or more physiological sensors include at least one of a photoplethysmography (PPG) sensor or an electrocardiogram (ECG) sensor. For example, the physiological data can be obtained from at least one of a PPG sensor or an ECG sensor. Further, for example, the IBI can be determined based on at least one of a PPG peak or an R peak of the physiological data.

[0018] In some implementations, determining the biomarker based on the physiological data and the acceleration data includes multiplying the median IBI and the acceleration data.

[0019] In some implementations, generating the data structure includes combining the biomarkers with other physiological data of the user. Further, in some implementations, an electronic device can be used to present the biomarkers to the user. For example, presenting the biomarkers to the user includes outputting the data structure on a display of a user interface.

[0020] 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.

[0021] 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]

[0022] [Figure 1] FIG. 1 illustrates an exemplary medical monitoring system. [Figure 2A] FIG. 1 illustrates an exemplary operation of a medical monitoring system. [Figure 2B] FIG. 1 illustrates an exemplary operation of a medical monitoring system. [Figure 3] FIG. 1 is a flowchart diagram of an exemplary process for monitoring a user's health status. [Figure 4] FIG. 1 is a flowchart diagram of an exemplary process for monitoring a user's health status. [Figure 5] FIG. 1 is a diagram of an exemplary computer system. DETAILED DESCRIPTION OF THE INVENTION

[0023] Like reference numbers and designations in the various drawings indicate like elements.

[0024] 1 illustrates an exemplary medical monitoring system 100 for automatically monitoring a user's health condition. 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). In general, the medical monitoring system 100 acquires sensor data about the 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.

[0025] 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.

[0026] 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 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).

[0027] 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).

[0028] 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.).

[0029] 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). Furthermore, 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 hard material such as a hard metal, a hard plastic, or the like. In some implementations, the sensor device 110 may be worn by a user without the substrate 118. For example, the sensor device 110 may be placed on the user's body through a wearable accessory or item (e.g., a wristband).

[0030] 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).

[0031] 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).

[0032] 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.

[0033] Additionally, electronic device 120 is shown as a component that is a separate component from sensor device 110. However, while electronic device 120 can be a separate component from sensor device 110, electronic device 120 can also include, be coupled to, or be adjacent to (e.g., within a housing for) sensor device 110. For example, electronic device 120 can be a wearable device that includes, is coupled to, or is adjacent to sensor device 110.

[0034] 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).

[0035] 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 wired links (e.g., serial links, Ethernet links, etc.) and / or wireless links (e.g., Wi-Fi links, Bluetooth links, etc.).

[0036] The database module 122 maintains information related to the operation of the medical monitoring system 100 .

[0037] 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.).

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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).

[0043] Exemplary data processing techniques are described in further detail below.

[0044] 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).

[0045] 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).

[0046] 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.

[0047] 2A and 2B illustrate an exemplary implementation and operation of the medical monitoring system 100. As shown in FIG.

[0048] 2A , the sensor device 110 is worn by the user 202 (e.g., attached to the user's skin, such as the user's chest). Furthermore, the user 202 continuously walks for a period of time. For example, when the user 202 is walking, the sensor device 110 acquires sensor data (e.g., cardiac data, acceleration data, etc.) about the user 202 at specific time intervals (e.g., 10 seconds, 30 seconds, 1 minute, etc.) and transmits at least a portion of the sensor data to the electronic device 120 for further processing. For example, the sensor data can be acquired at fixed predefined time intervals, such as every 10 seconds, every 30 seconds, or every minute. On the other hand, for example, the sensor data can be acquired at variable time intervals, where the sensor data is acquired more frequently during certain periods and less frequently during other periods.

[0049] The electronic device 120 receives the sensor data and processes the sensor data to determine biomarkers indicative of the user's health status. 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., related to physical activity) and / or cardiopulmonary status (e.g., the health status 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 status over time.

[0050] 2B , electronic device 120 may be a wearable wristband (e.g., a wristwatch, a smartwatch, etc.) that includes, is coupled to, or is adjacent to (e.g., within the same housing as) sensor device 110. For example, electronic device 120 may be placed on a user's wrist, and sensor device 110 may measure sensor data about user 202, including cardiac data (e.g., PPG data, ECG data) and acceleration data.

[0051] 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 the body (e.g., using a display screen, audio speaker, etc.) and / or to select a particular duration (e.g., a time period or interval over which a set of sensor data is acquired to determine biomarkers). As described above, the medical monitoring system 100 can determine biomarkers based on the sensor data.

[0052] In some implementations, the medical monitoring system 100 can acquire sensor data without requiring a user to input a specific time period. For example, the medical monitoring system 100 may be input with a default duration for which a set of sensor data is acquired to determine biomarkers. Further, for example, multiple sets of sensor data can be acquired based on multiple time periods of duration to determine biomarkers. Further, for example, each biomarker determined for each set of sensor data can be averaged to determine biomarkers for the multiple sets of sensor data. Furthermore, in some implementations, the medical monitoring system 100 can continuously acquire sensor data about a 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 was walking continuously during a specific time period. 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 a user's health status over time (e.g., without the user having to manually initiate a test and / or without the user having to perform a prescribed physical activity).

[0053] In some implementations, the biomarker can be determined, at least in part, by (i) determining a median IBI from among the IBIs of the user's heart rate over a time period (e.g., a time interval) and acceleration data of the user's movement over that time period, and (ii) combining the median IBI with the acceleration data.

[0054] For example, sensor device 110 may be used to acquire cardiac data of a user over a one-minute period (or for each one-minute interval), and based on such cardiac data, electronic device 120 may be used to extract R peaks (e.g., in the case of ECG data) or systolic peaks (e.g., in the case of PPG data). Electronic device 120 may be used to determine the number of IBIs, as well as a median IBI among the IBIs, based on the R peaks or systolic peaks. In some implementations, the user's cardiac data acquired from sensor device 110 may include the extracted R peaks or systolic peaks.

[0055] Further, for example, combining the median IBI and the acceleration data may include multiplying the median IBI and the acceleration data. By way of example, the biomarker X may be determined using the following relationship: X = Median IBI * Acceleration (Equation 1) where median IBI is the median of multiple IBIs of heartbeats measured during a time period or interval, and acceleration represents the magnitude of the acceleration vector in the x, y, and 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 (e.g., seconds, milliseconds) per heartbeat, and acceleration can represent the change in average acceleration of the user's movement over that period of distance (e.g., meters) in units of time squared (e.g., seconds squared). Further, for example, biomarker X can be expressed in velocity per heartbeat (e.g., meters / (seconds * heartbeats)).

[0056] In some implementations, the heart rate and median IBI used to determine the IBI can be determined by dividing cardiac data (e.g., ECG data, PPG data, etc.) 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, troughs, etc.) that are indicative of a heartbeat. Further, the number of segments can be counted to determine the total number of heartbeats.

[0057] Furthermore, the value of a biomarker can represent the relative health status of a user. For example, within a particular range of values ​​for a biomarker, a higher value can indicate a relatively better health status of the user, while a lower value for the biomarker can indicate a relatively poorer health status of the user. For example, such biomarkers can be useful and can be used as indicators for various purposes, such as assessing the quality of a user's health, tracking the progression of a disease, predicting the results of clinical trials, and determining health-related statistics.

[0058] As an example, during a time period, a first user and a second user walk in a similar manner. During the time period, the biomarkers of the first user are higher than the biomarkers of the second user, which may indicate that the first user has better functional capacity and / or cardiopulmonary fitness than the second user. This difference is reflected in the biomarkers of the two users, with the first user's biomarker values ​​being higher than the second user's biomarker values.

[0059] As another example, for illustrative purposes, over a set distance (e.g., 100 meters), a first user and a second user walk at different speeds or at the same speed. If the biomarkers of the first user during a time period are higher than the biomarkers of the second user, this may indicate that the first user has better functional capacity and / or cardiopulmonary fitness than the second user.

[0060] As another example, a user walks on a particular terrain (e.g., flat terrain, a boardwalk, a promenade) without time or distance constraints. If the user's biomarkers are higher than a predetermined threshold or an average biomarker among multiple biomarker data of different people on similar terrain, this may indicate that the user's functional capacity and / or cardiopulmonary health is better than that of an average person or the average value among multiple biomarker data of different people in a sample dataset. Furthermore, the user's biomarkers may change with different environmental or controlled conditions (e.g., slopes, climbing, specific activities), and the user's biomarkers can be compared to average or predetermined values ​​corresponding to these corresponding environmental conditions. In some implementations, the user can communicate with the electronic device 120 (e.g., manually input via touch or voice) to input the environmental or control conditions before walking or a specific exercise to initiate measurement of heart rate and acceleration data.

[0061] In some implementations, the medical monitoring system 100 can present the values ​​of the biomarkers. For example, the values ​​of the biomarkers can be presented as graphical display elements on a GUI (e.g., as presented on a display device) and / or as audio notifications (e.g., as presented using an audio speaker).

[0062] 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.

[0063] 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.).

[0064] 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 and provide biomarkers representative of the user's health 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.

[0065] 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.

[0066] 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 feedback to the user 202 regarding the acquired 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).

[0067] Example Process 3 is a flowchart diagram of an example process 300 for monitoring a user's health using physiological sensors. Process 300 can be implemented by a processor-based system, such as medical monitoring system 100 and system 500 described in this disclosure.

[0068] At 302, physiological data representative of a user's cardiac activity is acquired. For example, the physiological data may be acquired by one or more cardiac sensors (e.g., cardiac sensor 112) while the user is walking or moving. For example, a processor-based electronic device (e.g., electronic device 120) that communicates (e.g., wired or wirelessly communicates) with the one or more cardiac sensors may be used to cause the one or more cardiac sensors to acquire the physiological data. In some implementations, the electronic device includes, is coupled to, or is adjacent to (e.g., within the same housing as) the one or more cardiac sensors.

[0069] The physiological data may include or correspond to ECG data or PPG data. For example, the ECG data may include an ECG waveform including various peaks and troughs that reflect different phases of the cardiac cycle. In particular, the ECG data may include a QRS complex, and the R peak of the QRS complex may be used to determine the user's heart rate. Further, for example, the PPG data may include a PPG waveform including various peaks and troughs that reflect different phases of the cardiac cycle. In particular, the PPG data may include a systolic peak that may be used to determine the user's heart rate.

[0070] The physiological data can be acquired at specific time intervals (e.g., 10 seconds, 30 seconds, 1 minute, etc.) and transmitted to an electronic device for further processing. In some implementations, the physiological data can be acquired at fixed, predefined time intervals, such as every 10 seconds, every 30 seconds, or every minute. In some implementations, the physiological data can be acquired at variable time intervals, such as more frequently during certain periods and less frequently during other periods. After the physiological data is acquired, it is transmitted to an electronic device for further processing.

[0071] For example, after receiving or in response to receiving the physiological data, the electronic device can extract R peaks (if the physiological data includes or corresponds to ECG data). Further, based on the extracted R peaks, the electronic device can determine interbeat intervals (IBIs) of the user's heartbeats during the time interval. In some implementations, the R peaks and / or IBIs can be extracted from one or more cardiac sensors, and the electronic device can receive the extracted R peaks and / or IBIs of the user. In some implementations, if the physiological data includes or corresponds to PPG data, the electronic device can extract PPG peaks or systolic peaks of the PPG peaks after receiving or in response to receiving the physiological data. Further, based on these extracted PPG peaks or systolic peaks of the PPG peaks, the electronic device can determine IBIs of the user's heartbeats during the time interval. In some implementations, the systolic peaks and / or IBIs can be extracted from one or more cardiac sensors, and the electronic device can receive the extracted systolic peaks and / or IBIs of the user.

[0072] For example, a processor of the electronic device (e.g., one or more processors described in FIG. 1 ) can be configured to track physiological data acquired at predefined time intervals and determine the R-peak and / or IBI over one or more of the predefined time intervals.

[0073] At 304, acceleration data representative of the user's movement is obtained. For example, when the user is walking or moving, the acceleration data can be obtained by one or more accelerometers (e.g., accelerometer 114). 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 implementations, the electronic device includes, is coupled to, or is adjacent to (e.g., within the same housing as) the one or more accelerometers.

[0074] For example, the acceleration data may include or represent acceleration or acceleration vectors in three directions (e.g., x, y, and z directions on a Cartesian coordinate system) of a user while the user is walking or moving. For example, in a similar manner with respect to physiological data, acceleration data may be acquired at specific time intervals (e.g., 10 seconds, 30 seconds, 1 minute, etc.) and transmitted to an electronic device for further processing. In some implementations, acceleration data may be acquired at fixed, predefined time intervals, such as every 10 seconds, every 30 seconds, or every minute. In some implementations, acceleration data may be acquired at variable time intervals, with acceleration data being acquired more frequently during certain periods and less frequently during other periods. After the acceleration data is acquired, it is transmitted to an electronic device for further processing.

[0075] For example, after receiving or in response to receiving the acceleration data, the electronic device can determine the user's acceleration 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 user's acceleration can be determined in one or more accelerometers, and the electronic device can receive data representing the user's acceleration.

[0076] In some implementations, after receiving or in response to receiving the acceleration data, and before squaring the x-, y-, and z-components of the acceleration data or vector, the electronic device may apply a band-pass filter to the received acceleration data or vector independently for each axis (e.g., the x-, y-, and z-axes) to remove bias and isolate the acceleration components. After filtering, the x-, y-, and z-components corresponding to the band-pass filtered acceleration data for the x-, y-, and z-axes, respectively, may be squared and summed, and then the square root of the sum may be taken to determine the user's acceleration.

[0077] At 306, a biomarker representative of the user's health status is determined. In particular, the biomarker can be determined based on the physiological data and the acceleration data. For example, determining the biomarker may include determining, based on the physiological data and by the electronic device, an IBI of the user's heart rate during a time period (e.g., a time interval) and determining a median IBI from among multiple IBIs during the time period. For example, the IBI may be determined based on an extracted R peak of the physiological data (if the physiological data includes or corresponds to ECG data). In another example, the IBI may be determined based on a PPG peak or an extracted systolic peak of the physiological data (if the physiological data includes or corresponds to PPG data).

[0078] Further, determining the biomarker can include multiplying the median IBI and the acceleration data. For example, determining the biomarker can include multiplying the median IBI by the user's acceleration. For example, the acceleration can correspond to acceleration determined based on applying bandpass filtering, as described above.

[0079] For example, as discussed above with respect to the description of Equation 1, the biomarkers may be expressed in units of velocity per heartbeat (eg, meters / (seconds*heartbeats)).

[0080] Additionally, as previously discussed, 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 higher value may indicate a relatively better health state for the user, and a lower value for the biomarker may indicate a relatively worse health state for the user.

[0081] In some implementations, multiple sets of sensor data (including physiological data and acceleration data) can be acquired to determine biomarkers. For example, the multiple sets of sensor data can be acquired at multiple time intervals that correspond to or form part of the time period, and biomarkers for the time period can be determined. For example, to determine biomarkers for the multiple sets of sensor data, each biomarker determined for each set of sensor data at the respective time interval can be averaged.

[0082] At 308, a data structure representing the biomarkers is generated. For example, the electronic device may format the data into a standardized data format, such as JavaScript Object Notation (JSON) or Extensible Markup Language (XML) format. The standardized data format may also be any other suitable data format suitable for storage in a data store or hardware storage device.

[0083] The data structure is stored in a hardware storage device at 310. 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.).

[0084] In some implementations, instead of or in addition to storing the data structure in a hardware storage device, the data structure may be output to a user interface (e.g., using user interface module 128). For example, if the data structure is to be output for display to a user without being stored, the data structure may be generated in another format suitable for display in step 308. For example, if the data structure is to be stored and then output for display to a user, the data structure may be converted from its storage format to an appropriate display format and output.

[0085] 4 is a flowchart diagram of an exemplary process 400 for monitoring a user's health using physiological sensors. Process 400 can be implemented by processor-based systems such as medical monitoring system 100 and system 500 and can be viewed in conjunction with process 300.

[0086] Physiological data representative of a user's cardiac activity is acquired at 402. For example, the physiological data may be acquired by one or more cardiac sensors (e.g., cardiac sensor 112) while the user is walking or moving. The technique used in step 402 may be the same as the technique used in step 302 of FIG. 3, and therefore the technique will not be repeated here.

[0087] At 404, acceleration data representative of the user's movements may be obtained. For example, the acceleration data may be obtained by one or more accelerometers (e.g., accelerometer 114) while the user is walking or moving. The techniques used in step 404 may be the same as those used in step 304 of FIG. 3, and therefore the techniques will not be repeated here.

[0088] At 406, an IBI of the user's heartbeat is determined. For example, based on physiological data (e.g., ECG data, PPG data) including a waveform with various peaks and troughs reflecting different phases of a cardiac cycle, an R peak (in the case of ECG data) or a systolic peak (in the case of PPG data) is extracted by an electronic device (e.g., electronic device 120). For example, based on the extracted R peak or systolic peak, an IBI of the user's heartbeat over a specific time period is determined.

[0089] A median IBI among the determined (or extracted) IBIs is determined by the electronic device at 408. In some implementations, instead of the median IBI, an average IBI of the IBIs can be determined.

[0090] 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 the 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 expressed in units of time (e.g., seconds, milliseconds) per heartbeat, and the acceleration can represent the change in average acceleration of the user's movement over that period of distance (e.g., meters) in units of time squared (e.g., seconds squared). Further, for example, the biomarker can be expressed in velocity per heartbeat (e.g., meters / (seconds * heartbeat)).

[0091] At 412, a data structure representing the biomarkers is generated. For example, the electronic device may format the data into a standardized data format, such as JavaScript Object Notation (JSON) or Extensible Markup Language (XML) format. The standardized data format may be any other suitable data format suitable for storage in a data store.

[0092] At 414, the data structure is stored in a hardware storage device. For example, the data structure may be stored in one or more storage devices (e.g., one or more storage devices 530, data stores, etc.). The technique used in step 414 may be the same as the technique used in step 310 of Figure 3, so that technique will not be repeated here.

[0093] Exemplary Computer System 5 illustrates an exemplary computing system according to an implementation of the present disclosure. System 500 may be used for any of the operations described with respect to the various implementations discussed herein. System 500 may be included in, used by, in communication with, or corresponding to electronic device 120. Additionally, system 500 may include, be used by, or in communication with sensor device 110. System 500 may include one or more processors 510, memory 520, one or more storage devices 530, and one or more input / output (I / O) devices 560 controllable via one or more I / O interfaces 540. The various components 510, 520, 530, 540, or 560 may be interconnected through at least one system bus 550, which may enable data transfer between the various modules and components of system 500.

[0094] The processor 510 may be configured to process instructions for execution within the system 500. The processor 510 may include a single-threaded processor, a multi-threaded processor, or both. The processor 510 may be configured to process instructions stored in the memory 520 or the storage device 530. The processor 510 may include hardware-based processors, each including one or more cores. The processor 510 may include a general-purpose processor, a special-purpose processor, or both.

[0095] The memory 520 may store information within the system 500. In some implementations, the memory 520 includes one or more computer-readable media. The memory 520 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 520 may include read-only memory, random access memory, or both. In some examples, the memory 520 may be used as active or physical memory by one or more executing software modules.

[0096] The storage device 530 may be configured to provide (e.g., persistent) mass storage for the system 500. In some implementations, the storage device 530 may include one or more computer-readable media. For example, the storage device 530 may include a floppy disk device, a hard disk device, an optical disk device, or a tape device. The storage device 530 may include read-only memory, random access memory, or both. The storage device 530 may include one or more of an internal hard drive, an external hard drive, or a removable drive.

[0097] Either or both of memory 520 or storage device 530 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 500. 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 500 or may be external to system 500. 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 510 and memory 520 may be supplemented by, or incorporated in, one or more application-specific integrated circuits (ASICs).

[0098] System 500 may include one or more I / O devices 560. I / O devices 560 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 560 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 560 may be physically incorporated into one or more computing devices of system 500 or may be external to one or more computing devices of system 500.

[0099] The system 500 may include one or more I / O interfaces 540 to enable components or modules of the system 500 to control, interface with, or otherwise communicate with I / O devices 560. The I / O interfaces 540 may allow information to be transferred within or out of the system 500, or between components of the system 500, through serial, parallel, or other types of communication. For example, the I / O interfaces 540 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 540 may be configured to provide connections via Universal Serial Bus (USB) or Ethernet. In some examples, the I / O interfaces 540 may be configured to provide serial connections conforming to a version of the IEEE 1394 standard.

[0100] I / O interface 540 may also include one or more network interfaces that enable communication between computing devices within system 500 or between system 500 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.

[0101] The computing devices of system 500 may communicate with each other and with other computing devices using one or more networks. Such networks may include public networks such as the Internet, private networks such as organizational or personal intranets, or any combination of private and public networks. The networks may include any type of wired or wireless network, including, but not limited to, local area networks (LANs), wide area networks (WANs), wireless WANs (WWANs), wireless LANs (WLANs), mobile communication networks (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.

[0102] System 500 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, computing devices 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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]

[0118] 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 300 processes 400 processes 500 Systems 510 processor 520 memory 530 Storage Devices 540 I / O interface 550 System Bus 560 Input / Output (I / O) Devices

Claims

1. A sensor device configured to be attached to a user's body, one or more physiological sensors configured to acquire physiological data representative of a cardiac activity of the user; one or more acceleration sensors configured to acquire acceleration data representative of the user's movements; a sensor device comprising: an electronic device communicatively coupled to the sensor apparatus, causing the sensor device to acquire the physiological data and the acceleration data during a period of time; determining a biomarker representative of a health state of the user based on the physiological data and the acceleration data, the biomarker representing a relationship between a speed of the movement of the user and the cardiac activity of the user during the time period; generating a data structure representing the biomarkers; storing the data structure in a hardware storage device; the electronic device comprising one or more processors configured to perform A medical monitoring system comprising:

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

3. The medical monitoring system of claim 1 , wherein the biomarkers are correlated with the user's walking speed and the user's cardiac activity.

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

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

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

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

8. determining the biomarkers determining a plurality of beat-to-beat intervals (IBIs) of the user's heartbeat during the time period based on the physiological data; determining a median IBI from among the plurality of IBIs during the time period; The medical monitoring system of claim 1 , comprising:

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

10. determining the biomarkers The medical monitoring system of claim 8 , further comprising multiplying the median IBI and the acceleration data.

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

12. causing the user to present the biomarkers; The medical monitoring system of claim 11 , further comprising outputting the data structure on a display of a user interface.

13. generating the data structure, The medical monitoring system of claim 1 , further comprising combining the biomarkers with other physiological data of the user.

14. using the electronic device to cause one or more physiological sensors of the sensor apparatus to acquire physiological data representative of the user's cardiac activity during a period of time; using the electronic device to cause one or more acceleration sensors of the sensor apparatus to obtain acceleration data representative of the user's movements during the time period; determining, by the electronic device, a biomarker representative of a health state of the user based on the physiological data and the acceleration data, the biomarker representing a relationship between a speed of the movement of the user during the time period and the cardiac activity of the user; generating, by the electronic device, a data structure representing the biomarkers; storing, by the electronic device, the data structure in a hardware storage device; A method comprising:

15. determining the biomarkers based on the physiological data and the acceleration data, determining a plurality of beat-to-beat intervals (IBIs) of the user's heartbeat during the time period based on the physiological data; determining a median IBI from among the plurality of IBIs during the time period; The method of claim 14, comprising:

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

17. determining the biomarkers based on the physiological data and the acceleration data, The method of claim 15, comprising multiplying the median IBI and the acceleration data.

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

19. The method of claim 14 , further comprising causing the biomarkers to be presented to the user using the electronic device.

20. The step of having the user present the biomarkers includes:

20. The method of claim 19, comprising outputting the data structure on a display of a user interface.

Images