Medical system with physiological sensor, method, computer readable storage medium and program product

Abstract

Medical systems, methods, computer-readable media, and program products with physiological sensors are provided. In an example method, a computer system receives acceleration data, activity classification data, sleep data, and one or more vital sign metrics indicative of physical activity of a subject from a wearable medical sensor. The system determines a physical activity score, a sleep score, and a vital sign score based on acceleration data, activity classification data, sleep data, and one or more vital sign metrics. The system determines a quality of life score for the subject based on the physical activity score, the sleep score, and the vital sign score; and the system causes the quality of life score to be presented to the subject using the display screen.

Description

Technical Field

[0001] This instruction manual typically pertains to medical systems with physiological sensors. Background Technology

[0002] Typically, a user's health-related quality of life can be assessed by measuring one or more physiological characteristics and comparing the measured characteristics to health references. For example, users with physiological characteristics that meet or exceed specific health references may have a high quality of life, while users with physiological characteristics that do not meet health references may have a low quality of life. Summary of the Invention

[0003] Wearable medical systems can typically be used to monitor a user's health-related quality of life and facilitate treatment of that user.

[0004] In the 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 measures representing aspects of the user's health-related quality of life.

[0005] For example, a medical monitoring system can acquire sensor data indicating a user's physical activity level, sleep metrics, and / or vital signs. For instance, sensor data may include measurements of the user's physical activity (such as the number of steps taken during a time period) and / or measurements of cardiac activity (such as the user's heart rate or pulse rate during a time period, e.g., obtained by one or more cardiac sensors). Furthermore, sensor data may include measurements of the user's movement during a time period (e.g., acceleration data obtained using one or more accelerometers). Based on these measurements, the medical monitoring system can determine metrics representing the user's health-related quality of life. Additionally, the medical monitoring system may present these metrics to the user or other users (e.g., healthcare providers) to facilitate the user's care and / or monitor the user's health over time.

[0006] The implementation described in this paper can provide various technical benefits. As an example, the implementation described herein allows computer systems to automatically determine a user's health-related quality of life based on sensor data (e.g., cardiac data, acceleration data, etc.) without requiring manual feedback from humans. Furthermore, medical monitoring systems can provide this functionality by performing computer-specific manipulations on the input data in an objective manner (and in a way that is not feasible for humans), rather than relying on human interpretation of the input data, which may be less suitable for computers.

[0007] As another example, the implementation described herein can be used to facilitate (e.g., during clinical trials of pharmaceutical compositions or other interventions) the monitoring and management of users, thereby improving their health-related quality of life and / or health. For example, a medical monitoring system can generate a metric representing a user's health-related quality of life and provide that metric to the user or other users (e.g., healthcare providers). For instance, the health-related quality of life metric determined by the medical monitoring system can provide an objective measure of a user's health-related quality of life, reducing variability and bias in subjective user responses from questionnaires or surveys. Based on this information, the user or other users can take proactive steps to improve the user's health-related quality of life, such as conducting further testing to diagnose the user's medical condition, changing the user's behavior, lifestyle and / or diet, or any other actions.

[0008] In one aspect, a method includes: receiving from wearable medical sensors acceleration data, activity classification data, sleep data, and one or more vital sign measures indicative of a subject's physical activity; determining a physical activity score, a sleep score, and a vital sign score based on the acceleration data, the activity classification data, the sleep data, and the one or more vital sign measures; determining a quality of life score for the subject based on the physical activity score, the sleep score, and the vital sign score; and displaying the quality of life score to the subject using a display screen.

[0009] The implementation of these aspects may include one or more of the following features.

[0010] Some implementations involve storing data structures representing quality of life scores on hardware storage devices.

[0011] Some implementations include: determining that the quality of life score is less than a threshold quality of life score; and in response to determining that the quality of life score is less than the threshold quality of life score, causing an alarm to be generated indicating that the quality of life score is less than the threshold quality of life score.

[0012] Some implementations include: determining multiple quality of life scores for the subject at multiple time points; and determining the variability of the subject's quality of life based on the multiple quality of life scores.

[0013] Some implementations include: determining the subject's baseline quality of life based on the plurality of quality of life scores; determining one or more subsequent quality of life scores after determining the baseline quality of life; and determining that the one or more subsequent quality of life scores deviate from the baseline quality of life by a amount greater than a threshold.

[0014] In some implementations, the vital signs sensor includes at least one of a heart rate sensor and a pulse rate sensor.

[0015] Some implementations include receiving heart rate data and specified step count data from the wearable medical sensor, wherein the vital signs score is determined based on the heart rate data, the step count, and the activity classification data.

[0016] In some implementations, determining the vital signs score includes: receiving sleep metrics from the wearable medical sensor; determining a resting vital signs score while awake based on the activity classification data, the acceleration data, and the heart rate data; determining a sleep vital signs score based on the activity classification data, the acceleration data, the sleep metrics, and the heart rate data; determining a walking vital signs score based on the activity classification data, the acceleration data, the step count, and the heart rate data; and determining the vital signs score based on the resting vital signs score while awake, the sleep vital signs score, and the walking vital signs score.

[0017] In some implementations, determining the physical activity score includes determining a walking score and a sedentary score.

[0018] Some implementations include: receiving data from the wearable medical sensor for a specified step count, wherein the walking score is determined based on the step count, the acceleration data, and the activity classification data; and determining a sedentary score based on the activity classification data and the activity count according to the acceleration data.

[0019] In some implementations, the activity count represents the subject's movement determined based on the magnitude of the acceleration within a time interval.

[0020] Some implementations include filtering the activity classification data to extract low-activity samples when the subject is awake, wherein the sedentary score includes a count of consecutive low-activity samples when the subject is awake.

[0021] In some implementations, the walking score includes: step count score, purposeful walking score, light walking score, moderate walking score, and vigorous walking score, wherein each of the purposeful walking score, the light walking score, the moderate walking score, and the vigorous walking score is determined based on the number of steps taken within a time interval.

[0022] In some implementations, determining the sleep score includes determining one or more of a sleep duration score, a sleep latency score, and a sleep efficiency score based on the sleep metric.

[0023] Some implementations include: determining one or more recommendations for improving the health of the subject based on the quality of life score, and presenting the one or more recommendations to the subject.

[0024] Some implementations include monitoring the subjects based on the quality of life score.

[0025] Other embodiments of this aspect include corresponding computer systems, devices, and computer programs recorded on one or more computer storage devices, each computer system, device, and computer program configured to perform the actions or operations described herein. A system of one or more computers may be configured to perform specific actions by means of software, firmware, hardware, or combinations thereof installed on the system, which, in operation, causes the system to operate. A system of one or more computers may be configured to perform specific actions by means of including instructions that, when executed by a data processing device, cause the device to operate.

[0026] Details 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 specification, drawings, and claims. Attached Figure Description

[0027] Figure 1 This is a diagram of an example medical monitoring system.

[0028] Figure 2 This is a flowchart illustrating an example method for using a medical monitoring system to determine a user's health-related quality of life score.

[0029] Figure 3 This is a flowchart of an example method for determining physical activity scores.

[0030] Figure 4 This is a flowchart of an example method for determining a walking score.

[0031] Figure 5 This is a flowchart of an example method for determining a sedentary rating.

[0032] Figure 6 This is a flowchart of an example method for determining a sleep score.

[0033] Figure 7 This is a flowchart of an example method for determining vital sign scores.

[0034] Figure 8 This is a flowchart of another example method for determining vital signs scores.

[0035] Figure 9This is a flowchart of an example process used to monitor a user's physical health and related quality of life.

[0036] Figure 10 This is a diagram of an example computer system.

[0037] The same reference numerals and symbols in the various figures indicate the same elements. Detailed Implementation

[0038] Figure 1 An example medical monitoring system 100 is shown for automatically monitoring a user's health-related quality of life. The medical monitoring system 100 includes sensor devices 110 and electronics 120 communicatively coupled to each other (e.g., via one or more wired or wireless communication links 150). Typically, the medical monitoring system 100 uses the sensor devices 110 to acquire sensor data about the user and uses the electronics 120 to process the sensor data to determine one or more scores representing the user's health status. The one or more scores may represent the user's health status based on health references.

[0039] Sensor device 110 includes one or more sensors configured to acquire measurements of a user’s physiology, the user’s physical behavior (e.g., physical activity, sleep patterns) and / or any other characteristics of the user.

[0040] For example, such as Figure 1 As shown, sensor device 110 may include one or more accelerometers 114 configured to acquire sensor data representing a user’s movement 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., the x, y, and z directions in a Cartesian coordinate system). In some implementations, accelerometers 114 may acquire sensor data at 32 Hz (or approximately 32 Hz).

[0041] Furthermore, sensor device 110 may include one or more cardiac sensors 112 configured to acquire sensor data representing a user's cardiac activity. In some implementations, the cardiac sensor 112 may include one or more electrocardiogram (ECG) sensors and / or one or more photoplethysmography (PPG) sensors. In some implementations, the cardiac sensor 112 may acquire sensor data at 64 Hz or higher, 125 Hz or higher, or up to 250 Hz (or approximately 250 Hz).

[0042] Furthermore, sensor device 110 includes a communication module 116 configured to transmit and / or receive data relative to electronic device 120. As an example, communication module 116 may include one or more receivers, transmitters, and / or transceivers. In some implementations, communication module 116 may communicate with 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.).

[0043] Furthermore, the sensor device 110 is configured to be worn by a user. For example, such as Figure 1 As shown, sensor device 110 may include a substrate 118 configured to be attached to a user's body (e.g., via adhesive or tape). Furthermore, a heart sensor 112, an accelerometer 114, and a communication module 116 may be attached to the substrate, thus securing them to the user's body. In some implementations, at least a portion of the substrate 118 may be made of a compliant material (such as silicone, rubber, elastomers, and / or any other flexible material). In some implementations, at least a portion of the compliant substrate 118 may be made of a rigid material (such as rigid metal, rigid plastic, etc.).

[0044] Typically, electronic device 120 is configured to receive sensor data acquired by sensor device 110 and process the sensor data to determine one or more measures representing the user's health status. Furthermore, the electronic device is configured to present information about the rating and any other information to the user and / or another user (e.g., a healthcare provider).

[0045] Typically, 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 or laptop computers), server computing devices (e.g., server computers or cloud computing systems), mobile computing devices (e.g., cellular phones, smartphones, tablets, personal data assistants, network-enabled laptops), 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 operating using one or more operating systems (e.g., Microsoft Windows, Apple macOS, Linux, Unix, Google Android, and Apple iOS, etc.) and one or more architectures (e.g., x86, PowerPC, and ARM, etc.).

[0046] exist 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, 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 several computing devices, and components of electronic device 120 may be distributed across one or more of these computing devices.

[0047] 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).

[0048] Communication module 124 is configured to transmit and / or receive data relative to sensor device 110. As an example, communication module 124 may include one or more receivers, transmitters, and / or transceivers. In some implementations, 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.).

[0049] Database module 122 maintains information related to the operation of medical monitoring system 100.

[0050] As an example, database module 122 may store input data 122a for determining one or more measures representing a user's health. For example, input data 122a may include at least some of the sensor data generated by sensor device 110 (e.g., heart data, acceleration data, etc.).

[0051] As another example, database module 122 may store output data 122b generated by electronic device 120. As an example, output data 122b may include one or more metrics generated by electronic device 120 based on input data 122a.

[0052] In addition, database module 122 may store processing rules 122c, which specify how data in database module 122 can be processed for the operations described herein.

[0053] As an example, processing rule 122c may include specifying how to format, parse, and process input data 122a to determine one or more rules for one or more corresponding metrics or ratings about a user.

[0054] As another example, processing rule 122c may include one or more rules that specify (e.g., using user interface module 128) the conditions for presenting data to the user and the manner in which the data is presented.

[0055] As another example, processing rule 122c may include one or more rules specifying (e.g., using database module 122) how data is stored for future retrieval and / or processing.

[0056] The example data processing techniques are described in further detail below.

[0057] Processing module 126 processes stored data or data otherwise accessible to electronic device 120. For example, processing module 126 may be used to perform one or more of the operations described herein (e.g., by executing processing rule 122c on input data 112a to generate output data 122b).

[0058] User interface module 128 is configured to present information to a user and / or receive input from a user. As an example, user interface module 128 may include one or more display devices (e.g., a display screen, touchscreen, etc.) configured to present a user interface (e.g., a graphical user interface (GUI)) enabling the user to interact with electronic device 120 and / or sensor device 110. Example interactions include viewing data, transferring data from one component to another, and / or issuing commands to electronic device 120 and / or sensor device 110. Commands may include, for example, any user instruction to perform a specific operation or task on one or more of electronic device 120 and / or sensor device 110. 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 generators).

[0059] In some implementations, software applications can be used to facilitate the tasks described herein. As an example, the application can be installed on electronic device 120. Furthermore, the user can interact with the application to input data and / or commands to electronic device 120 and to examine data generated by electronic device 120.

[0060] Figure 2 An example method 200 is illustrated using a medical monitoring system 100 to determine a user's physical health-related quality of life.

[0061] In this example, sensor device 110 is worn by the user (e.g., attached to the user's skin or fixed to the user's wrist). While the user is wearing sensor device 110, sensor device 110 measures the user's physiological characteristics. Physiological characteristics may include data indicative of the user's physical health, such as acceleration data, activity classification, vital sign measurements, step count, sleep state, sleep pattern, heart rate, etc. For example, while the user is wearing sensor device 110, sensor device 110 continuously acquires measurements of the user's physiological characteristics.

[0062] Sensor device 110 outputs measured data to a standardized data format and data cleaning module 202. The standardized data format and data cleaning module 202 is included, for example, in electronic device 120. In the standardized data format and data cleaning module 202, the measured data from sensor device 110 is formatted into a common format used to determine a user's health-related quality of life score. For example, sensor metric standardization converts individual sensor-specific data types, column names, and sampling frequency resolutions into a standardized data format. Using a standardized data format enables the consistent processing of data obtained from multiple sensors and / or sensor types. The data cleaning portion of module 202 includes correcting data errors, removing noise, handling (e.g., removing) data anomalies, and determining whether data is suitable for further analysis. For example, module 202 may determine whether data is suitable for further analysis based on the number of samples available each day and / or irregular values ​​per unit time (e.g., values ​​that are too high or too low per unit time). Module 202 may also generate a log of data cleaning activities, including actions taken and any problems encountered. This log can be used to generate a report summarizing the data cleaning process and its results.

[0063] Module 202 outputs formatted and cleaned data from a subset (e.g., a group of metrics) to multiple quality of life score blocks. For example, in each quality of life score block, a normalized score with values ​​in the range of 0 to 100 (inclusive) is determined based on the input data. In some implementations, the scores can be normalized to cover different ranges of values ​​(e.g., 0 to 1, 0 to 1000, etc.).

[0064] Module 202 outputs a first subset 204 of the data to the body activity mass block 206. The body activity mass block 206 determines a body activity score 208 based on acceleration data from sensor device 110. (Reference) Figures 3-5 Further details regarding the physical activity mass block 206 are discussed.

[0065] Module 202 outputs a second subset 210 of the data to sleep quality block 212. Sleep quality block 212 determines the user's sleep score based on sleep data from sensor device 110. For example, sleep duration, sleep latency, and sleep efficiency are derived from the sleep data. Sleep block 212 determines the sleep score based on one or more of sleep duration, sleep latency, and sleep efficiency. (See reference) Figure 6 Further details regarding sleep quality block 212 will be discussed.

[0066] Module 202 outputs a third subset 216 of the data to the Vital Signs Quality Block 218. The Vital Signs Quality Block 218 determines a vital signs score 220 based on sensor data, including measurements of one or more of the user's vital signs (e.g., heart rate), acceleration data, and activity classification. The Vital Signs Score 220 can be used to indicate the user's health and comfort during various daily activities. By considering heart rate patterns during, for example, sleep, wake-rest periods, low physical activity, and high physical activity, the Vital Signs Score 220 provides valuable insights into the user's physiological systems' ability to appropriately respond to daily human activities. This ultimately contributes to a more comprehensive understanding of an individual's health and quality of life. Reference Figures 7-8 Further details regarding vital signs mass block 218 will be discussed.

[0067] In the overall quality of life score block 222, the physical activity score 208, sleep score 214, and vital signs score 220 are combined to determine the user's overall health-related quality of life score. For example, the aspect scores 208, 214, and 220 are averaged to generate the overall quality of life score. The overall quality of life score can be in the range of 0 to 100 (inclusive). In some implementations, a weighted average can be used to generate the overall quality of life score, where weights are assigned to the aspect scores 208, 214, and 220. Other methods for combining aspect scores 208, 214, and 220 are also possible. For example, a non-linear weighted sum can be used, where a non-linear function is applied to the aspect scores before summing; a regression model can be used to predict the overall score based on the aspect scores; or a categorical or clustered score can be determined based on the overall score associated with groupings of aspect scores.

[0068] In some implementations, users can be monitored based on health-related quality of life scores. For example, health providers can offer recommendations (e.g., activity recommendations, behavior changes) to users to improve their health-related quality of life scores.

[0069] Figures 3-5 This is a flowchart of an example method for determining a physical activity score 208. The physical activity quality block 206 receives a first subset 204 of data, including step counts 302, activity categorization 304, and activity counts 306, as input. Step counts 302 include, for example, the number of steps taken by the user, derived from acceleration data. Step counts 302 may also include the time associated with when the steps were taken to determine the rate of stepping (e.g., steps per minute, steps per hour). Activity categorization 304 includes classifying the activity into one of several categories. For example, activity categories may include walking, sedentary, sleeping, resting, moving, sitting, leaning, standing, etc. Activity counts 306 indicate the intensity of the user's physical activity. The physical activity quality block 206 uses two sub-blocks, a walking quality of life block 308 and a sedentary quality of life block 310, to determine the overall physical activity score 208. The walking quality of life block 308 provides insights into the user's daily walking ability, and the sedentary quality of life block 310 informs the user of daily sedentary behavior (e.g., periods of inactivity).

[0070] The Walking Quality of Life block 308 takes step count 302 and activity classification 304 as input. Data preprocessing step 312 prepares the input data for input into sub-scoring blocks 318-326. Data preprocessing step 312 includes step count aggregation 314, where step counts are totaled over a specified time period (e.g., one hour, one day, one week). Data preprocessing step 312 also includes walking segment detection 316. For example, activity classification 304 indicates the time period during which the user is walking. Walking segment detection 316 determines the time period during which the user is walking (e.g., walking for 2, 6, or 'n' minutes). Walking segment detection 316 can be used to determine the rate of steps taken (e.g., steps per minute) during the time period indicated as walking.

[0071] Sub-scoring blocks 318-326 determine the user's walking sub-scores that combine to form the total walking score 328. Step count score 318 takes the total step count from step count aggregation 314 as input. Step count score 318 scores the user based on the number of steps taken during the day. Step count score 318 can associate a certain number of daily steps with a reduced risk of all-cause mortality. For example, if the step count exceeds a threshold number of steps per day (e.g., 6,000-8,000 steps per day for users aged 60 or older, or 8,000-10,000 steps per day for users under 60), the user receives a step count score of 1. If the step count does not exceed the threshold number, the user receives a step count score of 0. In some implementations, if the step count does not exceed the threshold number, the step count score is the ratio of the number of steps taken during the day to the threshold number of steps.

[0072] The walking segment is the input for each of the four remaining sub-scoring blocks 320-326, which generate sub-scorings based on the intensity of the user's daily or weekly walking to obtain a positive impact on the user's health. The purposeful walking score 320 indicates whether 30 minutes of purposeful walking was detected each day (e.g., <1 minute per segment). If yes, the user receives a purposeful walking score of 1. If not, the user receives a purposeful walking score of 0, or a purposeful walking score that is the ratio of minutes of purposeful walking to the target (e.g., 30 minutes). Purposeful walking can be filtered from random walking based on the stepping rate. For example, random walking can be considered when the number of steps taken per minute is less than 40 steps per minute, while purposeful walking can be walking at a rate from 40 to 60 steps per minute.

[0073] The light walking score 322 indicates whether a threshold amount (e.g., 30 minutes) of light walking (e.g., duration greater than 1 minute) is detected daily. Light walking intensity can include, for example, a walking rate from 60 steps per minute to 100 steps per minute over a period of time. If the threshold amount of light walking is reached, the user receives a score of 1; if the threshold is not reached, the user receives a score of 0 or a partial score based on the ratio of the minutes of light walking divided by the threshold.

[0074] The moderate walking score 322 and vigorous walking score 324 indicate whether the user has achieved at least 10 minutes of moderate walking intensity (e.g., 100-129 steps / minute) or 10 minutes of vigorous walking intensity (e.g., greater than 130 steps / minute) per day. For both the moderate walking score 322 and vigorous walking score 324, the user receives a score of 1 if the threshold is met, and a score of 0 or a ratio score if the threshold is not met.

[0075] The sub-ratings from sub-rating blocks 318-326 can be combined to form a total walking score 328, for example, by averaging the sub-ratings. In some implementations, a weighted average of the sub-ratings can be used to determine the total walking score 328. For example, the step count score 318 can receive a higher weight than other sub-ratings. Example weighting schemes include a 0.7 weight for step count, a 0.7 weight for purposeful walking, a 0.7 weight for light walking, a 0.9 weight for moderate walking, and a 1 weight for vigorous walking. In some implementations, the total walking score 328 can be a score of 1 if at least one of the sub-ratings reaches a score of 1. Any sub-rating block in the sub-rating block reaching a score of 1 can help the user reduce total cost mortality.

[0076] The sedentary quality of life block 310 considers the amount of time spent in sedentary patterns (e.g., inactivity, rest, sitting, etc.) outside of a user's sleep routine. The sedentary quality of life block 310 takes activity classification 304 and activity counts 306 as input. Preprocessing of the input data is performed in block 330. Preprocessing includes filtering by waking activity 332, which filters activity counts 306 by periods of activity classified as waking (e.g., non-sleep). Low-activity filtering 334 further filters the data by identifying periods of activity with low activity counts. Sedentary behavior includes time spent sitting daily, such as watching television (TV), and periods of sedentary behavior may fall at the lowest (or lowest activity count) points in the body activity spectrum outside of sleep time. Activity count segment aggregation 336 identifies and aggregates periods of low consecutive activity counts (e.g., data segments indicated as waking activities with low activity counts). The aggregated activity count segments are input into a sedentary scoring rule 338 to determine a sedentary quality of life score. The sedentary score can be determined based on the total amount of time a user spends in sedentary patterns. For example, a 4-hour threshold can be used to distinguish between poor or good sedentary behavior. For instance, aggregated sedentary behavior of less than 4 hours might receive a good sedentary score of 340 (e.g., a score of 1). Aggregated sedentary behavior of four or more hours might receive a poor sedentary score of 342 (e.g., a score of 0).

[0077] The individual sub-scores 318-326 and 340-342 can be determined based on a binary system (e.g., 1 for meeting or exceeding the criterion, 0 otherwise) or a continuous scale (e.g., a ratio based on the proportion of criterion met). Continuous scores can more easily express a user's ability to achieve habits considered beneficial to a good quality of life. In this way, a physical activity score 208 higher than 1 can be interpreted as a strong ability to perform that daily activity. On the other hand, a physical activity score lower than 1 can indicate a weaker ability to perform that particular activity.

[0078] The overall physical activity score 208 can be determined by combining the total walking score 328 with the sedentary score 340 or 342. For example, the physical activity score 208 can be the sum, average, or weighted average of the total walking score 328 and the sedentary score 340 or 342.

[0079] Figure 6 This is a flowchart of an example method for determining the total sleep score 214. Sleep block 212 takes as input a second subset of data 210 from general data and data cleaning module 202. The second subset of data 210 includes sleep duration 602 (e.g., the amount of time spent sleeping), sleep latency 604 (e.g., the amount of time between going to bed and falling asleep), and sleep efficiency 606 (e.g., the amount of time spent in bed before falling asleep). Input data 602-606 is fed into data preprocessing block 608. Data preprocessing block 608 may format and / or categorize the input data 602-606 for use in scoring rules 610-614.

[0080] Scoring rules 610-614 receive data from data preprocessing block 608. Sleep duration score 610 is based on the amount of sleep per day. For example, if sleep duration is 7 hours or longer, sleep duration score 610 is 0; if sleep duration is between 6 and 7 hours, sleep duration score 610 is 1; if it is between 5 and 6 hours, sleep duration score 610 is 2; and if sleep duration is less than 5 hours, sleep duration score 610 is 3.

[0081] The sleep latency score 612 represents the amount of time from when a user lies in bed until they actually begin to sleep. For example, a sleep latency score of 612 is 0 if the sleep latency is 15 minutes or less, 1 if the sleep latency is between 15 and 30 minutes, 2 if the sleep latency is between 30 and 60 minutes, and 3 if the sleep latency is greater than 60 minutes.

[0082] The Sleep Efficiency Score 614 is based on the ratio between total sleep time and the total time a user spends in bed. For example, if a user's sleep efficiency is 85% or higher, the Sleep Efficiency Score 614 is 0; if sleep efficiency is between 75% and 85%, the Sleep Efficiency Score 614 is 1; if sleep efficiency is between 65% and 75%, the Sleep Efficiency Score 614 is 2; and if sleep efficiency is below 65%, the Sleep Efficiency Score 614 is 3.

[0083] In the aforementioned example, each sleep score 610-614 is a score value ranging from 0 to 3, where 0 is the best score and 3 is the worst score. The total sleep score 214 can be determined as the sum of the individual sleep scores 610-614, thus giving a total score range between 0 and 9. To combine the total sleep score with the physical activity score 208 and vital signs score 220, the total sleep score can be normalized and inverted. For example, the total sleep score 214 can be normalized to a range of 0 to 1 or 0 to 100 by dividing by the maximum possible sleep score (e.g., 9) and multiplying by the expected range. For example, the sleep score can be inverted by subtracting the normalized score from the maximum score within the expected range, such that 0 is a poor sleep score and 1 or 100 is a good sleep score.

[0084] Figure 7 This is a flowchart of an example method 700 for determining vital sign scores. Vital sign quality block 218 takes as input a third data subset 216 from data format standardization and data cleaning module 202. The third data subset 216 includes vital sign measures 702 (e.g., heart rate, respiratory rate, blood oxygen level), activity classification 704, and activity counts 706. Vital sign quality block 218 identifies whether a user's vital signs respond as expected given a specific daily activity. The input data is preprocessed (e.g., filtered, categorized) based on three activity classes: wake-resting vital signs 708, sleep vital signs 710, and walking vital signs 712. For example, the data vital sign measures 702 are categorized based on activity classification 704 and activity counts 706. After preprocessing, vital signs 708-712 are scored based on various criteria.

[0085] The pulse rate / heart rate (PR / HR) resting abnormality rate 714 (ROR) indicates the percentage of HR samples that are outside the “normal” range during a resting period. For this score, periods of PR / HR within and outside the “normal” range are aggregated. The percentage of abnormality is determined based on comparisons with samples within the normal range. A second version of the PR / HR resting abnormality rate 716 (RORv2) considers consecutive resting periods (e.g., resting periods longer than 5 minutes). For both scores 714 and 716, the score can be between 0% and 100%, where the best possible outcome is represented by 100 (e.g., when the abnormality rate is 0), and the worst outcome is represented by 0 (when the abnormality rate is 100).

[0086] The sleep abnormality rate (SOR) of 720 (PR / HR) can be determined similarly to the ROR score and RORv2 score. In the case of SOR 720, the “normal” range of heart rate is shifted to a range suitable for sleep. For example, the “normal” range of sleep heart rate can be 20% to 30% lower than the resting heart rate of a typical adult. A typical resting heart rate can be, for example, 50-70 beats per minute. In some implementations, the range can be personalized for the user (e.g., based on the user's historical heart rate values).

[0087] The two supplemental scores, 718 and 722, are based on the National Early Warning Score (NEWS) framework to identify users at risk of developing sepsis given their vital signs (e.g., to detect if a user's organs are malfunctioning). Within this framework, higher scores indicate more severe illness and a greater need for immediate medical intervention. For example, in the NEWS-2 Resting PR / HR Score 718, resting vital sign segments are identified and scored for each day. For instance, a score of 0 (or normal) is given if any bpm sample is between 51 and 90, a score of 1 is given if the bpm sample is between 41 and 50 bpm or 91 and 110 bpm, a score of 2 is given if the bpm sample is between 111 and 130 bpm, and a score of 3 is given if the bpm sample is below 40 bpm or above 130 bpm. For each segment, two example options for determining the overall resting segment score include determining the average score of the resting segment and determining the score based on the maximum score from the resting segment (indicating the worst condition during that segment). The same options (e.g., average score or maximum score) can be used to determine the overall daily score. Using the maximum score may be a more prudent approach.

[0088] For the NEWS-2 walking PR / HR score 722, the processing appears similar to that described for the NEWS-2 resting PR / HR score 718, where the ranges for each score are shifted based on personalized user baseline offset adjustments. The ranges of normal or abnormal vital signs established in the original National Alert Score (NEWS-2) are not applicable when determining the HR / PR score during walking (or any form of exercise) because these ranges are established only for resting HR / PR. For example, the user's baseline resting heart rate is the average between the 25th and 75th percentiles (interquartile range or IQR) (the middle 50% of its captured data) and is a good measure of typical variation in a person's heart rate. For each walking segment detected during the day, the median walking HR is calculated. Based on these two values, the difference variable is determined as the difference between the median walking heart rate and the baseline walking heart rate. This difference is added to the individual values ​​of the NEWS-2 ranges described earlier and is used for processing the final daily score as described for the NEWS-2 resting PR / HR score 718.

[0089] The total vital signs score 220 can be determined based on the sum, average, or weighted average of the various scores 714-722. For example, the total vital signs score 220 can be a value between 0 and 1 or between 0 and 100, indicating the average of the scores 714-722 (which are themselves values ​​between 0 and 1 or between 0 and 100).

[0090] Figure 8 This is a flowchart of another example method 800 for determining vital sign scores 220. The vital sign quality block 218 takes vital signs 802 and activity classification 804, output from the shared data model and data cleaning module 202, as input.

[0091] Vital signs 802 are filtered into low-activity segments based on activity classification 804 (806). For example, vital signs 802 are filtered based on sedentary, resting, and / or sleep activity categories. Based on activity classification 804, vital signs 802 are further classified into awake activity 808 and sleep activity 810.

[0092] Arousal activity 808 is scored based on the resting anomaly rate 812. For this score, PR / HR periods within and outside the "normal" range are aggregated. The percentage of anomalies is determined based on comparisons with samples within the normal range (e.g., identifying samples whose HR deviates more than a threshold relative to the mean within the normal range). The resting anomaly rate 812 can be represented as a number between 0 and 1 or 0 and 100, indicating the percentage of data samples identified as anomalies.

[0093] The sleep abnormality rate 814 is based on sleep activity 810. Similar to the resting abnormality rate 812, the sleep abnormality rate 814 scores PR / HR periods within and outside the normal sleep range. The sleep abnormality rate 814 can be expressed as a number between 0 and 1 or 0 and 100, indicating the percentage of sleep activity identified as abnormal.

[0094] Additionally, sleep activity 810 is scored to determine the sleep decline trend 816. The sleep decline trend 816 can measure the trend of heart rate decline during sleep. In some implementations, the sleep decline trend can have a relationship with f(x) = 1 / x. 2 The lines have a similar shape, with the asymptote located at 20-30% of the subject's resting heart rate.

[0095] Example processing

[0096] Figure 9 An example process 900 for monitoring a user's health status using physiological sensors is shown. In some implementations, process 900 may be performed by the medical monitoring system 100 described in this disclosure.

[0097] In processing 900, the system receives 902 acceleration data, activity classification data, and vital sign measurements indicative of the subject's physical activity from wearable medical sensors. Based on the acceleration data, activity classification data, and vital sign measurements, the system determines 904 a physical activity score, a sleep score, and a vital sign score. Based on the physical activity score, sleep score, and vital sign score, the system determines 906 the subject's quality of life score. The system then causes 908 to present the quality of life score to the subject using a display screen. The system stores 910 the data structure representing the quality of life score on a hardware storage device.

[0098] In some implementations, the system determines that the quality of life score is less than a threshold quality of life score; and in response to determining that the quality of life score is less than the threshold quality of life score, the system causes an alarm to be generated indicating that the quality of life score is less than the threshold quality of life score.

[0099] In some implementations, the system receives heart rate data and specified step count data from wearable medical sensors, where a vital signs score is determined based on the heart rate data, step count, and activity classification data.

[0100] In some implementations, determining vital signs scores includes receiving sleep metrics from wearable medical sensors; determining a resting vital signs score while awake based on activity classification data, acceleration data, and heart rate data; determining a sleep vital signs score based on activity classification data, acceleration data, sleep metrics, and heart rate data; determining a walking vital signs score based on activity classification data, acceleration data, step count, and heart rate data; and determining a vital signs score based on a resting vital signs score while awake, a sleep vital signs score, and a walking vital signs score.

[0101] In some implementations, determining physical activity scores includes determining walking scores and sedentary scores.

[0102] In some implementations, the system receives data from wearable medical sensors for a specified number of steps, where a walking score is determined based on the number of steps, acceleration data, and activity classification data; and a sedentary score is determined based on the activity classification data and the activity count based on the acceleration data.

[0103] In some implementations, activity counts represent the subject’s movement based on the magnitude of acceleration over a time interval.

[0104] In some implementations, the system filters activity classification data to extract low-activity samples when the subject is awake, where the sedentary score includes a count of consecutive low-activity samples when the subject is awake.

[0105] In some implementations, walking scores include step count scores, purposeful walking scores, light walking scores, moderate walking scores, and vigorous walking scores, where the purposeful walking scores, light walking scores, moderate walking scores, and vigorous walking scores are each determined based on the number of steps taken in a time interval.

[0106] In some implementations, determining a sleep score involves determining one or more of a sleep duration score, a sleep latency score, and a sleep efficiency score based on sleep metrics.

[0107] In some implementations, the system determines one or more recommendations for improving the health of the subject based on the quality of life score, and the one or more recommendations are presented to the subject.

[0108] Example computer system

[0109] Figure 10 An example computing system according to an implementation of this disclosure is depicted. System 1000 can be used for any of the operations described with respect to the various implementations discussed herein. System 1000 may include one or more processors 1010, memory 1020, one or more hardware storage devices 1030, and one or more input / output (I / O) devices 1060 controllable via one or more I / O interfaces 1040. The various components 1010, 1020, 1030, 1040, or 1060 may be interconnected via at least one system bus 1050, which enables data transfer between various modules and components of system 1000.

[0110] One or more processors 1010 may be configured to process instructions for execution within system 1000. One or more processors 1010 may include one or more single-threaded processors, one or more multi-threaded processors, or both. One or more processors 1010 may be configured to process instructions stored in memory 1020 or on storage device 1030. One or more processors 1010 may include one or more hardware-based processors, each including one or more cores. One or more processors 1010 may include one or more general-purpose processors, one or more special-purpose processors, or both.

[0111] Memory 1020 can store information within system 1000. In some implementations, memory 1020 includes one or more computer-readable media. Memory 1020 can include any number of volatile memory cells, any number of non-volatile memory cells, or both. Memory 1020 can include read-only memory, random access memory, or both. In some examples, memory 1020 can be used as active or physical memory by one or more software modules.

[0112] One or more storage devices 1030 may be configured to provide (e.g., persistent) mass storage to system 1000. In some implementations, one or more storage devices 1030 may include one or more computer-readable media. For example, one or more storage devices 1030 may include a floppy disk device, a hard disk device, an optical disk device, or a magnetic tape device. One or more storage devices 1030 may include read-only memory, random access memory, or both. One or more storage devices 1030 may include one or more of an internal hard disk drive, an external hard disk drive, and a removable drive.

[0113] One or more of memory 1020 or storage devices 1030 may include one or more computer-readable storage media (CRSM). 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. CRSM can provide storage for computer-readable instructions describing data structures, processes, applications, programs, other modules, or other data used for the operation of system 1000. In some implementations, CRSM may include data storage that provides storage for computer-readable instructions or other information in a non-transitory format. CRSM may be incorporated into system 1000 or may be external to system 1000. 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, and flash memory devices; disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. In some examples, (one or more) processors 1010 and memory 1020 may be supplemented by or incorporated into one or more application-specific integrated circuits (ASICs).

[0114] System 1000 may include one or more I / O devices 1060. One or more I / O devices 1060 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 devices. In some examples, one or more I / O devices 1060 may also include one or more output devices, such as a display, one or more LEDs, audio output devices (e.g., speakers), printers, and haptic output devices. One or more I / O devices 1060 may be physically incorporated into one or more computing devices of system 1000, or may be external to one or more computing devices of system 1000.

[0115] System 1000 may include one or more I / O interfaces 1040 to enable components or modules of system 1000 to control, interact with, or otherwise communicate with (one or more) I / O devices 1060. The I / O interfaces 1040 may enable information to be transmitted within or outside system 1000 or between components of system 1000 via serial communication, parallel communication, or other types of communication. For example, the I / O interfaces 1040 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 1040 may be configured to provide connectivity via a Universal Serial Bus (USB) or Ethernet. In some examples, (one or more) I / O interfaces 1040 can be configured to provide a serial connection compliant with the IEEE 1394 standard.

[0116] One or more I / O interfaces 1040 may also include one or more network interfaces that enable communication between computing devices in system 1000 or between system 1000 and other network-connected computing systems. One or more network interfaces may include one or more network interface controllers (NICs) or other types of transceiver devices configured to send and receive communications on one or more networks using any network protocol.

[0117] The computing devices of System 1000 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 institutional or personal intranets, 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 communication networks (e.g., 3G, 4G, Edge, etc.). In some implementations, communication between computing devices can be encrypted or otherwise protected. For example, communication can employ one or more public or private encryption keys, passwords, digital certificates, or other credentials supported by security protocols such as Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocols.

[0118] System 1000 may include any number of computing devices of any type. One or more computing devices may include, but are not limited to: personal computers, smartphones, tablet computers, wearable computers, implantable computers, mobile gaming devices, e-book readers, automotive 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-package (SiP), etc. Although the examples herein may describe one or more computing devices as one or more physical devices, implementations are not limited thereto. In some examples, a computing device may 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 may include clusters, clouds, farms, or other groups of multiple devices that coordinate operations to provide load balancing, failover support, parallel processing capabilities, shared storage resources, shared networking capabilities, or other aspects.

[0119] This specification uses the term "configured to" in conjunction with system and computer program components. For a system of one or more computers configured to perform a particular operation or action, this means that the system has software, firmware, hardware, or a combination thereof installed on it that, in operation, causes the system to perform that operation or action. For a computer program configured to perform a particular operation or action, this means that one or more programs include instructions that, when executed by a data processing device, cause the device to perform that operation or action.

[0120] Embodiments of the subject matter and functional operation described in this specification may be implemented in digital electronic circuits, in tangibly embodied computer software or firmware, in computer hardware including the structures disclosed in this specification and their equivalents, or in one or more of these combinations. Embodiments of the subject matter described in this specification may 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 a data processing device or for controlling the operation of a data processing device. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or one or more of these combinations. Alternatively or additionally, program instructions may be encoded on artificially generated propagation signals (e.g., machine-generated electrical, optical, or electromagnetic signals) generated to encode information for transmission to a suitable receiver device for execution by the data processing device.

[0121] The term "data processing device" refers to data processing hardware and encompasses all kinds of devices, apparatuses, and machines used for processing data, including, by way of example, programmable processors, computers, or multiple processors or computers. The device may also be or further include special-purpose logic circuitry, such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits). In addition to hardware, the device may optionally include code that creates an execution environment for computer programs, such as code constituting processor firmware, protocol stacks, database management systems, operating systems, or combinations thereof.

[0122] Computer programs (which may also be referred to or described as programs, software, software applications, apps, modules, software modules, scripts, or code) can be written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages); and computer programs can be deployed in any form (including as standalone programs or as modules, components, subroutines, or other units suitable for use in a computing environment). A program may, but does not necessarily, correspond to a file in a file system. A program may be stored as part of a file holding other programs or data (e.g., stored in one or more scripts within a markup language document), stored in a single file dedicated to the program in question, or stored in multiple coordinating files (e.g., files storing one or more modules, subroutines, or code sections). Computer programs can be deployed to execute on a single computer or on multiple computers located at a single site or distributed across multiple sites and interconnected via a data communication network.

[0123] In this specification, the term "database" is used broadly to refer to any collection of data: data that does not need to be structured in any particular way, or does not need to be structured at all, and can be stored on storage devices in one or more locations. Therefore, for example, an indexed database may include multiple data collections, which can be organized and accessed differently.

[0124] Similarly, in this specification, the term "engine" is used broadly to refer to a software-based system, subsystem, or process programmed to perform one or more specific functions. Typically, an engine will be 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 will be dedicated to a particular engine; in others, multiple engines may be installed and run on the same one or more computers.

[0125] The processing and logic flow described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by manipulating input data and generating outputs. The processing and logic flow can also be performed by dedicated logic circuitry (e.g., FPGA or ASIC), or by a combination of dedicated logic circuitry and one or more programmable computers.

[0126] A computer suitable for executing computer programs can be based on a general-purpose microprocessor, a special-purpose microprocessor, or both, or any other type of central processing unit (CPU). Typically, the CPU receives instructions and data from read-only memory (ROM) or random access memory (RAM), or both. The basic components of a computer are the CPU for making or executing instructions and one or more memory devices for storing instructions and data. The CPU and memory may be supplemented by or incorporated into special-purpose logic circuitry. Typically, a computer will also include one or more mass storage devices (e.g., disks, magneto-optical disks, or optical disks) for storing data, or operatively coupled to receive data from or transfer data to one or more mass storage devices (e.g., disks, magneto-optical disks, or optical disks), or both. However, a computer does not necessarily need to have such devices. Furthermore, a computer can be embedded in another device (e.g., 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 a few).

[0127] 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 EPROM, EEPROM, and flash memory devices; magnetic disks, such as internal hard disks or removable disks; magneto-optical disks; and CD ROMs and DVD-ROMs.

[0128] To provide interaction with the user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) and a keyboard and pointing device (e.g., a mouse or trackball) via which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback, such as visual, auditory, or tactile feedback; and input from the user can be received in any form, including acoustic, voice, or tactile input. Additionally, the computer can interact with the user by sending documents to and receiving documents from the device used by the user; for example, by sending a webpage to a web browser on the user's device in response to a request received from a web browser. Furthermore, the computer can interact with the user by sending text messages or other forms of messages to a personal device (e.g., a smartphone running a messaging application) and receiving response messages from the user in response.

[0129] Embodiments of the subject matter described in this specification can be implemented in a computing system that includes back-end components (e.g., as a data server), or middleware components (e.g., an application server), or front-end components (e.g., a client computer with a graphical user interface, web browser, or 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 via digital data communication (e.g., a communication network) of any form or medium. Examples of communication networks include local area networks (LANs) and wide area networks (WANs) (e.g., the Internet).

[0130] A computing system may include clients and servers. Clients and servers are typically geographically separated and interact via a communication network. The client-server relationship is established by means of computer programs running on respective computers and having a client-server relationship with each other. In some embodiments, the server transmits data (e.g., HTML pages) to a user device, for example, for the purpose of displaying data to a user interacting with the device acting as a client and receiving user input from that user. Data generated at the user device (e.g., the result of user interaction) can be received from that device at the server.

[0131] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope that may be claimed, but rather as descriptions of features specific to particular embodiments of a particular invention. Certain features described in the specification within the context of separate embodiments may also be implemented in combination in a single embodiment. On the other hand, various features described in the context of a single embodiment may also be implemented separately or in any suitable sub-combination in multiple embodiments. Furthermore, although features may be described above as functioning in certain combinations and even initially claimed in this way, one or more features from a claimed combination may, in some cases, be removed from that combination, and a claimed combination may refer to a sub-combination or a variation of a sub-combination.

[0132] Similarly, although operations are depicted in the accompanying drawings and described in the claims in a specific order, this should not be construed as requiring such operations to be performed in the specific order shown or in sequence, or requiring all illustrated operations to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0133] Specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. For example, the actions recited in the claims may be performed in a different order and still achieve the desired result. As an example, the processes depicted in the figures do not necessarily require the specific order or sequence shown to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous.

Claims

1. A medical system comprising: Wearable medical sensors, including accelerometers and vital signs sensors; Display screen; Hardware storage devices; as well as At least one processor and a memory storing instructions, the instructions causing operations to be performed when executed by the at least one processor, the operations including: The wearable medical sensor receives acceleration data, activity classification data, sleep data, and one or more vital sign measurements that indicate the subject's physical activity. The physical activity score, sleep score, and vital sign score are determined based on the acceleration data, the activity classification data, the sleep data, and one or more vital sign measures. The subject's quality of life score is determined based on the physical activity score, the sleep score, and the vital signs score; The data structure representing the quality of life score is stored in the hardware storage device; and The visual representation of the quality of life score is then drawn on the display screen.

2. The medical system according to claim 1, wherein, The operation also includes: The quality of life score is determined to be less than the threshold quality of life score; and In response to determining that the quality of life score is less than the threshold quality of life score, an alarm indicating that the quality of life score is less than the threshold quality of life score is generated.

3. The medical system according to claim 1, wherein, The operation also includes: Determine multiple quality of life scores for the subjects at multiple time points; and The variability of the subject's quality of life is determined based on the multiple quality of life scores.

4. The medical system according to claim 3, wherein, The operation also includes: The baseline quality of life of the subjects was determined based on the multiple quality of life scores; After determining the baseline quality of life, one or more subsequent quality of life scores are determined; and It is determined that one or more subsequent quality of life scores deviate from the baseline quality of life by a threshold amount.

5. The medical system according to claim 1, wherein, The vital signs sensor includes at least one of a heart rate sensor and a pulse rate sensor; as well as The operation further includes receiving heart rate data and a specified step count from the wearable medical sensor, wherein the vital signs score is determined based on the heart rate data, the step count, and the activity classification data.

6. The medical system according to claim 5, wherein, Determining the vital signs score includes: Sleep measurements are received from the wearable medical sensor; The resting vital signs score while awake is determined based on the activity classification data, the acceleration data, and the heart rate data. A sleep vital signs score is determined based on the activity classification data, the acceleration data, the sleep metrics, and the heart rate data. A walking vital signs score is determined based on the activity classification data, the acceleration data, the step count, and the heart rate data; and The vital signs score is determined based on the resting vital signs score while awake, the sleep vital signs score, and the walking vital signs score.

7. The medical system according to claim 1, wherein, Determining the physical activity score includes determining the walking score and the sedentary score.

8. The medical system according to claim 7, wherein, The operation also includes: Receive data for a specified number of steps from the wearable medical sensor. The walking score is determined based on the step count, the acceleration data, and the activity classification data; and The sedentary score is determined based on the activity classification data and the activity count based on the acceleration data.

9. The medical system according to claim 8, wherein, The activity count represents the subject's movement, determined based on the magnitude of the acceleration within the time interval.

10. The medical system according to claim 9, wherein, The operation also includes: Filter the activity classification data to extract low-activity samples when the subject is awake. The sedentary score includes the count of consecutive low-activity samples while the subject is awake.

11. The medical system according to claim 8, wherein, The walking score includes: Step count score, purposeful walking score, light walking score, moderate walking score, and vigorous walking score. The walking scores among the purposeful walking score, the light walking score, the moderate walking score, and the vigorous walking score are determined based on the number of steps taken within a time interval.

12. The medical system according to claim 6, wherein, Determining the sleep score includes determining one or more of a sleep duration score, a sleep latency score, and a sleep efficiency score based on the sleep metrics.

13. The medical system according to claim 1, wherein, The operation also includes: Based on the quality of life score, one or more recommendations are determined for improving the health of the subject, and the one or more recommendations are presented to the subject.

14. A method for determining the quality of life of a subject, the method comprising: The wearable medical sensor receives acceleration data, activity classification data, sleep data, and one or more vital sign measurements that indicate the subject's physical activity. The physical activity score, sleep score, and vital sign score are determined based on the acceleration data, the activity classification data, the sleep data, and one or more vital sign measures. The subject's quality of life score is determined based on the physical activity score, the sleep score, and the vital signs score; as well as This allows the quality of life score to be presented to the subject using a display screen.

15. The method of claim 14, further comprising: The subjects were monitored based on their quality of life scores.

16. The method of claim 14, further comprising: The quality of life score is determined to be less than the threshold quality of life score; as well as In response to determining that the quality of life score is less than the threshold quality of life score, an alarm indicating that the quality of life score is less than the threshold quality of life score is generated.

17. The method of claim 14, further comprising: The wearable medical sensor receives heart rate data and a specified step count, wherein the vital signs score is determined based on the heart rate data, the step count, and the activity classification data.

18. The method according to claim 17, wherein, Determining the vital signs score includes: Sleep measurements are received from the wearable medical sensor; The resting vital signs score while awake is determined based on the activity classification data, the acceleration data, and the heart rate data. A sleep vital signs score is determined based on the activity classification data, the acceleration data, the sleep metrics, and the heart rate data. A walking vital signs score is determined based on the activity classification data, the acceleration data, the step count, and the heart rate data; and The vital signs score is determined based on the resting vital signs score while awake, the sleep vital signs score, and the walking vital signs score.

19. The method of claim 14, wherein, Determining the physical activity score includes determining the walking score and the sedentary score.

20. The method of claim 19, further comprising: Receive data for a specified number of steps from the wearable medical sensor. The walking score is determined based on the step count, the acceleration data, and the activity classification data; and The sedentary score is determined based on the activity classification data and the activity count based on the acceleration data.

21. The method according to claim 20, wherein, The activity count represents the subject's movement, determined based on the magnitude of the acceleration within the time interval.

22. The method of claim 21, further comprising: Filter the activity classification data to extract low-activity samples when the subject is awake. The sedentary score includes the count of consecutive low-activity samples while the subject is awake.

23. The method of claim 20, wherein, The walking score includes: Step count score, purposeful walking score, light walking score, moderate walking score, and vigorous walking score. The walking scores among the purposeful walking score, the light walking score, the moderate walking score, and the vigorous walking score are determined based on the number of steps taken within a time interval.

24. The method according to claim 18, wherein, Determining the sleep score includes determining one or more of a sleep duration score, a sleep latency score, and a sleep efficiency score based on the sleep metrics.

25. The method of claim 14, further comprising: Based on the quality of life score, one or more recommendations are determined for improving the health of the subject, and This results in one or more recommendations being presented to the subject.

26. One or more non-transitory computer-readable storage media storing instructions that, when executed by at least one processor, cause the at least one processor to perform the method according to any one of claims 14-25.

27. A computer program product that stores instructions that, when executed by at least one processor, cause the at least one processor to perform the method according to any one of claims 14-25.

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