Medical system with physiological sensors

Abstract

To provide a medical system with physiological sensors.SOLUTION: In an example method, a computer system receives, from a wearable medical sensor, acceleration data indicating physical activity of a subject, activity classification data, sleep data, and one or more vital sign metrics. The system determines a physical activity score, a sleep score, and a vital signs score based on the acceleration data, the activity classification data, the sleep data, and the 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 signs score; and the system causes the quality of life score to be presented to the subject using a display screen.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] FIELD OF THE INVENTION This disclosure relates generally to medical systems with physiological sensors. [Background technology]

[0002] Generally, a user's health-related quality of life 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 with physiological characteristics that meet or exceed a particular health standard may have a high quality of life, while a user with physiological characteristics that do not meet the health standard may have a low quality of life. Summary of the Invention

[0003] Generally, wearable medical systems can be used to monitor a user's health-related quality of life and to facilitate the user's treatment.

[0004] In one example implementation, a medical monitoring system includes a sensor device configured to acquire sensor data representative of one or more physiological characteristics of a user, and one or more processor modules (e.g., computer processors) configured to process the sensor data and determine one or more metrics representative of quality of life aspects related to the user's physical health.

[0005] For example, the medical monitoring system can acquire sensor data indicative of a user's physical activity level, sleep metrics, and / or vital signs. For example, the sensor data can include measurements of the user's physical activity, such as the number of steps taken over a period of time, and / or measurements of cardiac activity, such as the user's heart rate or pulse rate over a period of time (e.g., acquired by one or more cardiac sensors). Further, the sensor data can include measurements of the user's movement over a period of time (e.g., acceleration data acquired using one or more acceleration sensors). Based on these measurements, the medical monitoring system can determine metrics representative of quality of life related to the user's physical health. Furthermore, the medical monitoring system can present the metrics to the user or another user (e.g., a healthcare provider) to facilitate the user's treatment and / or monitoring of the user's health over time.

[0006] 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 physical health-related quality of life based on sensor data (e.g., cardiac data, acceleration data, etc.) without requiring manual feedback from a human. Furthermore, rather than relying on human interpretation of the input data, which may be poorly suited for computer implementation, medical monitoring systems can provide this functionality by performing computer-specific operations on input data in an objective manner (and in a manner that is infeasible for humans to perform).

[0007] As another example, embodiments described herein can be used to facilitate user monitoring and treatment (e.g., during clinical trials of pharmaceutical compositions or other interventions), thereby improving the user's physical health-related quality of life and / or wellness. For example, a medical monitoring system can generate metrics representative of a user's physical health-related quality of life and provide the metrics to the user or another user (e.g., a healthcare provider). The physical health-related quality of life metrics determined by the medical monitoring system can provide an objective measure of the user's physical health-related quality of life to reduce variability and bias from subjective user responses to, for example, questionnaires or surveys. Based on this information, the user or another user can take proactive steps to improve the user's physical health-related quality of life, 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.

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

[0009] Implementations of this aspect may include one or more of the following features.

[0010] Some implementations include storing a data structure representing the quality of life score in a hardware storage device.

[0011] Some implementations include determining that the quality of life score is 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, generating an alert 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 a variance in the subject's quality of life based on the multiple quality of life scores.

[0013] Some implementations include determining a baseline quality of life for the subject 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 more than a threshold amount.

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

[0015] Some implementations include receiving heart rate data and data specifying the number of steps from a wearable medical sensor, and determining a vital sign score based on the heart rate data, the number of steps, and the activity classification data.

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

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

[0018] Some implementations include receiving data specifying the number of steps from the wearable medical sensor, wherein a walking score is determined based on the number of steps, the acceleration data, and the activity classification data, and a sedentary score is determined based on the activity classification data and the number of activities based on the acceleration data.

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

[0020] Some implementations include filtering the activity classification data to extract low activity samples while the subject is awake, and the sedentary score includes the number of consecutive low activity samples while the subject is awake.

[0021] In some implementations, the walking score includes a step score, a purposeful walking score, a light walking score, a moderate walking score, and a vigorous walking score, each of which is determined based on the number of steps 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 subject's health based on the quality of life score and having the one or more recommendations presented to the subject.

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

[0025] 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.

[0026] 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]

[0027] [Figure 1] FIG. 1 illustrates an exemplary medical monitoring system. [Figure 2] FIG. 1 is a flowchart diagram of an exemplary method of using a medical monitoring system to determine a quality of life score related to a user's physical health. [Figure 3] FIG. 1 is a flowchart diagram of an exemplary method for determining a physical activity score. [Figure 4] FIG. 1 is a flowchart diagram of an exemplary method for determining a walking score. [Figure 5] FIG. 1 is a flowchart diagram of an exemplary method for determining a locus score. [Figure 6] FIG. 1 is a flowchart diagram of an exemplary method for determining a sleep score. [Figure 7] FIG. 1 is a flowchart diagram of an exemplary method for determining a vital sign score. [Figure 8]FIG. 10 is a flowchart diagram of another exemplary method for determining a vital sign score. [Figure 9] FIG. 1 is a flowchart diagram of an exemplary process for monitoring a user's physical health-related quality of life. [Figure 10] FIG. 1 is a diagram of an exemplary computer system. DETAILED DESCRIPTION OF THE INVENTION

[0028] Like reference numbers and designations in the various drawings indicate like elements.

[0029] 1 illustrates an exemplary medical monitoring system 100 for automatically monitoring a user's physical health-related quality of life. The medical monitoring system 100 includes a sensor device 110 and an electronic device 120 communicatively connected to each other (e.g., via one or more wired or wireless communication links 150). Generally, the medical monitoring system 100 acquires sensor data about the user using the sensor device 110 and processes the sensor data using the electronic device 120 to determine one or more scores representative of the user's health status. The one or more scores can represent the user's health status based on health criteria.

[0030] The sensor device 110 includes one or more sensors configured to obtain measurements related to the user's physiology, the user's physical behavior (e.g., physical activity, sleep patterns), and / or any other characteristic of the user.

[0031] 1, 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).

[0032] Additionally, the sensor device 110 may include one or more cardiac sensors 112 configured to acquire sensor data representative of the user's cardiac activity. In some implementations, the cardiac sensors 112 may include one or more electrocardiogram (ECG) sensors and / or one or more photoplethysmography (PPG) sensors. In some implementations, the cardiac sensors 112 may acquire sensor data at or above 64 Hz, above 125 Hz, or up to (or about) 250 Hz.

[0033] 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.).

[0034] 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 adhesive or straps). Further, the cardiac sensor 112, the accelerometer 114, and the communication module 116 may be attached to the substrate such that they are secured to the user's body. In some implementations, at least a portion of the substrate 118 may be composed of a compliant material, such as silicone, rubber, elastomer, and / or any other flexible material. In some implementations, at least a portion of the compliant substrate 118 may be composed of a rigid material, such as a hard metal, a hard plastic, or the like.

[0035] Generally, electronic device 120 is configured to receive sensor data acquired by sensor device 110 and process the sensor data to determine one or more metrics representative of the user's health status. Further, electronic device is configured to present information regarding the score and any other information to the user and / or another user (e.g., a healthcare provider).

[0036] 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).

[0037] 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.

[0038] 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).

[0039] The communications module 124 is configured to transmit data to and / or receive data from the sensor device 110. By way of example, the communications module 124 may include one or more receivers, transmitters, and / or transceivers. In some implementations, the communications module 124 may communicate with the sensor device 110 (e.g., via the communications module 116) over one or more wireless links (e.g., serial links, Ethernet links, etc.) and / or wireless links (e.g., Wi-Fi links, Bluetooth links, etc.).

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

[0041] As an example, the database module 122 may store input data 122a used to determine one or more metrics representative of a user's health. 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.).

[0042] As another example, the database module 122 can store output data 122b generated by the electronic device 120. As an example, the output data 122b can include one or more metrics generated by the electronic device 120 based on the input data 122a.

[0043] 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.

[0044] 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 scores for the user.

[0045] 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.

[0046] 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).

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

[0048] 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).

[0049] The user interface module 128 is configured to present information to a user and / or receive input from a user. As an example, the user interface module 128 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).

[0050] 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. Furthermore, a user can interact with the application to input data and / or commands to electronic device 120 and to review data generated by electronic device 120.

[0051] FIG. 2 illustrates an exemplary method 200 for determining a user's physical health-related quality of life using the medical monitoring system 100.

[0052] In this example, sensor device 110 is worn by a user (e.g., attached to the user's skin or fastened to the user's wrist). Sensor device 110 measures physiological characteristics of the user while the user is wearing sensor device 110. The physiological characteristics may include data indicative of the user's physical health, such as acceleration data, activity classification, vital sign measurements, number of steps, sleep state, sleep patterns, heart rate, etc. For example, sensor device 110 continuously obtains measurements of the user's physiological characteristics while the user is wearing sensor device 110.

[0053] The sensor device 110 outputs the 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 the electronic device 120. The standardized data format and data cleaning module 202 formats the measured data from the sensor device 110 into a common format for use in determining the user's health-related quality of life score. For example, sensor metric standardization converts each specific sensor data type, column name, and sample frequency resolution into a standardized data format. The standardized data format allows data obtained from multiple sensors and / or sensor types to be processed in a consistent manner. The data cleaning portion of module 202 includes correcting data errors, removing noise, handling (e.g., removing) data outliers, and determining whether the data is suitable for further analysis. Module 202 can determine whether the data is suitable for further analysis based, for example, on the number of samples per day available in the data and / or irregular values ​​per unit time (e.g., values ​​per unit time that are too high or too low). The module 202 can also generate a log of the data cleaning activities, including the actions taken and any issues encountered, which can be used to generate reports summarizing the data cleaning process and its results.

[0054] The module 202 outputs the formatted and cleaned data into subsets (e.g., metric groups) to multiple quality of life scoring blocks. For example, in each quality of life scoring block, a normalized score having a value ranging from 0 to 100 is determined based on the input data. In some implementations, the scores can be normalized to cover different value ranges (e.g., 0 to 1, 0 to 1000, etc.).

[0055] The module 202 outputs the first data subset 204 to a physical activity quality block 206. The physical activity quality block 206 determines a physical activity score 208 based on the acceleration data from the sensor device 110. Further details of the physical activity quality block 206 are described with reference to Figures 3-5.

[0056] The module 202 outputs the second data subset 210 to a sleep quality block 212. The sleep block 212 determines a sleep score for the user based on the sleep data from the sensor device 110. For example, based on the sleep data, sleep duration, sleep latency, and sleep efficiency are derived. The sleep block 212 determines the sleep score based on one or more of the sleep duration, sleep latency, and sleep efficiency. Further details of the sleep quality block 212 are described with reference to FIG. 6.

[0057] The module 202 outputs the third data subset 216 to a vital sign quality block 218. The vital sign quality block 218 determines a vital sign 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 sign score 220 can be used to indicate the user's health and well-being during various daily activities. For example, by considering heart rate patterns during sleep, periods of waking rest, periods of low physical activity, and periods of high physical activity, the vital sign score 220 provides valuable insight into the ability of the user's physiological systems to respond appropriately to daily human activities. This ultimately facilitates a more holistic understanding of an individual's health and quality of life. Further details of the vital sign quality block 218 are described with reference to FIGS. 7-8.

[0058] The physical activity score 208, sleep score 214, and vital signs score 220 are combined in an overall quality of life score block 222 to determine the user's overall health-related quality of life score. For example, each of the aspect scores 208, 214, 220 is averaged to generate the overall quality of life score. The overall quality of life score can range from 0 to 100. In some implementations, the overall quality of life score can be generated using a weighted average, in which a weight is assigned to each of the aspect scores 208, 214, 220. Other methods of combining the aspect scores 208, 214, 220 are also possible. For example, a nonlinear weighted sum can be used, in which a nonlinear function is applied to each aspect score before summing, a regression model can be used to predict the overall score based on the aspect scores, or a category score or cluster score can be determined based on the overall score associated with a grouping of the aspect scores.

[0059] In some implementations, a user can receive monitoring based on their health-related quality of life score. For example, a healthcare provider can provide recommendations (e.g., recommended activities, behavioral changes) to the user to improve their health-related quality of life score.

[0060] 3-5 are flowcharts of an exemplary method for determining a physical activity score 208. The physical activity quality block 206 receives as input a first data subset 204 including a step count 302, an activity classification 304, and an activity number 306. The step count 302 includes the number of steps taken by the user, derived, for example, from acceleration data. The step count 302 may also include a time associated with the number of steps taken to determine a step rate (e.g., steps per minute, steps per hour). The activity classification 304 includes classifying the activity into one of a number of classes. For example, the activity classes may include walking, sitting, sleeping, resting, moving, sitting, leaning, standing, etc. The activity number 306 indicates the intensity of the user's physical activity. The physical activity quality block 206 uses two sub-blocks, a walking quality of life 308 and a sedentary quality of life 310, to determine the overall physical activity score 208. The walking quality of life block 308 provides insight into the user's daily walking ability, and the sedentary quality of life block 310 informs about the user's daily sedentary behavior (e.g., periods of inactivity).

[0061] The walking quality of life block 308 receives step counts 302 and activity classification 304 as input. A data preprocessing step 312 prepares the input data for input to the subscoring blocks 318-326. The data preprocessing step 312 includes step aggregation 314, in which step counts are summed over a specified period (e.g., 1 hour, 1 day, 1 week). The data processing step 312 also includes walk segment detection 316. For example, the activity classification 304 indicates periods when the user is walking. The walk segment detection 316 determines periods when the user is walking (e.g., 2 minutes, 6 minutes, or "n" minutes of walking). The walk segment detection 316 can be used to determine the percentage of steps taken (e.g., steps per minute) during the period indicated as walking.

[0062] Subscoring blocks 318-326 determine the user's walking subscores and combine them to calculate a total walking score 328. Step score 318 receives the total number of steps from step count tally 314 as input. Step score 318 scores the user based on the number of steps taken per day. Step score 318 may indicate a correlation between a certain number of steps taken each day and a reduced risk of all-cause mortality. For example, if the number of steps exceeds a threshold number of steps per day (e.g., 6,000-8,000 steps per day for users 60 years of age or older, or 8,000-10,000 steps per day for users under 60 years of age), the user receives a step score of 1. If the threshold number of steps is not exceeded, the user receives a step score of 0. In some implementations, if the threshold number of steps is not exceeded, the step score is the ratio of the number of steps taken per day to the threshold number of steps.

[0063] The walking segments are input to each of the remaining four subscoring blocks 320-326, which generate subscores based on the user's daily or weekly walking intensity to capture positive health impacts. The intentional walking score 320 indicates whether 30 minutes of intentional walking (e.g., walking less than 1 minute per segment) per day was detected. If so, the user receives an intentional walking score of 1. If not, the user receives an intentional walking score of 0 or the ratio of intentional walking time to the goal (e.g., 30 minutes). Intentional steps can be filtered from accidental steps based on the step rate. For example, steps per minute less than 40 steps per minute are considered accidental, while intentional steps are considered when walking at a rate between 40 steps per minute and 60 steps per minute.

[0064] Light walking score 322 indicates whether a threshold amount (e.g., 30 minutes) of light walking (e.g., greater than 1 minute in duration) per day has been detected. The intensity of the light walking may include, for example, a period of step rate from 60 steps per minute to 100 steps per minute. If the threshold amount of light walking is achieved, the user may receive a score of 1; if the threshold is not achieved, the user may receive a 0 or partial score based on the ratio of minutes of light walking divided by the threshold.

[0065] The moderate walking score 324 and the vigorous walking score 326 indicate whether the user achieved at least 10 minutes of moderate walking intensity (e.g., 100-129 steps / min) or 10 minutes of vigorous walking intensity (e.g., 130 steps / min or greater) per day. For both the moderate walking score 324 and the vigorous walking score 326, the user receives a score of 1 if the threshold is achieved, and a 0 or ratio score if the threshold is not achieved.

[0066] The subscores from the subscoring blocks 318-326 are combined into a total walking score 328, for example, by averaging the subscores. In some implementations, a weighted average of the subscores can be used to determine the total walking score 328. For example, the step count score 318 can receive a higher weighting than the other subscores. An exemplary weighting scheme includes a weight of 0.7 for the step count score, a weight of 0.7 for the purposeful walking score, a weight of 0.7 for the light walking score, a weight of 0.9 for the moderate walking score, and a weight of 1 for the vigorous walking score. In some implementations, if at least one of the subscores achieves a score of 1, the total walking score 328 can be a score of 1. If any of the subscoring blocks achieves a score of 1, the user can reduce all cost-mortality.

[0067] The sedentary quality block 310 considers the amount of time spent in a sedentary mode (e.g., inactive, resting, sitting, etc.) outside of the user's sleep routine. The sedentary quality block 310 receives as input an activity classification 304 and an activity count 306. The input data is preprocessed in block 330. Preprocessing includes a wakefulness filter 332 that filters the activity count 306 for periods of activity classified as wakefulness (e.g., not sleeping). The data is further filtered by a low activity count filter 334 that identifies periods of low activity counts. Sedentary behaviors include daily sedentary time, such as watching television, and periods of sedentary behavior may fall on the low end of the physical activity spectrum (or low activity count) outside of sleep. The activity count segment aggregation 336 finds and aggregates consecutive periods of low activity counts (e.g., data segments indicated as wakefulness activities with low activity counts). The aggregated activity count segments are input to sedentary scoring rules 338 to determine a sedentary quality score. The sedentary score can be determined based on the total amount of time the user spends in sedentary mode. For example, a four-hour threshold can be used to distinguish between bad and good sedentary behavior. For example, if the aggregated sedentary behavior is less than four hours, it can result in a good sedentary score 340 (e.g., 1), while if the aggregated sedentary behavior is more than four hours, it can result in a bad sedentary score 342 (e.g., 0).

[0068] Each of the subscores 318-326, 340-342 can be determined on a binary basis (e.g., 1 if the criterion is met or exceeded, 0 if not) or on a continuous scale (e.g., a ratio based on the percentage of people who meet the criterion). A continuous score can more easily represent a user's ability to achieve habits that are considered beneficial to a good quality of life. Thus, a physical activity score 208 higher than 1 can be interpreted as a strong ability to perform that aspect of daily life. On the other hand, a physical activity score below 1 may indicate a weaker ability to perform that particular aspect.

[0069] The overall physical activity score 208 can be determined by combining the total walking score 328 and 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.

[0070] 6 is a flowchart of an exemplary method for determining a total sleep score 214. The sleep block 212 receives as input a second data subset 210 from the common data and data cleaning module 202. The second data subset 210 includes sleep duration 602 (e.g., the amount of time spent sleeping), sleep latency 604 (e.g., the amount of time from going to bed to falling asleep), and sleep efficiency 606 (e.g., the amount of time spent in bed sleeping). The input data 602-606 are sent to a data preprocessing block 608. The data preprocessing block 608 can format and / or sort the input data 602-606 for input into scoring rules 610-614.

[0071] Scoring rules 610-614 receive data from the data preprocessing block 608. The sleep duration score 610 is based on the amount of sleep time per day. For example, if the sleep duration is 7 hours or more, the sleep duration score 610 is 0; if the sleep duration is between 6 and 7 hours, the sleep duration score 610 is 1; if the sleep duration is between 5 and 6 hours, the sleep duration score 610 is 2; and if the sleep duration is less than 5 hours, the sleep duration score 610 is 3.

[0072] The sleep latency score 612 represents the length of time from when the user gets into bed until when they actually fall asleep. For example, if the sleep latency is 15 minutes or less, the sleep latency score 612 is 0, if the sleep latency is between 15 minutes and 30 minutes, the sleep latency score 612 is 1, if the sleep latency is between 30 minutes and 60 minutes, the sleep latency score 612 is 2, and if the sleep latency is more than 60 minutes, the sleep latency score 612 is 3.

[0073] The sleep efficiency score 614 is based on the ratio of total sleep time to the total time the user is in bed. For example, if the user's sleep efficiency is 85% or greater, the sleep efficiency score 614 is 0, if the sleep efficiency is between 75% and 85%, the sleep efficiency score 614 is 1, if the sleep efficiency is between 65% and 75%, the sleep efficiency score 614 is 2, and if the sleep efficiency is less than 65%, the sleep efficiency score 614 is 3.

[0074] In the previous example, each sleep score 610-614 is a score value ranging from 0 to 3, with 0 being the highest score and 3 being the lowest score. A total sleep score 214 can be determined as the sum of each of the sleep scores 610-614, resulting in a total score ranging from 0 to 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 desired range. The sleep score can be inverted, for example, by subtracting the normalized score from the maximum score within the desired range, so that 0 is a poor sleep score and 1 or 100 is a good sleep score.

[0075] FIG. 7 is a flowchart of an exemplary method 700 for determining vital sign scores. The vital sign quality block 218 receives as input the third data subset 216 from the data format standardization and data cleaning module 202. The third data subset 216 includes vital sign metrics 702 (e.g., heart rate, respiratory rate, blood oxygen level), activity classification 704, and activity count 706. The vital sign quality block 218 identifies whether the user's vital signs are responding as expected to a given specific daily activity. The input data is preprocessed (e.g., filtered, sorted) based on three activity categories, including resting awake vitals 708, sleeping vitals 710, and ambulatory vitals 712. For example, the vital sign metrics 702 of the data are sorted based on the activity classification 704 and activity count 706. After preprocessing, the vitals 708-712 are scored based on various criteria.

[0076] The Pulse Rate / Heart Rate (PR / HR) Resting Outlier Ratio 714 (ROR) indicates the percentage of HR samples that are outside the "normal" range during resting time. This score tallies the durations during which PR / HR is within the "normal" range and the durations during which it is outside the normal range. The outlier percentage is determined based on a comparison with samples that were within the normal range. The second version of PR / HR Resting Outlier Ratio 716 (RORv2) takes into account consecutive rest periods (e.g., rest periods longer than 5 minutes). For both scores 714-716, the score will be between 0% and 100%, with the best possible result represented by 100 (e.g., an outlier rate of 0) and the worst possible result represented by 0 (e.g., an outlier rate of 100).

[0077] A PR / HR sleep outlier 720 (SOR) can be determined similarly to the ROR score and RORv2 score. For SOR 720, the "normal" range of heart rate is shifted to a range appropriate for sleep. For example, the "normal" range of heart rate during sleep may be 20%-30% lower than the resting heart rate of a typical adult. A typical resting heart rate may be, for example, around 50-70 beats per minute. In some implementations, the range can be personalized for the user (e.g., based on the user's past heart rate values).

[0078] Two additional scores 718, 722 are based on the National Early Warning Score (NEWS) framework to determine users at risk for developing sepsis by considering vital signs (e.g., to detect whether the user's organs are functioning poorly). In this framework, a higher score indicates a 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 determined and scored for each day. For example, if the beats per minute (bpm) sample is between 51 and 90, the score is 0 (or normal); if the bpm sample is between 41 and 50 bpm or between 91 and 110 bpm, the score is 1; if the bpm sample is between 111 and 130 bpm, the score is 2; and if the bpm sample is less than 40 bpm or more than 130 bpm, the score is 3. For each segment, two exemplary options for determining the overall resting segment score are determining the average score of the resting segments or determining a score based on the maximum score from the resting segments (representing the worst state during the segment). The overall score for each day can be determined using the same options, e.g., the average score or the maximum score. Using the maximum score may be a more conservative approach.

[0079] For the NEWS-2 walking PR / HR score 722, the process appears similar to that described for the NEWS-2 resting PR / HR score 718, except that the ranges for each score are shifted based on a personalized user baseline offset adjustment. When determining the HR / PR score while walking (or during any form of exercise), the normal or abnormal vital ranges established in the raw National Early Warning Score (NEWS-2) do not apply. These ranges are established only for resting HR / PR. For example, a user's baseline resting heart rate is the average value between the 25th and 75th percentiles (interquartile range or IQR), which captures the middle 50% of the data and is a good measure of typical variability in a person's heart rate. For each walking segment detected during the day, a median walking HR is calculated. From these two values, a difference variable is determined as the difference between the median walking heart rate and the baseline. This difference is added to each of the NEWS-2 range values ​​mentioned above, and the final daily score process works in the same way as described for the NEWS-2 resting PR / HR score 718 .

[0080] The total vital score 220 can be determined based on the sum, average, or weighted average of the various scores 714-722. For example, the total vital score 220 can be a value between 0-1 or 0-100, which represents the average value of the scores 714-722, which themselves are values ​​between 0-1 or 0-100.

[0081] 8 is a flowchart of another example method 800 for determining vital signs scores 220. The vital signs quality block 218 receives as input the vital signs 802 and activity classifications 804 output from the common data model and data cleaning module 202.

[0082] Vital signs 802 are filtered into low activity segments based on activity classification 804. For example, vital signs 802 are filtered based on activity classes of sedentary, resting, and / or sleeping. Vital signs 802 are further sorted into awake activity 808 and sleep activity 810 based on activity classification 804.

[0083] Wake activity 808 is scored based on the resting outlier percentage 812. This score tallies PR / HR periods within the "normal" range with PR / HR periods outside the normal range. The outlier percentage is determined based on a comparison of samples that fell within the normal range (e.g., identifying samples that deviate by more than a threshold from the average HR value within the normal range). The resting outlier percentage 812 can be expressed as a number between 0 and 1 or 0 and 100 indicating the percentage of data samples identified as outliers.

[0084] The sleep outlier rate 814 is based on the sleep activity 810. Similar to the resting outlier rate 812, the sleep outlier rate 814 scores periods of PR / HR within normal sleep ranges and periods of PR / HR outside normal sleep ranges. The sleep outlier rate 814 can be expressed as a number between 0 and 1 or 0 and 100 indicating the percentage of sleep activity identified as an outlier.

[0085] Sleep activity 810 is further scored to determine sleep decrement trend 816. Sleep decrement trend 816 can measure the tendency of heart rate to decrement during sleep. In some implementations, sleep decrement trend is a function of f(x)=1 / x, which has an asymptote at 20-30% of the subject's resting heart rate. 2 It may have a shape similar to a straight line.

[0086] Example Process 9 shows an example process 900 for monitoring a user's health using physiological sensors. In some implementations, the process 900 can be performed by the medical monitoring system 100 described in this disclosure.

[0087] In process 900, the system receives acceleration data, activity classification data, and vital sign metrics indicative of the subject's physical activity from a wearable medical sensor (902). The system determines a physical activity score, a sleep score, and a vital sign score based on the acceleration data, activity classification data, and vital sign metrics (904). The system determines a quality of life score for the subject based on the physical activity score, sleep score, and vital sign score (906). The system presents the quality of life score to the subject using a display screen (908). The system stores a data structure representing the quality of life score in a hardware storage device (910).

[0088] In some implementations, the system determines that the quality of life score is 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, the system causes an alert to be generated indicating that the quality of life score is less than the threshold quality of life score.

[0089] In some implementations, the system receives heart rate data and data specifying the number of steps from the wearable medical sensor, and determines a vital sign score based on the heart rate data, the number of steps, and the activity classification data.

[0090] In some implementations, determining the vital sign score includes receiving sleep metrics from the wearable medical sensor; determining a resting awake vital score based on the activity classification data, acceleration data, and heart rate data; determining a sleep vital score based on the activity classification data, acceleration data, sleep metric, and heart rate data; determining an ambulatory vital score based on the activity classification data, acceleration data, step count, and heart rate data; and determining a vital sign score based on the resting awake vital score, sleep vital score, and ambulatory vital score.

[0091] In some implementations, determining a physical activity score includes determining an ambulation score and a sedentary score.

[0092] In some implementations, the system receives data specifying the number of steps from the wearable medical sensor, a walking score is determined based on the number of steps, the acceleration data, and the activity classification data, and a sedentary score is determined based on the activity classification data and the number of activities based on the acceleration data.

[0093] In some implementations, the activity number represents the subject's movement determined based on the magnitude of acceleration over a time interval.

[0094] In some implementations, the system filters the activity classification data to extract low activity samples while the subject is awake, and the sedentary score includes the number of consecutive low activity samples while the subject is awake.

[0095] In some implementations, the walking score includes a step score, a purposeful walking score, a light walking score, a moderate walking score, and a vigorous walking score, each of which is determined based on the number of steps within a time interval.

[0096] 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.

[0097] In some implementations, the system determines one or more recommendations for improving the subject's health based on the quality of life score and presents the one or more recommendations to the subject.

[0098] Exemplary Computer System 10 illustrates an exemplary computing system according to an implementation of the present disclosure. System 1000 may 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 through at least one system bus 1050, which may enable data transfer between the various modules and components of system 1000.

[0099] The processor 1010 may be configured to process instructions for execution within the system 1000. The processor 1010 may include a single-threaded processor, a multi-threaded processor, or both. The processor 1010 may be configured to process instructions stored in the memory 1020 or the storage device 1030. The processor 1010 may include hardware-based processors, each including one or more cores. The processor 1010 may include a general-purpose processor, a special-purpose processor, or both.

[0100] The memory 1020 may store information within the system 1000. In some implementations, the memory 1020 includes one or more computer-readable media. The memory 1020 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 1020 may include read-only memory, random access memory, or both. In some examples, the memory 1020 may be used as active or physical memory by one or more executing software modules.

[0101] The storage device 1030 may be configured to provide (e.g., persistent) mass storage for the system 1000. In some implementations, the storage device 1030 may include one or more computer-readable media. For example, the storage device 1030 may include a floppy disk device, a hard disk device, an optical disk device, or a tape device. The storage device 1030 may include read-only memory, random access memory, or both. The storage device 1030 may include one or more of an internal hard drive, an external hard drive, or a removable drive.

[0102] Either or both of memory 1020 or storage device 1030 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 that describe data structures, processes, applications, programs, other modules, or other data for operation of system 1000. In some implementations, the CRSM may include a data store that provides non-transitory storage of computer-readable instructions or other information. The CRSM may be incorporated into system 1000 or may be external to system 1000. The CRSM may include read-only memory, random-access memory, or both. The 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, the processor 1010 and memory 1020 may be supplemented by, or incorporated in, one or more application-specific integrated circuits (ASICs).

[0103] System 1000 may include one or more I / O devices 1060. 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 device. In some examples, I / O devices 1060 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 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.

[0104] The system 1000 may include one or more I / O interfaces 1040 to enable components or modules of the system 1000 to control, interface with, or otherwise communicate with I / O devices 1060. The I / O interfaces 1040 may enable the transfer of information within, outside, or between components of the system 1000 through serial, parallel, 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 connections via Universal Serial Bus (USB) or Ethernet. In some examples, the I / O interfaces 1040 may be configured to provide serial connections conforming to a version of the IEEE 1394 standard.

[0105] I / O interface 1040 may also include one or more network interfaces that enable communication between computing devices within system 1000 or between system 1000 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.

[0106] The computing devices of system 1000 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.

[0107] System 1000 may include any number of any type of computing devices. Computing devices may include, but are not limited to, personal computers, smartphones, tablet computers, wearable computers, embedded computers, mobile gaming devices, e-readers, in-vehicle computers, desktop computers, laptop computers, notebook computers, game consoles, home entertainment devices, network computers, server computers, mainframe computers, distributed computing devices (e.g., cloud computing devices), microcomputers, systems-on-chips (SoCs), systems-in-packages (SiPs), etc. Although examples herein describe computing devices as physical devices, implementations are not limited in this respect. In some examples, a computing device may include one or more virtual computing environments, hypervisors, emulations, or virtual machines running on one or more physical computing devices. In some examples, two or more computing devices may include a cluster, cloud, farm, or other group of multiple devices that coordinate operations to provide load balancing, failover support, parallel processing capabilities, shared storage resources, shared network capabilities, or other aspects.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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]

[0123] 100 Medical Monitoring System 110 Sensor device 112 Heart Sensor 114 Accelerometer 116 Communication Module 118 PCB 120 Electronic Devices 122 Database Modules 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 Data format and data cleaning module, Data format standardization and data cleaning module, Common data and data cleaning module, Common data model and data cleaning module 204 First Data Subset 206 Physical Activity Quality Block 208 Physical Activity Score 210 Second Data Subset 212 Sleep Quality Block 212 Sleep Block 214 Total Sleep Score 216 Third Data Subset 218 Vital Signs Quality Block 220 Vital Signs Score 222 Overall Quality of Life Score Block 302 steps 304 Activity classification 306 Number of activities 308 Walking Quality of Life Block 310 Sedentary Quality of Life Block 312 Data Preprocessing Steps 314 Step Count 316 Gait Segment Detection 318 Step Score 320 Purposeful Walking Score 322 Light Walking Score 324 Moderate walking score 326 Strenuous Walking Score 328 Total Walking Score 332 Filtering by Arousal Activity 334 Low Activity Filter 336 Activity Segment Aggregation 338 Sitting Scoring Rules 340 Good Sitting Score 342 poor sitting score 602 Sleep time 604 Sleep latency 606 Sleep Efficiency 608 Data Preprocessing Block 610 Sleep Time Score 612 Sleep Latency Score 614 Sleep Efficiency Score 700 methods 702 Vital Signs Metrics 704 Activity classification 706 Number of activities 708 Vital Signs at Rest and Awake 710 Sleep Vitals 712 Walking Vitals 714 Pulse Rate / Heart Rate (PR / HR) Resting Outlier Rate (ROR) 716 PR / HR resting outlier rate 718 NEWS-2 Resting PR / HR score 720 PR / HR Sleep Outliers (SOR) 722 NEWS-2 Walking PR / HR Score 800 ways 802 Vital Signs 804 Activity classification 808 Awakening Activity 810 Sleep Activities 812 resting outlier rate 814 Sleep Outlier Rate 816 Tendency to decrease sleep 900 processes 1000 systems 1010 processor 1020 memory 1030 Storage Device 1040 I / O interface 1050 system bus 1060 Input / Output (I / O) Devices

Claims

1. Wearable medical sensors with accelerometers and vital signs sensors, Display screen, hardware storage devices, and at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the instructions to perform operations, the operations comprising: receiving acceleration data indicative of a subject's physical activity, activity classification data, sleep data, and one or more vital sign metrics from the accelerometer of the wearable medical sensor; 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 metrics; determining a quality of life score for the subject based on the physical activity score, the sleep score, and the vital signs score; storing a data structure representing the quality of life score on the hardware storage device; rendering a visual representation of the quality of life score on the display screen; and A medical system that includes:

2. The operation is determining that the quality of life score is less than a threshold quality of life score; in response to determining that the quality of life score is less than the threshold quality of life score, generating an alert indicating that the quality of life score is less than the threshold quality of life score; The medical system of claim 1 further comprising:

3. The operation is determining a plurality of quality of life scores for the subject at a plurality of time points; determining a variation in the subject's quality of life based on the plurality of quality of life scores; The medical system of claim 1 further comprising:

4. The instruction: determining a baseline quality of life for the subject based on the plurality of quality of life scores; determining one or more subsequent quality of life scores after determining said baseline quality of life score; determining that the one or more subsequent quality of life scores deviate from the baseline quality of life by more than a threshold amount; The medical system of claim 3 further comprising:

5. the vital signs sensor comprises at least one of a heart rate sensor or a pulse rate sensor; 10. The medical system of claim 1, wherein the operations further comprise receiving heart rate data and data specifying a number of steps from the wearable medical sensor, and wherein the vital sign score is determined based on the heart rate data, the number of steps, and the activity classification data.

6. determining the vital sign score, receiving sleep metrics from the wearable medical sensor; determining a resting awake vital score based on the activity classification data, the acceleration data, and the heart rate data; determining a sleep vital score based on the activity classification data, the acceleration data, the sleep metric, and the heart rate data; determining an ambulatory vital score based on the activity classification data, the acceleration data, the number of steps, and the heart rate data; determining the vital sign score based on the resting awake vital score, the sleep vital score, and the ambulatory vital score; The medical system of claim 5 , comprising:

7. The medical system of claim 1 , wherein determining the physical activity score comprises determining an ambulation score and a sedentary score.

8. The operation is receiving data from the wearable medical sensor specifying a number of steps; the walking score is determined based on the number of steps, the acceleration data, and the activity classification data; The medical system of claim 7 , wherein the sitting position score is determined based on the activity classification data and the number of activities based on the acceleration data.

9. The medical system of claim 8 , wherein the activity number represents the subject's movement determined based on the magnitude of the acceleration over a time interval.

10. The operation is filtering the activity classification data to extract low activity samples while the subject is awake; 10. The medical system of claim 9, wherein the sedentary score comprises a number of consecutive low activity samples while the subject is awake.

11. The walking score is It includes a step score, purposeful walking score, light walking score, moderate walking score, and vigorous walking score. The medical system of claim 8 , wherein each of the intentional walking score, the light walking score, the moderate walking score, and the vigorous walking score is determined based on a number of steps within a time interval.

12. 7. The medical system of claim 6, wherein determining the sleep score comprises determining one or more of a sleep duration score, a sleep latency score, and a sleep efficiency score based on the sleep metric.

13. The operation is determining one or more recommendations for improving the subject's health based on the quality of life score; and causing the subject to present the one or more recommendations; and The medical system of claim 1 further comprising:

14. 1. A method for determining quality of life in a subject, comprising: receiving, from a wearable medical sensor, acceleration data indicative of the subject's physical activity, activity classification data, sleep data, and one or more vital sign metrics; 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 metrics; determining a quality of life score for the subject based on the physical activity score, the sleep score, and the vital signs score; presenting said quality of life score to said subject using a display screen; A method comprising:

15. 15. The method of claim 14, further comprising monitoring the subject based on the quality of life score.

16. determining that the quality of life score is less than a threshold quality of life score; in response to determining that the quality of life score is less than the threshold quality of life score, generating an alert indicating that the quality of life score is less than the threshold quality of life score; The method of claim 14 further comprising:

17. 15. The method of claim 14, further comprising receiving heart rate data and data specifying a number of steps from the wearable medical sensor, and wherein the vital signs score is determined based on the heart rate data, the number of steps, and the activity classification data.

18. determining the vital sign score comprises: receiving sleep metrics from the wearable medical sensor; determining a resting wakefulness vital score based on the activity classification data, the acceleration data, and the heart rate data; determining a sleep vital score based on the activity classification data, the acceleration data, the sleep metric, and the heart rate data; determining an ambulatory vital score based on the activity classification data, the acceleration data, the number of steps, and the heart rate data; determining the vital sign score based on the resting awake vital score, the sleep vital score, and the ambulatory vital score; 20. The method of claim 17, comprising:

19. 15. The method of claim 14, wherein determining the physical activity score comprises determining an ambulation score and a sedentary score.

20. receiving data from the wearable medical sensor specifying a number of steps; the walking score is determined based on the number of steps, the acceleration data, and the activity classification data; The method of claim 19 , wherein the sedentary score is determined based on the activity classification data and an activity count based on the acceleration data.

21. 21. The method of claim 20, wherein the activity number represents movement of the subject determined based on a magnitude of the acceleration over a time interval.

22. filtering the activity classification data to extract low activity samples while the subject is awake; 22. The method of claim 21, wherein the sedentary score comprises a number of consecutive low activity samples while the subject is awake.

23. The walking score is It includes a step score, purposeful walking score, light walking score, moderate walking score, and vigorous walking score.

21. The method of claim 20, wherein each of the intentional walking score, the light walking score, the moderate walking score, and the vigorous walking score is determined based on the number of steps within a time interval.

24. 20. The method of claim 18, wherein determining the sleep score comprises determining one or more of a sleep duration score, a sleep latency score, and a sleep efficiency score based on the sleep metric.

25. determining one or more recommendations for improving the subject's health based on the quality of life score; causing the subject to present the one or more recommendations; The method of claim 14 further comprising:

26. 15. 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 of claim 14.

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