Techniques for measuring compressive toughness using wearable-based data

Abstract

Methods, systems, and devices for measuring compressive toughness are described. A system may obtain heart rate variability (HRV) data from a user via a wearable device over a plurality of time intervals, wherein each time interval includes a waking interval at which the user wakes and a sleeping interval at which the user falls asleep. The system may determine a stress index and a recovery index associated with a waking interval of the respective time interval and a sleep recovery index associated with a sleeping interval of the respective time interval. The system may then determine a compressive toughness metric for the user based on a weighted sum of the pressure index, the recovery index, and the sleep recovery index for multiple time intervals, where the compressive toughness metric indicates a relative ability of the user to handle and / or recover from pressure.

Description

[0001] Cross-references This patent application claims priority to U.S. Patent Application No. 18 / 452,941, filed August 21, 2023, entitled “TECHNIQUES FORMEASURING RESILIENCE TO STRESS USING WEARABLE-BASED DATA”, which has been assigned to the assignee of this application and is expressly incorporated herein by reference. Technical Field

[0002] The following content relates to wearable devices and data processing, including techniques for measuring compressive resilience using wearable-based data. Background Technology

[0003] Some wearable devices can be configured to collect data from users, including heart rate, activity data, temperature data, and photoplethysmography (PPG) data. In some cases, wearable devices can perform various actions, such as providing users with health insights based on the acquired physiological data to help them improve their overall health. However, conventional technologies implemented by wearable devices have limitations. Attached Figure Description

[0004] Figure 1 An example of a system supporting various aspects of this disclosure for using wearable-based data to measure compressive strength is illustrated.

[0005] Figure 2 An example of a system supporting various aspects of this disclosure for using wearable-based data to measure compressive strength is illustrated.

[0006] Figure 3 An example flowchart for evaluating stress-related metrics associated with a user, according to various aspects of this disclosure, is shown.

[0007] Figure 4 An example flowchart for evaluating a user’s acute stress is shown according to various aspects of this disclosure.

[0008] Figure 5 An example of a graphical user interface (GUI) illustrating acute stress in a user, according to various aspects of this disclosure, is shown.

[0009] Figure 6 An example flowchart for determining interpolated heart rate variability (HRV) data is shown according to various aspects of this disclosure.

[0010] Figure 7 An example flowchart for evaluating a user’s cumulative stress is shown according to various aspects of this disclosure.

[0011] Figure 8 An example flowchart for evaluating a user's compressive toughness according to various aspects of this disclosure is shown.

[0012] Figure 9 An example GUI illustrating the compressive strength of a user according to various aspects of this disclosure is shown.

[0013] Figure 10 A block diagram of an apparatus supporting a technique for measuring compressive toughness using wearable-based data is shown, according to various aspects of this disclosure.

[0014] Figure 11 A block diagram is shown that supports various aspects of this disclosure for a wearable application that uses wearable-based data to measure compressive strength.

[0015] Figure 12 A diagram is shown illustrating a system according to various aspects of this disclosure, including a device for supporting a technique of measuring compressive strength using wearable-based data.

[0016] Figure 13 A flowchart illustrating a method for measuring compressive toughness using wearable-based data, according to various aspects of this disclosure, is shown. Detailed Implementation

[0017] Some wearable devices can be configured to collect physiological data from users, such as temperature data, heart rate data, etc. For example, wearable devices can collect heart rate measurements and corresponding heart rate variability (HRV) measurements from users, where HRV is a measure of the fluctuation (e.g., variability) of the time interval between adjacent heartbeats. HRV can be used to calculate various physiological parameters, such as the user's sleep quality. Furthermore, there may be a correlation between a user's HRV data and the user's relative stress or relaxation level.

[0018] However, while some wearable devices can use HRV as input to assess a user's relative sleep quality, they cannot calculate a user's stress level. Specifically, because heart rate data can be inaccurate due to insufficient skin-to-skin contact between the wearable device's sensors and the user's tissue caused by the user's movement, HRV data may not be accurate enough for calculating a user's stress level.

[0019] Therefore, aspects of this disclosure relate to systems and methods for evaluating various stress-related measures associated with users, including (1) acute stress, (2) cumulative stress, and (3) stress resilience. For the purposes of this disclosure, the term "acute stress" can refer to a "real-time" indication of a user's stress when awake and sedentary. In contrast, the term "cumulative stress" can refer to the sum of stress experienced by a user over a prolonged period of time, such as several weeks or months. Finally, the term "stress resilience" can refer to a user's ability to cope with and recover from stress.

[0020] As described herein, a user device, wearable device, or both can calculate a user's acute stress level based on a comparison of the user's daytime HRV with the user's baseline daytime HRV. That is, some aspects of this disclosure focus on determining a real-time indication of a user's stress. In some examples, a wearable device can determine a user's baseline daytime HRV value by calculating a weighted average of the daily median daytime HRV values ​​within a rolling reference window. For example, the wearable device can collect daily HRV measurements over a duration such as three weeks. The wearable device can determine whether the user is awake or sedentary via sensor data and can perform measurements accordingly to improve measurement accuracy.

[0021] The system can calculate a user's acute stress level by comparing their current daytime HRV value with their baseline daytime HRV value. The wearable device can periodically measure HRV throughout the day to obtain the user's current daytime HRV value. As described further in detail herein, the system can use machine learning models to fill in missing or inaccurate data (e.g., using machine learning models to fill in missing or inaccurate HRV measurements). In some cases, the system can determine the difference between the user's current daytime HRV value and their baseline daytime HRV value, and can compare this difference with stress and recovery thresholds to determine the user's acute stress measure. For example, if the difference between the current daytime HRV value and the baseline daytime HRV value exceeds a stress threshold, the user may be in a stressed state. In some other examples, if the difference between the current daytime HRV value and the baseline daytime HRV value is below a recovery threshold, the user may be in a recovery state. Specifically, if the user's daytime HRV is below a stress threshold, the system can classify the time interval as stress-related. Conversely, if the user's daytime HRV exceeds a recovery threshold, the system can classify the time interval as recovery-related. In some cases, the stress threshold and recovery threshold can be the same value. In other cases, the stress threshold and recovery threshold can be different, and there can be one or more corresponding states between stress and recovery states. For example, in some cases, the absolute values ​​of the stress threshold and recovery threshold may be the same, but their signs may be opposite (e.g., +, -). User devices can use a graphical user interface (GUI) to display acute stress levels to the user. Users can change one or more behaviors, such as stress reduction, based on acute stress levels, which can help improve their overall health based on the user's current mental, physical, and emotional state.

[0022] Additional or alternative aspects of this disclosure relate to techniques for evaluating a user's cumulative stress levels over extended time periods, such as several weeks or months. In some cases, the wearable device can collect baseline HRV data over a duration and can determine separate baseline HRV values ​​and / or ranges for the user during the day and night. The daytime values ​​and / or ranges may be referred to as the daytime baseline HRV, and the nighttime values ​​and / or ranges may be referred to as the nighttime baseline HRV. The wearable device can collect HRV data from the user over multiple days and nights, thereby using machine learning models to fill in missing or inaccurate data. The wearable device can transmit the acquired HRV data to the user's device.

[0023] User devices, wearable devices, or both can compare acquired HRV data with an applicable baseline HRV value, specifically comparing HRV data collected during the day (e.g., when the user is awake) with the user's daytime baseline HRV, and comparing HRV data collected during the night (e.g., when the user is asleep) with the user's nighttime baseline HRV. By comparing daytime and nighttime HRV data with corresponding baseline HRV data, the system can calculate the user's daily stress level. Over time, the calculated stress levels can be compared to each other to observe the long-term stress experienced by the user (e.g., cumulative stress), which can be used to predict burnout, identify chronic diseases, etc. In some cases, user devices can use a GUI to display cumulative stress levels (e.g., an indication of the long-term stress experienced by the user) to the user by comparing the stress levels of the previous day with the stress levels of the current day. Users can change one or more behaviors based on cumulative stress levels, such as reducing the risk of burnout, chronic diseases, etc., which can help improve their overall health based on the user's current mental, physical, and emotional state.

[0024] Additional or alternative aspects of this disclosure relate to techniques for evaluating a user's stress resilience (e.g., how a user copes with and / or recovers from stress). Specifically, a user device, wearable device, or both can calculate a user's stress resilience score based on a comparison of the user's stress levels, daytime recovery, and sleep recovery over a multi-day period (e.g., a two-week period). The stress resilience score can indicate a user's ability to manage stress and recover from it. For example, for each 24-hour period of the two-week time interval, the wearable device can acquire physiological data to determine a convergent stress index for the corresponding daytime, where the convergent stress index includes the daytime stress level, the daytime recovery level, and the sleep recovery level for the corresponding nighttime.

[0025] In some cases, the system can determine a user's resilience score by calculating a weighted sum of aggregate stress indices over a two-week period, where the weight of the aggregate stress indices is assigned based on the recentity of the data (e.g., the aggregate stress indices from the most recent daytime period have a greater weight). The user device can provide feedback to the user based on the resilience score. For example, the user device can provide positive feedback to the user on how to improve their resilience score or instruct the user to maintain their current resilience score. Users can change one or more behaviors based on the instructions, which can help improve their ability to cope with stress and improve their overall well-being based on their current mental, physical, and emotional state.

[0026] First, aspects of this disclosure are described within the context of systems supporting the collection of physiological data from users via wearable devices. Additional aspects of this disclosure are described within the context of example processing flows and example GUIs. Aspects of this disclosure are also illustrated and described with reference to apparatus diagrams, system diagrams, and flowcharts relating to techniques for measuring compressive strength using wearable-based data.

[0027] Figure 1 An example of a system 100 supporting a technique for measuring compressive strength using wearable-based data is shown according to various aspects of this disclosure. System 100 includes multiple electronic devices (e.g., wearable device 104, user device 106) that can be worn and / or operated by one or more users 102. System 100 further includes a network 108 and one or more servers 110.

[0028] Electronic devices may include any electronic devices known in the art, including wearable device 104 (e.g., a ring wearable device, a watch wearable device, etc.) and user device 106 (e.g., a smartphone, a laptop computer, a tablet computer). Electronic devices associated with a corresponding user 102 may include one or more of the following functions: 1) measuring physiological data; 2) storing the measured data; 3) processing the data; 4) providing output (e.g., via a GUI) to user 102 based on the processed data; and 5) communicating data with each other and / or with other computing devices. Different electronic devices may perform one or more of these functions.

[0029] Example wearable device 104 may include wearable computing devices, such as ring computing devices (hereinafter referred to as "rings") configured to be worn on the finger of user 102, wrist computing devices (e.g., smartwatches, fitness bands, or bracelets) configured to be worn on the wrist of user 102, and / or head-mounted computing devices (e.g., glasses / goggles). Wearable device 104 may also include cords, straps (e.g., flexible or non-flexible cords or straps), hook and loop sensors, etc., that can be positioned in other locations, such as straps around the head (e.g., forehead bands), arms (e.g., forearm straps and / or double headbands), and / or legs (e.g., thigh or calf straps), behind the ears, under the armpits, etc. Wearable device 104 may also be attached to or included in clothing items. For example, wearable device 104 may be included in pockets and / or pouches on clothing. As another example, wearable device 104 may be clipped and / or pinned to clothing, or may otherwise be held near user 102. Exemplary clothing items may include, but are not limited to, hats, shirts, gloves, trousers, socks, outerwear (e.g., jackets), and underwear. In some implementations, wearable device 104 may be included in other types of equipment, such as training / sports equipment used during physical activity. For example, wearable device 104 may be attached to or included in a bicycle, skis, tennis racket, golf club, and / or training weights.

[0030] Many aspects of this disclosure can be described in the context of the ring wearable device 104. Therefore, unless otherwise indicated herein, the terms "ring 104," "wearable device 104," and similar terms may be used interchangeably. However, the use of the term "ring 104" should not be considered limiting, as it is contemplated herein that various aspects of this disclosure can be implemented using other wearable devices (e.g., watch wearable devices, necklace wearable devices, bracelet wearable devices, earring wearable devices, ankle wearable devices, etc.).

[0031] In some aspects, user equipment 106 may include handheld mobile computing devices, such as smartphones and tablet computing devices. User equipment 106 may also include personal computers, such as laptop and desktop computing devices. Other example user equipment 106 may include server computing devices capable of communicating with other electronic devices, such as via the Internet. In some implementations, the computing device may include medical devices, such as external wearable computing devices (e.g., Holter monitors). Medical devices may also include implantable medical devices, such as pacemakers and defibrillators. Other example user equipment 106 may include home computing devices, such as Internet of Things (IoT) devices (e.g., IoT devices), smart TVs, smart speakers, smart displays (e.g., video call displays), hubs (e.g., wireless communication hubs), security systems, smart appliances (e.g., thermostats and refrigerators), and fitness equipment.

[0032] Some electronic devices (e.g., wearable device 104, user device 106) can measure physiological parameters of the corresponding user 102, such as photoplethysmography waveforms, continuous skin temperature, pulse waveforms, respiratory rate, heart rate, HRV, body motion monitoring, skin conductance response, pulse oxygen saturation, oxygen saturation (SpO2), blood glucose levels (e.g., glucose indicators), and / or other physiological parameters. Some electronic devices that measure physiological parameters may also perform some / all of the calculations described herein. Some electronic devices may not measure physiological parameters but may perform some / all of the calculations described herein. For example, a ring (e.g., wearable device 104), a mobile device application, or a server computing device may process physiological data received from other devices.

[0033] In some implementations, user 102 may operate or be associated with multiple electronic devices, some of which can measure physiological parameters, and some of which can process the measured physiological parameters. In some implementations, user 102 may have a ring (e.g., wearable device 104) for measuring physiological parameters. User 102 may also have or be associated with user device 106 (e.g., a mobile device, a smartphone), wherein wearable device 104 and user device 106 are communicatively coupled to each other. In some cases, user device 106 may receive data from wearable device 104 and perform some / all of the calculations described herein. In some implementations, user device 106 may also measure physiological parameters described herein, such as motion / activity parameters.

[0034] For example, such as Figure 1As shown, a first user 102-a (user 1) can operate, or be associated with, a wearable device 104-a (e.g., ring 104-a) and a user device 106-a that can operate as described herein. In this example, the user device 106-a associated with user 102-a can process / store physiological parameters measured by ring 104-a. In contrast, a second user 102-b (user 2) can be associated with ring 104-b, a watch-wearable device 104-c (e.g., watch 104-c), and user device 106-b, wherein the user device 106-b associated with user 102-b can process / store physiological parameters measured by ring 104-b and / or watch 104-c. Furthermore, an nth user 102-n (user N) can be associated with an arrangement of electronic devices (e.g., ring 104-n, user device 106-n) described herein. In some respects, wearable devices 104 (e.g., ring 104, watch 104) and other electronic devices can be communicatively coupled to user equipment 106 of the corresponding user 102 via Bluetooth, Wi-Fi and other wireless protocols.

[0035] In some implementations, the ring 104 of system 100 (e.g., wearable device 104) can be configured to collect physiological data from the corresponding user 102 based on arterial blood flow within the user's finger. Specifically, the ring 104 may utilize one or more light-emitting components, such as LEDs (e.g., red LEDs, green LEDs), that emit light on the palmar side of the user's finger to collect physiological data based on arterial blood flow within the user's finger. Generally, the terms light-emitting component, light-emitting element, and similar terms may include, but are not limited to, LEDs, micro LEDs, mini LEDs, laser diodes (LDs) (e.g., vertical-cavity surface-emitting lasers (VCSELs), etc.).

[0036] In some cases, system 100 can be configured to collect physiological data from a corresponding user 102 based on blood flow diffusing into the skin's microvascular bed, which has capillaries and arterioles. For example, system 100 can collect PPG data based on the measured blood volume diffusing into the microvascular system of capillaries and arterioles. In some embodiments, ring 104 may use a combination of both green and red LEDs to acquire physiological data. Physiological data may include any physiological data known in the art, including but not limited to temperature data, accelerometer data (e.g., movement / exercise data), heart rate data, HRV data, blood oxygen level data, or any combination thereof.

[0037] The use of both green and red LEDs offers several advantages over other solutions, as they have been found to have distinct strengths in acquiring physiological data under different conditions (e.g., bright / dark, active / inactive) and through different parts of the body. For example, green LEDs have been found to exhibit better performance during exercise. Furthermore, wearable devices using multiple LEDs distributed around a ring 104 (e.g., green and red LEDs) have been found to exhibit superior performance compared to wearable devices using LEDs positioned close together (such as within a watch). Additionally, blood vessels in the fingers (e.g., arteries, capillaries) are more easily accessed via LEDs than those in the wrist. Specifically, arteries in the wrist are located at the base of the wrist (e.g., the palmar side of the wrist), meaning that only capillaries are accessible at the top of the wrist (e.g., the back of the wrist on the palmar side), where wearable watches and similar devices are typically worn. Accordingly, it has been found that using LEDs and other sensors within the ring 104 exhibits superior performance compared to wearable devices worn on the wrist, because the ring 104 has greater access to arteries (compared to capillaries), resulting in stronger signals and more valuable physiological data.

[0038] Electronic devices of system 100 (e.g., user equipment 106, wearable device 104) can be communicatively coupled to one or more servers 110 via wired or wireless communication protocols. For example, such as Figure 1 As shown, electronic devices (e.g., user device 106) can be communicatively coupled to one or more servers 110 via network 108. Network 108 can implement Transmission Control Protocol and Internet Protocol (TCP / IP) such as the Internet, or it can implement other network 108 protocols. The network connection between network 108 and the corresponding electronic device can facilitate data transmission via email, web, text messaging, mail, or any other suitable form of interaction within computer network 108. For example, in some implementations, a ring 104-a associated with a first user 102-a can be communicatively coupled to user device 106-a, wherein user device 106-a is communicatively coupled to server 110 via network 108. In additional or alternative cases, wearable device 104 (e.g., ring 104, watch 104) can be directly communicatively coupled to network 108.

[0039] System 100 can provide on-demand database services between user equipment 106 and one or more servers 110. In some cases, server 110 can receive data from user equipment 106 via network 108, and can store and analyze that data. Similarly, server 110 can provide data to user equipment 106 via network 108. In some cases, server 110 may be located in one or more data centers. Server 110 can be used for data storage, management, and processing. In some implementations, server 110 may provide a web-based interface to user equipment 106 via a web browser.

[0040] In some respects, system 100 can detect the duration of user 102's sleep and categorize the duration of user 102's sleep into one or more sleep stages (e.g., sleep stage classification). For example, as... Figure 1 As shown, user 102-a can be associated with wearable device 104-a (e.g., ring 104-a) and user device 106-a. In this example, ring 104-a can collect physiological data associated with user 102-a, including temperature, heart rate, HRV, respiratory rate, etc. In some aspects, the data collected by ring 104-a can be fed into a machine learning classifier, which is configured to determine the time period during which user 102-a is asleep (or previously asleep). Furthermore, the machine learning classifier can be configured to classify the time period into different sleep stages, including awake sleep, rapid eye movement (REM) sleep, light sleep (non-REM (NREM)), and deep sleep (NREM). In some aspects, the classified sleep stages can be displayed to user 102-a via the GUI of user device 106-a. The sleep stage classification can be used to provide user 102-a with feedback on the user's sleep patterns, such as recommended sleep times, recommended wake-up times, etc. Furthermore, in some implementations, the sleep stage classification technique described in this paper can be used to calculate scores for the corresponding user, such as sleep score, readiness score, etc.

[0041] In some respects, system 100 can leverage features derived from circadian rhythms to further improve physiological data collection, data processing procedures, and other techniques described herein. The term circadian rhythm can refer to the natural internal processes that regulate an individual's sleep-wake cycle, which repeats approximately every 24 hours. In this regard, the techniques described herein can utilize circadian rhythm regulation models to improve physiological data collection, analysis, and data processing. For example, a circadian rhythm regulation model, along with physiological data collected from user 102-a via wearable device 104-a, can be fed into a machine learning classifier. In this example, the circadian rhythm regulation model can be configured to "weight" or regulate physiological data collected throughout the user's natural, approximately 24-hour circadian rhythm. In some implementations, the system can initially start with a "baseline" circadian rhythm regulation model and can modify the baseline model using physiological data collected from each user 102 to generate a customized, personalized circadian rhythm regulation model specific to each respective user 102.

[0042] In some respects, System 100 can utilize other circadian rhythms to further improve the collection, analysis, and processing of physiological data through phases of these other rhythms. For example, if a weekly rhythm is detected within an individual's baseline data, the model can be configured to adjust the "weights" of the data according to the days within that week. Circadian rhythms that may require adjustment of the model in this manner include: 1) ultradian rhythms (faster than the day rhythm, including sleep cycles during sleep and oscillations in physiological variables measured during waking states ranging from less than an hour cycle to several hours cycle; 2) diurnal rhythms; 3) non-endogenous daily rhythms that are applied over diurnal rhythms, such as in a work schedule; 4) weekly rhythms, or other exogenously applied artificial time cycles (e.g., a 12-day rhythm can be used in a hypothetical culture with a "week" of 12 days); 5) multi-day ovarian rhythms in women and spermatogenesis rhythms in men; 6) lunar rhythms (associated with individuals living in low or no artificial light); and 7) seasonal rhythms.

[0043] Biorhythms are not always resting rhythms. For example, many women experience variability in ovarian cycle length between cycles, and even within a single user, it is not expected that superdial rhythms will occur at exactly the same time or cycle over several days. Thus, signal processing techniques sufficient to quantify frequency components while maintaining temporal resolution of these rhythms in physiological data can be used to improve the detection of these rhythms, assign phases of each rhythm to each moment measured, and thereby modify regulatory models and comparisons of time intervals. Biorhythm regulatory models and parameters can be added, in linear or nonlinear combinations as appropriate, to more accurately capture the dynamic physiological baseline of an individual or group of individuals.

[0044] In some aspects, system 200 can support the calculation of pressure-related parameters. For example, system 200 can support the calculation of a user's acute stress level, cumulative stress level, stress resilience score, or any combination thereof. Specifically, the technology described herein supports wearable device 104, such as as referenced. Figure 1 The wearable ring device 104 is described. For example, wearable device 104 may include an inner housing 205 configured to house a sensor module, which includes one or more sensors configured to acquire physiological data from user 102. One or more sensors of wearable device 104 may acquire physiological measurements from the user (e.g., a temperature sensor, an additional LED-PD sensor for measuring heart rate, oxygen saturation, or one or more sensors that the device can use to detect whether the user is asleep, active, etc.).

[0045] For example, one or more sensors of wearable device 104 may acquire physiological data from the user over one or more time intervals, wherein the physiological data may include heart rate data, motion data, or both. In some examples, the time intervals may occur during periods of sedentary and waking activity. Additionally or alternatively, the time intervals may include waking intervals for the user and sleeping intervals for the user. User device 106 may receive physiological data from wearable device 104 (e.g., including physiological data measured during one or more time intervals).

[0046] In some cases, one or more sensors of wearable device 104 are configured to acquire physiological data from the user based on arterial blood flow, body temperature, etc. In some implementations, one or more sensors of wearable device 104 are configured to acquire physiological data (e.g., including PPG data) from the user based on blood flow diffusing into the skin's microvascular bed, which has capillaries and arterioles. One or more sensors of wearable device 104 may be examples of photodetectors from PPG system 235, temperature sensor 240, motion sensor 245, current sensor, and other sensors.

[0047] While most of this disclosure describes one or more components in the context of a wearable ring device, aspects of this disclosure may be additionally or alternatively implemented in the context of other wearable devices. For example, in some implementations, one or more components described herein may be implemented in the context of other wearable devices, such as bracelets, watches, necklaces, piercing jewelry, etc. For example, wearable device 104 may wrap around a user's fingers, wrist, ankle, earlobe, etc.

[0048] For example, as previously noted herein, the wearable device 104 of system 200 can be worn by a user to collect data from the user, including temperature data, sleep data, recovery data, activity data, heart rate data, HRV data, respiratory rate data, blood pressure data, blood glucose data, etc. The wearable device 104 of system 200 can collect physiological data from the user based on temperature sensors and measurements extracted from arterial blood flow (e.g., using PPG signals). In some cases, the wearable device 104 can collect physiological data from the user based on measurements extracted from capillary blood flow, arteriolar blood flow, or both. Physiological data can be collected continuously.

[0049] In some implementations, one or more sensors of the wearable device 104 can continuously sample the user's temperature throughout the day and throughout the night. Sampling at a sufficient rate (e.g., one sample per minute) throughout the day and / or night can provide sufficient temperature data for the analysis described herein. In some implementations, the wearable device 104 can acquire temperature data continuously (e.g., at a sampling rate). In some examples, even with continuous temperature collection, the system 200 can utilize other information it has already collected or otherwise derived about the user (sleep stage, activity level, illness onset, stress, etc.) to select a representative temperature for a particular daytime period that accurately represents the underlying physiological phenomenon.

[0050] In some respects, physiological data (e.g., HRV data) collected via wearable device 104 can be used to evaluate various stress-related measures of user 102, such as (1) acute stress, (2) cumulative stress, and (3) stress resilience. As previously noted herein, for the purposes of this disclosure, the term “acute stress” can refer to a “real-time” indication of user stress when the user is awake and sedentary. In contrast, the term “cumulative stress” can refer to the total stress experienced by the user over an extended period of time, such as several weeks or months. Finally, the term “stress resilience” can refer to the user’s ability to cope with and recover from stress.

[0051] Those skilled in the art will understand that one or more aspects of this disclosure can be implemented in system 100 to additionally or alternatively address problems beyond those described above. Furthermore, various aspects of this disclosure can provide technical improvements to "conventional" systems or processes as described herein. However, the specification and drawings only include exemplary technical improvements derived from implementing aspects of this disclosure and therefore do not represent all technical improvements provided within the scope of the claims.

[0052] Figure 2Examples of a system 200 supporting techniques for measuring compressive strength using wearable-based data, according to various aspects of this disclosure, are shown. System 200 may implement or be implemented by system 100. Specifically, system 200 shows examples of a ring 104 (e.g., wearable device 104), a user device 106, and a server 110, as referenced. Figure 1 As described.

[0053] In some aspects, the ring 104 can be configured to be worn on a user's finger and, when worn on the user's finger, can determine one or more user physiological parameters. Examples of measurements and determinations may include, but are not limited to, user skin temperature, pulse waveform, respiratory rate, heart rate, HRV, blood oxygen level (SpO2), blood glucose level (e.g., glucose index), etc.

[0054] System 200 further includes user equipment 106 (e.g., a smartphone) that communicates with ring 104. For example, ring 104 may communicate wirelessly and / or via wired connection with user equipment 106. In some implementations, ring 104 may transmit measured and processed data (e.g., temperature data, photoplethysmography (PPG) data, motion / accelerometer data, ring input data, etc.) to user equipment 106. User equipment 106 may also send data to ring 104, such as ring 104 firmware / configuration updates. User equipment 106 may process the data. In some implementations, user equipment 106 may transmit data to server 110 for processing and / or storage.

[0055] Ring 104 may include a housing 205, which may include an inner housing 205-a and an outer housing 205-b. In some aspects, the housing 205 of ring 104 may store or otherwise include various components of the ring, including but not limited to device electronics, power sources (e.g., battery 210, and / or capacitors), one or more substrates (e.g., printable circuit boards) interconnecting the device electronics and / or power sources, etc. Device electronics may include device modules (e.g., hardware / software), such as: processing module 230-a, memory 215, communication module 220-a, power module 225, etc. Device electronics may also include one or more sensors. Example sensors may include one or more temperature sensors 240, PPG sensor assemblies (e.g., PPG system 235), and one or more motion sensors 245.

[0056] These sensors may include association modules (not shown) configured to communicate with corresponding components / modules of ring 104 and generate signals associated with the corresponding sensors. In some aspects, each of the components / modules of ring 104 may be communicatively coupled to each other via a wired or wireless connection. Furthermore, ring 104 may include additional and / or alternative sensors or other components configured to collect physiological data from the user, including light sensors (e.g., LEDs), pulse oximeters, etc.

[0057] Reference Figure 2 The ring 104 shown and described is provided for illustrative purposes only. Therefore, the ring 104 may include, for example... Figure 2 Additional or alternative components, such as those shown, can be manufactured. Other rings 104 can be manufactured to provide the functionality described herein. For example, rings 104 with fewer components (e.g., sensors) can be manufactured. In a particular example, a ring 104 can be manufactured having a single temperature sensor 240 (or other sensor), a power supply, and device electronics configured to read the single temperature sensor 240 (or other sensor). In another particular example, the temperature sensor 240 (or other sensor) can be attached to a user's finger (e.g., using adhesive, wrapping, clamp, spring-loaded clamp, etc.). In this case, the sensor can be wired to another computing device, such as a wrist-worn computing device that reads the temperature sensor 240 (or other sensor). In other examples, rings 104 can be manufactured to include additional sensors and processing capabilities.

[0058] Housing 205 may include one or more housing 205 assemblies. Housing 205 may include an outer housing 205-b assembly (e.g., a housing) and an inner housing 205-a assembly (e.g., a molded part). Housing 205 may be included in... Figure 2 Additional components not explicitly shown (e.g., additional layers). For example, in some implementations, ring 104 may include one or more insulating layers that electrically insulate device electronics and other conductive materials (e.g., electrical traces) from housing 205-b (e.g., metal housing 205-b). Housing 205 may provide structural support for device electronics, battery 210, one or more substrates, and other components. For example, housing 205 may protect device electronics, battery 210, and one or more substrates from mechanical forces such as pressure and shock. Housing 205 may also protect device electronics, battery 210, and one or more substrates from water and / or other chemicals.

[0059] The housing 205-b can be made of one or more materials. In some implementations, the housing 205-b may include a metal, such as titanium, which provides strength and abrasion resistance at a relatively light weight. The housing 205-b may also be made of other materials, such as polymers. In some implementations, the housing 205-b can be both protective and decorative.

[0060] The inner housing 205-a can be configured to engage with a user's finger. The inner housing 205-a can be formed of a polymer (e.g., a medical-grade polymer) or other materials. In some implementations, the inner housing 205-a can be transparent. For example, the inner housing 205-a can be transparent to light emitted by a PPG light-emitting diode (LED). In some implementations, the inner housing 205-a assembly can be molded onto the outer housing 205-b. For example, the inner housing 205-a can include a polymer molded (e.g., injection molded) to fit into the metal casing of the outer housing 205-b.

[0061] Ring 104 may include one or more substrates (not shown). Device electronics and battery 210 may be included on one or more substrates. For example, device electronics and battery 210 may be mounted on one or more substrates. Example substrates may include one or more printed circuit boards (PCBs), such as flexible PCBs (e.g., polyimide). In some implementations, electronics / battery 210 may include surface-mount devices (e.g., surface mount technology (SMT) devices) on a flexible PCB. In some implementations, one or more substrates (e.g., one or more flexible PCBs) may include electrical traces providing electrical communication between device electronics. The electrical traces may also connect battery 210 to device electronics.

[0062] Device electronics, battery 210, and substrate can be arranged in various ways within ring 104. In some implementations, a substrate including the device electronics may be mounted along the bottom (e.g., lower half) of ring 104, such that sensors (e.g., PPG system 235, temperature sensor 240, motion sensor 245, and other sensors) engage with the underside of a user's finger. In these implementations, battery 210 may be included along the top portion of ring 104 (e.g., on another substrate).

[0063] The various components / modules of ring 104 may include functions (e.g., circuits and other components) within ring 104. A module may include any discrete and / or integrated electronic circuit components that implement analog and / or digital circuits capable of producing the functions attributed to the modules herein. For example, a module may include analog circuitry (e.g., amplifier circuitry, filter circuitry, analog-to-digital converter circuitry, and / or other signal conditioning circuitry). A module may also include digital circuitry (e.g., combinational or sequential logic circuitry, memory circuitry, etc.).

[0064] The memory 215 (memory module) of ring 104 may include any volatile, non-volatile, magnetic, or electrical dielectric, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, or any other memory device. Memory 215 may store any data described herein. For example, memory 215 may be configured to store data collected by the corresponding sensors and PPG system 235 (e.g., motion data, temperature data, PPG data). Furthermore, memory 215 may include instructions that, when executed by one or more processing circuits, cause the module to perform various functions belonging to the modules herein. The device electronics of ring 104 described herein are merely example device electronics. Therefore, the type of electronic components used to implement the device electronics may vary based on design considerations.

[0065] The functionality of the modules belonging to ring 104 described herein can be embodied in one or more processors, hardware, firmware, software, or any combination thereof. Describing different features as modules is intended to highlight different functional aspects and does not necessarily imply that these modules must be implemented by separate hardware / software components. Rather, the functionality associated with one or more modules can be performed by separate hardware / software components or integrated within common hardware / software components.

[0066] The processing module 230-a of ring 104 may include one or more processors (e.g., processing units), microcontrollers, digital signal processors, system-on-a-chip (SoC), and / or other processing devices. The processing module 230-a communicates with modules contained within ring 104. For example, the processing module 230-a may send / receive data to / from modules and other components (such as sensors) of ring 104. As described herein, modules may be implemented from various circuit components. Therefore, modules may also be referred to as circuits (e.g., communication circuits and power supply circuits).

[0067] Processing module 230-a can communicate with memory 215. Memory 215 may include computer-readable instructions that, when executed by processing module 230-a, cause processing module 230-a to perform various functions belonging to processing module 230-a herein. In some implementations, processing module 230-a (e.g., a microcontroller) may include additional features associated with other modules, such as communication functions provided by communication module 220-a (e.g., an integrated Bluetooth Low Energy transceiver) and / or additional onboard memory 215.

[0068] Communication module 220-a may include circuitry providing wireless and / or wired communication with user equipment 106 (e.g., communication module 220-b of user equipment 106). In some implementations, communication modules 220-a and 220-b may include wireless communication circuitry, such as Bluetooth circuitry and / or Wi-Fi circuitry. In some implementations, communication modules 220-a and 220-b may include wired communication circuitry, such as Universal Serial Bus (USB) communication circuitry. Using communication module 220-a, ring 104 and user equipment 106 may be configured to communicate with each other. Ring processing module 230-a may be configured to transmit / receive data to / from user equipment 106 via communication module 220-a. Example data may include, but is not limited to, motion data, temperature data, pulse waveform, heart rate data, HRV data, PPG data, and status updates (e.g., charging status, battery charge level, and / or ring 104 configuration settings). The ring's processing module 230-a can also be configured to receive updates (e.g., software / firmware updates) and data from the user equipment 106.

[0069] Ring 104 may include a battery 210 (e.g., a rechargeable battery 210). Example battery 210 may include a lithium-ion or lithium-polymer type battery 210, but various battery options are possible. Battery 210 can be wirelessly charged. In some implementations, ring 104 may include a power source other than battery 210, such as a capacitor. The power source (e.g., battery 210 or capacitor) may have a curved geometry that matches the curves of ring 104. In some aspects, the charger or other power source may include additional sensors that can be used to collect data in addition to or supplement the data collected by ring 104 itself. Furthermore, the charger or other power source of ring 104 may act as user equipment 106, in which case the charger or other power source of ring 104 may be configured to receive data from ring 104, store and / or process data received from ring 104, and communicate data between ring 104 and server 110.

[0070] In some aspects, ring 104 includes a power module 225 that controls the charging of battery 210. For example, power module 225 may engage with an external wireless charger that charges battery 210 when engaged with ring 104. The charger may include a reference structure that mates with a reference structure of ring 104 to create a specified orientation of ring 104 during charging. Power module 225 may also regulate the voltage of device electronics, regulate the power output to device electronics, and monitor the state of charge of battery 210. In some implementations, battery 210 may include a protection circuit module (PCM) that protects battery 210 from high-current discharge, overvoltage during charging, and undervoltage during discharging. Power module 225 may also include electrostatic discharge (ESD) protection.

[0071] One or more temperature sensors 240 may be electrically coupled to processing module 230-a. Temperature sensors 240 may be configured to generate temperature signals (e.g., temperature data) indicating the temperature read or sensed by the temperature sensors 240. Processing module 230-a may determine the temperature at the location of the user at the temperature sensor 240. For example, in ring 104, the temperature data generated by the temperature sensor 240 may indicate the user's temperature at the user's finger (e.g., skin temperature). In some implementations, the temperature sensor 240 may contact the user's skin. In other implementations, a portion of housing 205 (e.g., inner housing 205-a) may form a barrier (e.g., a thin thermally conductive barrier) between the temperature sensor 240 and the user's skin. In some implementations, the portion of ring 104 configured to contact the user's finger may have a thermally conductive portion and a thermally insulating portion. The thermally conductive portion conducts heat from the user's finger to the temperature sensor 240. The thermally insulating portion insulates portions of ring 104 (e.g., temperature sensor 240) from ambient temperature.

[0072] In some implementations, temperature sensor 240 can generate a digital signal (e.g., temperature data), which processing module 230-a can use to determine the temperature. As another example, if temperature sensor 240 includes a passive sensor, processing module 230-a (or temperature sensor 240 module) can measure the current / voltage generated by temperature sensor 240 and determine the temperature based on the measured current / voltage. Example temperature sensor 240 may include a thermistor (such as a negative temperature coefficient (NTC) thermistor) or other types of sensors, including resistors, transistors, diodes, and / or other electrical / electronic components.

[0073] Processing module 230-a can sample the user's temperature over time. For example, processing module 230-a can sample the user's temperature based on a sampling rate. An example sampling rate might include one sample per second, but processing module 230-a can be configured to sample the temperature signal at other sampling rates, higher or lower than one sample per second. In some implementations, processing module 230-a can continuously sample the user's temperature throughout the day and night. Sampling at a sufficient rate (e.g., one sample per second) throughout the day can provide sufficient temperature data for the analysis described herein.

[0074] Processing module 230-a can store the sampled temperature data in memory 215. In some implementations, processing module 230-a can process the sampled temperature data. For example, processing module 230-a can determine the average temperature value over a time period. In one example, processing module 230-a can determine the average temperature value for a minute by summing all temperature values ​​collected per minute and dividing by the number of samples in that minute. In a specific example of sampling temperature at one sample per second, the average temperature could be the sum of all sampled temperatures for one minute divided by sixty seconds. Memory 215 can store the average temperature value over time. In some implementations, memory 215 can store the average temperature (e.g., one per minute) instead of the sampled temperatures to save memory 215.

[0075] The sampling rate, which can be stored in memory 215, can be configurable. In some implementations, the sampling rate can be the same throughout the day and night. In other implementations, the sampling rate can vary throughout the day / night. In some implementations, ring 104 can filter / reject temperature readings, such as large spikes in temperature that do not indicate physiological changes (e.g., temperature spikes from a hot shower). In some implementations, ring 104 can filter / reject temperature readings that may be unreliable due to other factors, such as excessive movement during exercise (e.g., as indicated by motion sensor 245).

[0076] Ring 104 (e.g., a communication module) can transmit sampled temperature data and / or average temperature data to user equipment 106 for storage and / or further processing. User equipment 106 can transmit sampled temperature data and / or average temperature data to server 110 for storage and / or further processing.

[0077] Although ring 104 is shown as including a single temperature sensor 240, ring 104 may include multiple temperature sensors 240 in one or more locations, such as arranged along the inner housing 205-a near the user's finger. In some implementations, the temperature sensor 240 may be a standalone temperature sensor 240. Additionally or alternatively, one or more temperature sensors 240 may be included with other components (e.g., packaged together with other components), such as with an accelerometer and / or a processor.

[0078] Processing module 230-a can acquire and process data from multiple temperature sensors 240 in a manner similar to that described with respect to a single temperature sensor 240. For example, processing module 230 can sample, average, and store temperature data from each of the multiple temperature sensors 240 separately. In other examples, processing module 230-a can sample the sensors at different rates and average / store different values ​​for different sensors. In some implementations, processing module 230-a can be configured to determine a single temperature based on the average of two or more temperatures determined by two or more temperature sensors 240 at different locations on the finger.

[0079] Temperature sensors 240 on ring 104 can acquire the distal temperature at a user's finger (e.g., any finger). For example, one or more temperature sensors 240 on ring 104 can acquire the user's temperature from the underside of the finger or different locations on the finger. In some implementations, ring 104 can continuously acquire distal temperatures (e.g., at a sampling rate). While distal temperatures measured by ring 104 at a finger are described herein, other devices can measure temperatures at the same / different locations. In some cases, the distal temperature measured at a user's finger may differ from the temperature measured at the user's wrist or other external body locations. Furthermore, the distal temperature measured at a user's finger (e.g., "shell" temperature) may differ from the user's core temperature. Thus, ring 104 can provide a useful temperature signal that may not have been acquired at other internal / external locations of the body. In some cases, continuous temperature measurements at the finger can capture temperature fluctuations (e.g., small or large fluctuations) that may not be apparent in the core temperature. For example, continuous temperature measurements at the fingertips can capture temperature fluctuations minute by minute or hour by hour, providing additional insights that other temperature measurements in other parts of the body may not offer.

[0080] Ring 104 may include a PPG system 235. The PPG system 235 may include one or more light emitters that emit light. The PPG system 235 may also include one or more light receivers that receive light emitted by the one or more light emitters. The light receivers may generate a signal indicating the amount of light received by the light receivers (hereinafter referred to as a "PPG" signal). The light emitters may illuminate an area of ​​the user's finger. The PPG signal generated by the PPG system 235 may indicate blood perfusion in the illuminated area. For example, the PPG signal may indicate changes in blood volume in the illuminated area caused by the user's pulse pressure. Processing module 230-a may sample the PPG signal and determine the user's pulse waveform based on the PPG signal. Processing module 230-a may determine various physiological parameters, such as the user's respiratory rate, heart rate, HRV, oxygen saturation, and other circulatory parameters, based on the user's pulse waveform.

[0081] In some implementations, the PPG system 235 can be configured as a reflective PPG system 235, wherein one or more light receivers receive transmitted light reflected from an area of ​​the user's finger. In some implementations, the PPG system 235 can be configured as a transmissive PPG system 235, wherein one or more light emitters and one or more light receivers are arranged opposite each other such that light is directly transmitted through a portion of the user's finger to one or more light receivers.

[0082] The number and ratio of transmitters and receivers included in the PPG system 235 can vary. Example light transmitters may include light-emitting diodes (LEDs). Light transmitters may emit light in the infrared spectrum and / or other spectra. Example light receivers may include, but are not limited to, photoelectric sensors, phototransistors, and photodiodes. Light receivers can be configured to generate PPG signals in response to wavelengths received from the light transmitter. The positions of the transmitters and receivers can be varied. Furthermore, a single device may include a reflective and / or transmissive PPG system 235.

[0083] In some implementations, Figure 2 The PPG system 235 shown may include a reflective PPG system 235. In these implementations, the PPG system 235 may include a centrally located optical receiver (e.g., at the bottom of ring 104) and two optical emitters located on each side of the optical receiver. In this implementation, the PPG system 235 (e.g., the optical receiver) may generate a PPG signal based on light received from one or both of these optical emitters. In other implementations, further placements, combinations, and / or configurations of one or more optical emitters and / or optical receivers are considered.

[0084] Processing module 230-a can control one or both of the optical emitters to emit light while sampling the PPG signal generated by the optical receiver. In some implementations, processing module 230-a can cause the optical emitter with a stronger received signal to emit light while sampling the PPG signal generated by the optical receiver. For example, when the PPG signal is sampled at a sampling rate (e.g., 250 Hz), the selected optical emitter can emit light continuously.

[0085] Sampling the PPG signal generated by the PPG system 235 can produce a pulse waveform, which may be referred to as "PPG". The pulse waveform can indicate the blood pressure pair (vs) time over multiple cardiac cycles. The pulse waveform may include peak values ​​indicating cardiac cycles. Furthermore, the pulse waveform may include respiratory-induced changes that can be used to determine respiratory rate. In some implementations, the processing module 230-a may store the pulse waveform in memory 215. The processing module 230-a may process the pulse waveform when it is generated and / or when it is retrieved from memory 215 to determine the user physiological parameters described herein.

[0086] Processing module 230-a can determine a user's heart rate based on a pulse waveform. For example, processing module 230-a can determine the heart rate (e.g., in heartbeats per minute) based on the time between peaks in the pulse waveform. The time between peaks may be referred to as the inter-beat interval (IBI). Processing module 230-a can store the determined heart rate value and IBI value in memory 215.

[0087] Processing module 230-a can determine the HRV over time. For example, processing module 230-a can determine the HRV based on changes in the IBI. Processing module 230-a can store the HRV value over time in memory 215. Furthermore, processing module 230-a can determine the user's respiratory rate over time. For example, processing module 230-a can determine the respiratory rate based on the user's IBI value over a time period using frequency modulation, amplitude modulation, or baseline modulation. The respiratory rate can be calculated as breaths per minute or as another respiratory rate (e.g., breaths every 30 seconds). Processing module 230-a can store the user's respiratory rate value over time in memory 215.

[0088] Ring 104 may include one or more motion sensors 245, such as one or more accelerometers (e.g., 6-D accelerometers) and / or one or more gyroscopes. Motion sensors 245 may generate motion signals indicating the motion of the sensors. For example, ring 104 may include one or more accelerometers that generate acceleration signals indicating the acceleration of the accelerometers. As another example, ring 104 may include one or more gyroscope sensors that generate gyroscope signals indicating angular motion (e.g., angular velocity) and / or orientation changes. Motion sensors 245 may be included in one or more sensor packages. An example accelerometer / gyroscope sensor is the Bosch BM1160 inertial microelectromechanical system (MEMS) sensor, which can measure angular rate and acceleration on three vertical axes.

[0089] Processing module 230-a can sample the motion signal at a sampling rate (e.g., 50 Hz) and determine the motion of ring 104 based on the sampled motion signal. For example, processing module 230-a can sample an acceleration signal to determine the acceleration of ring 104. As another example, processing module 230-a can sample a gyroscope signal to determine angular motion. In some implementations, processing module 230-a can store motion data in memory 215. The motion data may include sampled motion data and motion data calculated based on the sampled motion signal (e.g., acceleration and angle values).

[0090] Ring 104 can store various types of data described herein. For example, ring 104 can store temperature data, such as raw sampled temperature data and calculated temperature data (e.g., average temperature). As another example, ring 104 can store PPG signal data, such as pulse waveforms and data calculated based on pulse waveforms (e.g., heart rate values, IBI values, HRV values, and respiratory rate values). Ring 104 can also store motion data, such as sampled motion data indicating linear and angular motion.

[0091] Ring 104 or other computing devices can calculate and store additional values ​​based on the sampled / computed physiological data. For example, processing module 230 can calculate and store various metrics such as sleep metrics (e.g., sleep score), activity metrics, and readiness metrics. In some implementations, the additional value / metric may be referred to as a "derived value." Ring 104 or other computing / wearable devices can calculate various values / metrics related to movement. Example derived values ​​of movement data may include, but are not limited to, movement count values, regularity values, intensity values, metabolic equivalence (MET) of task values, and orientation values. Movement counts, regularity values, intensity values, and MET can indicate the amount of user movement over time (e.g., speed / acceleration). Orientation values ​​can indicate how ring 104 is oriented on the user's fingers and whether ring 104 is worn on the left or right hand.

[0092] In some implementations, motion counts and regularity values ​​can be determined by counting the number of acceleration peaks over one or more time periods (e.g., one or more time periods of 30 seconds to 1 minute). Intensity values ​​can indicate the number of motions and the associated intensity of the motions (e.g., acceleration values). Depending on the associated threshold acceleration value, intensity values ​​can be categorized as low, medium, and high. MET can be determined based on the intensity of motions during a time period (e.g., 30 seconds), the regularity / irregularity of the motions, and the number of motions associated with different intensities.

[0093] In some implementations, processing module 230-a can compress the data stored in memory 215. For example, processing module 230-a can delete sampled data after performing calculations based on the sampled data. As another example, processing module 230-a can average data over a longer time period to reduce the number of stored values. In a particular example, if the user's average temperature over one minute is stored in memory 215, processing module 230-a can calculate the average temperature over a five-minute time period for storage and then erase the one-minute average temperature data. Processing module 230-a can compress data based on various factors, such as the total amount of memory 215 used / available and / or the time elapsed since the last time loop 104 transmitted the data to user equipment 106.

[0094] While a user's physiological parameters can be measured by sensors included on ring 104, other devices can also measure these parameters. For example, while a user's temperature can be measured by temperature sensor 240 included in ring 104, other devices can also measure it. In some examples, other wearable devices (e.g., wrist devices) may include sensors for measuring a user's physiological parameters. Furthermore, medical devices such as external medical devices (e.g., wearable medical devices) and / or implantable medical devices can measure a user's physiological parameters. The techniques described herein can be implemented using one or more sensors on any type of computing device.

[0095] Physiological measurements can be acquired continuously throughout the day and / or night. In some implementations, physiological measurements can be acquired during various parts of the day and / or night. In some implementations, physiological measurements can be acquired in response to determining that the user is in a specific state (e.g., active state, resting state, and / or sleeping state). For example, ring 104 can perform physiological measurements during rest / sleep states to obtain cleaner physiological signals. In one example, ring 104 or other devices / systems can detect when the user is resting and / or sleeping and acquire physiological parameters (e.g., temperature) of the detected state. When the user is in other states, the devices / systems can use rest / sleep physiological data and / or other data to implement the techniques of this disclosure.

[0096] In some implementations, as described previously herein, ring 104 may be configured to collect, store, and / or process data, and may transfer any data described herein to user device 106 for storage and / or processing. In some aspects, user device 106 includes wearable application 250, operating system (OS), web browser application (e.g., web browser 280), one or more additional applications, and GUI 275. User device 106 may further include other modules and components, including sensors, audio devices, haptic feedback devices, etc. Wearable application 250 may include examples of applications (e.g., “apps”) that can be installed on user device 106. Wearable application 250 may be configured to acquire data from ring 104, store the acquired data, and process the acquired data as described herein. For example, wearable application 250 may include user interface (UI) module 255, acquisition module 260, processing module 230-b, communication module 220-b, and storage module (e.g., database 265) configured to store application data.

[0097] The various data processing operations described herein can be performed by ring 104, user equipment 106, server 110, or any combination thereof. For example, in some cases, data collected by ring 104 may be preprocessed and transmitted to user equipment 106. In this example, user equipment 106 may perform some data processing operations on the received data, transmit the data to server 110 for data processing, or both. For example, in some cases, user equipment 106 may perform processing operations requiring relatively low processing power and / or requiring relatively low latency, while user equipment 106 may transmit data to server 110 for processing operations requiring relatively high processing power and / or allowing relatively high latency.

[0098] In some aspects, the ring 104, user device 106, and server 110 of system 200 can be configured to assess a user's sleep patterns. Specifically, the corresponding components of system 200 can be used to collect data from the user via ring 104 and generate one or more scores (e.g., sleep score, readiness score) for the user based on the collected data. For example, as previously noted herein, the ring 104 of system 200 can be worn by the user to collect data from the user, including temperature, heart rate, HRV, etc. The data collected by ring 104 can be used to determine when the user fell asleep to assess the user's sleep for a given "sleep day." In some aspects, a score can be calculated for each corresponding sleep day, such that a first sleep day is associated with a first set of scores, and a second sleep day is associated with a second set of scores. The score for each corresponding sleep day can be calculated based on data collected by ring 104 during the corresponding sleep day. The scores can include, but are not limited to, sleep scores, readiness scores, etc.

[0099] In some cases, a "sleep day" can be aligned with a traditional calendar day, allowing a given sleep day to extend from midnight to midnight on the corresponding calendar day. In other cases, a sleep day can be offset relative to a calendar day. For example, a sleep day can extend from 6:00 PM (6:00 PM) on a calendar day to 6:00 PM (6:00 PM) on a subsequent calendar day. In this example, 6:00 PM can serve as a "deadline," where data collected from the user before 6:00 PM is counted for the current sleep day, and data collected from the user after 6:00 PM is counted for subsequent sleep days. Because most individuals sleep the most at night, offsetting the sleep day relative to the calendar day allows System 200 to assess the user's sleep patterns in a manner consistent with their sleep schedule. In some cases, users may be able to selectively adjust (e.g., via a GUI) the timing of their sleep day relative to the calendar day, aligning the sleep day with the duration of the corresponding user's typical sleep.

[0100] In some implementations, a user's total score for each corresponding day (e.g., sleep score, readiness score) can be determined / calculated based on one or more "contributors," "factors," or "contribution factors." For example, a user's total sleep score can be calculated based on a set of contributors, including: total sleep, efficiency, restfulness, REM sleep, deep sleep, wait time, timing, or any combination thereof. The sleep score can include any number of contributors. A "total sleep" contributor can refer to the sum of all sleep periods on a sleep day. A "efficiency" contributor can reflect the percentage of time spent asleep compared to the time spent waking up while sleeping, and can be calculated using the average efficiency of the long sleep periods (e.g., the main sleep period) of the sleep day, weighted by the duration of each sleep period. A "restfulness" contributor can indicate how restful a user's sleep is, and can be calculated using the average of all sleep periods of the sleep day, weighted by the duration of each period. Tranquility contributors can be based on “wake-up count” (e.g., the sum of all wake-ups detected during different sleep periods when the user wakes up), excessive movement, and “get-out count” (e.g., the sum of all get-outs detected during different sleep periods when the user gets out of bed).

[0101] A “REM sleep” contributor can refer to the sum of REM sleep durations across all sleep segments on a sleep day that includes REM sleep. Similarly, a “deep sleep” contributor can refer to the sum of deep sleep durations across all sleep segments on a sleep day that includes deep sleep. A “waiting time” contributor can represent how long it takes a user to fall asleep (e.g., average, median, longest) and can be calculated using the average of long sleep segments between sleep days, weighted by the duration of each segment and the number of such segments (e.g., combining one or more given sleep stages can be its own contributor or can be weighted by other contributors). Finally, a “timed” contributor can refer to the relative timed sleep segments within a sleep day and / or calendar day and can be calculated using the average of all sleep segments on a sleep day weighted by the duration of each segment.

[0102] As another example, a user's overall readiness score can be calculated based on a set of contributors, including: sleep, sleep balance, heart rate, HRV balance, recovery index, temperature, activity, activity balance, or any combination thereof. The readiness score can include any number of contributors. A "sleep" contributor can refer to the combined sleep score of all sleep segments within a sleep day. A "sleep balance" contributor can refer to the cumulative duration of all sleep segments within a sleep day. Specifically, sleep balance can indicate to a user whether the sleep a user has taken over a certain period (e.g., the past two weeks) is in line with the user's needs. Typically, adults need 7-9 hours of sleep per night to maintain health, alertness, and optimal mental and physical performance. However, occasional nights with poor sleep are common, so sleep balance contributors consider long-term sleep patterns to determine whether each user's sleep needs are being met. A "resting heart rate" contributor can indicate the lowest heart rate from the longest sleep segment of the sleep day (e.g., the main sleep segment) and / or the lowest heart rate from a nap that occurs after the main sleep segment.

[0103] Continuing to reference the "contributors" (e.g., factors, contributing factors) of the readiness score, the "HRV balance" contributor can indicate the highest average HRV from the main sleep period and naps that occur after the main sleep period. The HRV balance contributor helps users track their recovery status by comparing their HRV trend over a first time period (e.g., two weeks) with the average HRV over a second, longer time period (e.g., three months). The "recovery index" contributor can be calculated based on the longest sleep period. The recovery index measures how long it takes for a user's resting heart rate to stabilize during the night. A very good sign of recovery is that the user's resting heart rate stabilizes during the first half of the night (at least six hours before the user wakes up), leaving time for the body to recover the next day. If the user's highest temperature during a nap is at least 0.5°C higher than the highest temperature during the longest sleep period, the "body temperature" contributor can be calculated based on the longest sleep period (e.g., the main sleep period) or based on naps that occur after the longest sleep period. In some aspects, the ring can measure the user's body temperature while the user is asleep, and the system 200 can display the user's average temperature relative to the user's baseline temperature. If a user's body temperature is outside their normal range (e.g., clearly above or below 0.0), the body temperature contributor can be highlighted (e.g., put into "attention" status) or otherwise generate an alert for the user.

[0104] In some respects, physiological data (e.g., HRV data) collected via the wearable device 104 of system 200 can be used to evaluate various stress-related measures of user 102, such as (1) acute stress, (2) cumulative stress, and (3) stress resilience. As previously noted herein, for the purposes of this disclosure, the term “acute stress” can refer to a “real-time” indication of a user’s stress while awake and sedentary. In contrast, the term “cumulative stress” can refer to the total stress experienced by a user over an extended period of time, such as several weeks or months. Finally, the term “stress resilience” can refer to a user’s ability to cope with and recover from stress.

[0105] Figure 3 An example of a system 300 for evaluating stress-related metrics associated with a user, according to various aspects of this disclosure, is shown. System 300 may implement system 100, system 200, or both, or be implemented by system 100, system 200, or both. Specifically, system 300 illustrates examples of a ring 305 (e.g., wearable device 104), user equipment 310, and server 315, as referenced. Figure 1 As described. For example, system 300 can be configured to evaluate various stress-related metrics associated with a user, such as acute stress, cumulative stress, and / or stress resilience.

[0106] Ring 305 can acquire physiological data. Physiological data may include temperature data, heart rate data, respiratory rate data, HRV data, sleep data, blood oxygenation data, exercise data, and other forms of physiological data as described herein. Ring 305 can transmit physiological data indication 320 to user equipment 310, wherein physiological data indication 320 may include physiological data 325 and physiological data 330. For example, physiological data indication 320 may include HRV data over one or more time intervals. Physiological data indication 320 may include multiple transmissions of physiological data, such as the transmission of physiological data 325 and the transmission of physiological data 330. In some cases, multiple devices may acquire physiological data. For example, a first computing device (e.g., user equipment 310) and a second computing device (e.g., ring 305) may acquire physiological data.

[0107] For example, ring 305 can acquire user physiological data, such as user temperature data, respiratory rate data, heart rate data, HRV data, sleep data, SpO2 data (e.g., blood oxygen saturation), skin conductance, kinesiology, and / or other user physiological data. Ring 305 can acquire raw data and transform it into features with daily granularity. In some implementations, input data with different granularities can be used. Ring 305 can send the data to another computing device, such as a mobile device (e.g., user device 310), for further processing.

[0108] In some cases, wearable device 305 may measure physiological data 325 (e.g., HRV data and motion data) within a reference time interval 335. The reference time interval 335 may span multiple days, weeks, or any other time interval. In some cases, physiological data 325 may originate from periods of sedentary and waking activity determined by wearable device 305, user device 310, or both, based on data collected at one or more sensors on wearable device 305 (e.g., based on physiological data 325). In some other cases, physiological data 325 may originate from periods of sleep.

[0109] Wearable device 305 can send physiological data 325 to user device 310, and user device 310 can send historical data 375, including physiological data 325, to server 315. Server 315 can store historical data 375 as server data 380. In some cases, wearable device 305, user device 310, or both can determine a baseline HRV value 355 based on physiological data 325. In some cases, if physiological data 325 is measured when the user is sedentary and awake, the baseline HRV value 355 can be a baseline daytime HRV value. In some other cases, if physiological data 325 is measured when the user is awake and when the user is asleep, the baseline HRV value 355 can include a first baseline HRV value for the user during the user's awake period and a second baseline HRV value for the user during the user's asleep period.

[0110] In some examples, wearable device 305 can measure physiological data 330 (e.g., within time interval 340). Time interval 340 may occur after time interval 335. In some cases, time interval 340 may be relatively shorter than time interval 335. For example, physiological data 330 from time interval 340 may represent the user's current physiological state (e.g., measured in seconds, minutes, or hours), while physiological data 325 from time interval 335 may represent the user's historical physiological state. In some other cases, physiological data 330 from time interval 340 may span multiple days and multiple nights.

[0111] In some cases, a user may be both seated and awake throughout the time interval 340, which can be determined by wearable device 305, user device 310, or both based on data collected at one or more sensors on wearable device 305 (e.g., based on physiological data 330). In other cases, a user may be awake during one period of time interval 340 and asleep during other periods of time interval 340, which can also be determined by wearable device 305, user device 310, or both based on data collected at one or more sensors on wearable device 305 (e.g., based on physiological data 330). Wearable device 305 may transmit physiological data 330 to user device 310. User device 310 may determine one or more HRV values ​​for the user within time interval 340. For example, if the user is both seated and awake during time interval 340, the HRV values ​​may be daytime HRV values. In some other examples, user device 310 may determine a first set of HRV values ​​based on the periods of time interval 340 during which the user is awake, and a second set of HRV values ​​based on the periods of time interval 340 during which the user is asleep.

[0112] In some cases, user device 310 can access server data 380 to obtain a baseline HRV value 355, such as a baseline daytime HRV value, a first baseline HRV value for the user during the user's waking hours, a second baseline HRV value for the user during the user's sleeping hours, or any combination thereof. For example, user device 310 can receive physiological data 330 from wearable device 305, which can trigger user device 310 to access the baseline HRV value 355 based on physiological data 325 stored at server 315.

[0113] In some cases, user equipment 310 can compare the daytime HRV value from physiological data 330 with the baseline daytime HRV value from physiological data 325 to determine the user's acute stress level. The user's acute stress level can represent the relative amount of stress experienced by the user over the entire time interval 340. In other words, the user's acute stress level may represent a real-time or near-real-time "snapshot" of the user's stress level.

[0114] In some other cases, user 310 may compare a first set of HRV values ​​with a first baseline HRV value, compare a second set of HRV values ​​with a second baseline HRV value, or both, to determine the user's cumulative stress level over the entire time interval 340. The cumulative stress level may be based on a baseline stress level, which can be calculated from physiological data 325 within a reference time interval (e.g., time interval 335), based on user input received via user equipment 310, or both. The cumulative stress level may indicate the total amount of stress experienced by the user over the entire time interval 340, a trend in the user's stress level over the entire time interval 340, or both.

[0115] In some other cases, wearable device 305 may collect physiological data from the user over multiple time intervals, such as time intervals 335 and 340. Each time interval may span between the user's waking and sleeping periods, which wearable device 305, user device 310, or both may determine based on physiological data (e.g., heart rate data and / or exercise data). Wearable device 305 may (e.g., in physiological data indication 320) send physiological data from multiple time intervals to user device 310. User device 310 may determine stress and recovery indices within each waking interval and a sleep recovery index from each sleeping interval. User device 310 may calculate a stress resilience metric for the user. For example, user device 310 may obtain a weighted sum of the stress, recovery, and sleep recovery indices for each time interval. The weight of each index may be related to its recentity. For example, more recent indices may have a relatively larger weight in the stress resilience metric calculation. In some cases, the stress resilience metric may indicate the user's relative ability to cope with stress, recover from stress, or both.

[0116] Although the system can be implemented via ring 305 and user equipment 310, any combination of computing devices described herein can be implemented to achieve the characteristics attributed to system 300. In some cases, system 300 can smooth data (e.g., using a 7-day smoothing window, a 90-day smoothing window, or other windows). Missing values ​​can be imputed (e.g., using predictor imputation methods in Python packages).

[0117] User device 310 may include a wearable application 350 and an operating system 345. The wearable application 350 may run on the operating system 345 of user device 310 and be associated with ring 305. The wearable application 350 may include at least module 365 and application data 370. In some cases, application data 370 may include the user's historical physiological data patterns and other data. Physiological data patterns may include temperature data, heart rate data, respiratory rate data, HRV data, sleep data, blood oxygen saturation data, or combinations thereof.

[0118] In some cases, user device 310 may present a stress measurement 360, a baseline HRV value 355, or both to the user via wearable application 350. Wearable application 350 may include an application data processing module capable of performing data processing. For example, the application data processing module may include module 365 that provides functionality attributed to system 300. Example module 365 may include a baseline HRV module, a baseline stress module, a stress measurement module, etc.

[0119] The stress measurement module can calculate a user's stress measurement 360 based on physiological data 325, physiological data 330, or both. For example, the stress measurement module can calculate acute stress level, cumulative stress level, stress resilience measure, or any combination thereof. The baseline HRV module can calculate one or more baseline HRV values ​​355 based on physiological data (e.g., physiological data 325). The baseline stress module can determine the user's baseline stress level, for example, based on physiological data and input from the user for calculating cumulative stress level. In this case, system 300 can receive physiological data indication 320 including the user's physiological data from wearable device 305 and output one or more baseline HRV values ​​355 and the corresponding calculated stress measurement 360 (e.g., acute stress level, cumulative stress level, stress resilience measure, or any combination thereof). Wearable application 350 can store application data 370, such as the acquired physiological data.

[0120] In some cases, user-recorded symptoms (e.g., labels) combined with the user's physiological data can characterize a baseline HRV value 355, a stress measure 360, or both. In such cases, user-recorded input can help calculate the baseline HRV value 355, the stress measure 360, or both. Recorded user input can be an indication of a rest day, an indication of an activity goal, an indication of the start date of the menstrual cycle, or an example of one or more labels or a combination thereof.

[0121] System 300 can enable the user equipment 310's GUI to display the baseline HRV value 355, pressure measurement 360, or both, which will be relevant to... Figure 4Further detailed description is provided. System 300 can generate a message for display on the GUI of user device 310, indicating a baseline HRV value of 355, a stress metric of 360, or both. Calculating the baseline HRV value of 355, the stress metric of 360, or both can trigger a personalized message highlighting educational content associated with the baseline HRV value of 355, the stress metric of 360, or both to the user. In some cases, the message may include rest recommendations, recommendations to improve athletic performance, exercise recommendations, adjusted sleep goals, calorie consumption, minutes of activity, minutes of inactivity, number of hours of workout to be completed per week, or a combination thereof.

[0122] In some implementations, the wearable application 350 may notify the user of a baseline HRV value 355, a stress measurement 360, or both, and / or prompt the user to perform various tasks in the active GUI. Notifications and prompts may include text, graphics, and / or other user interface elements. The user device 310 may display notifications and prompts in a separate window on the home screen and / or overlay them on other screens (e.g., at the top of the home screen). In some cases, the user device 310 may display notifications and prompts on a mobile device, the user's watch device, or both.

[0123] In some implementations, user device 310 may store historical user data 375. Historical data 375 may include the user's historical temperature pattern, historical heart rate pattern, historical respiratory rate pattern, historical HRV pattern, historical sleep data, historical blood oxygen saturation, or combinations thereof. Historical data 375 may be selected from the most recent months. Historical data 375 may be used (e.g., by user device 310 or server 315) to calculate one or more baseline HRV values ​​355. Historical data 375 may be used by server 315. Using historical data 375 allows user device 310 and / or server 315 to personalize the GUI by taking the user's historical data 375 into account. In some cases, historical data 375 may be an example of physiological data 325.

[0124] User device 310 can transmit historical data 375 to server 315. In some cases, the transmitted historical data 375 may be the same as the historical data stored in wearable application 350. In other examples, the historical data 375 may be different from the historical data stored in wearable application 350. Server 315 can receive historical data 375. Server 315 may store historical data 375 in server data 380.

[0125] In some implementations, user device 310 and / or server 315 may also store other data that may be examples of user information. User information may include, but is not limited to, the user's age, weight, height, body mass index, gender, and medical history. In some implementations, user information may be used as an additional characteristic for calculating a baseline HRV value 355, baseline stress level, or both, along with physiological data 325. Server data 380 may include other data such as user information.

[0126] Figure 4 An example of a flowchart 400 for assessing a user's acute stress according to various aspects of this disclosure is shown. The aspects of flowchart 400 may be implemented by aspects of system 100, system 200, system 300, or any combination thereof, or by aspects of system 100, system 200, system 300, or any combination thereof. The various steps / functions illustrated in flowchart 400 may be implemented via wearable device 104, user device 106, one or more servers, or any combination thereof.

[0127] For the purposes of this disclosure, the terms "acute stress," "acute stress measurement," "acute stress level," and similar terms can refer to the tension and recovery experienced by a user while awake. In this respect, "acute stress" can refer to a "real-time" indication of a user's stress when the user is sedentary and awake. Thus, a user's "acute stress level" can include any measure or calculation designed to capture or describe how the user's stress evolves throughout the day, thereby ideally providing near real-time information about their stress level and stress-recovery balance.

[0128] In some aspects, the techniques described herein can determine a user's acute stress level based on HRV data collected via wearable device 104. According to some aspects of this disclosure, the techniques described herein can determine a user's acute stress level, rather than reporting the HRV value itself, which includes a measurement of the member's stress level throughout the day. In particular, movement and restrictions associated with the wearable device can make some HRV calculations unreliable throughout the day. However, by combining HRV with all other information provided by sensors and algorithms, aspects of this disclosure can estimate how a user's activity and recovery moments affect their acute stress level.

[0129] Now, referring to flowchart 400. At 405, the system can acquire daytime HRV (DHRV) data from the user throughout the day. Direct DHRV measurements can be taken simultaneously by wearable device 104 and daytime heart rate measurements. Thus, DHRV data can be acquired at regular or irregular intervals.

[0130] At 410, the system can determine whether the user is active and / or asleep. In particular, excessive movement may render the collected physiological data (e.g., collected heart rate and / or HRV data) unreliable. Thus, the system can determine whether the user is awake and sedentary (e.g., exercise below a certain threshold). That is, the system can be configured to determine the user's acute stress level only when the user is awake and relatively still (e.g., heart rate below a certain threshold, exercise below a certain threshold). If the user is awake and relatively still (e.g., step 410 = No), flowchart 400 can proceed to step 420.

[0131] At 420, the system can compare a user's DHRV data (e.g., HRV data collected when the user is awake and relatively sedentary) with the user's baseline HRV data 415 and / or with the expected DHRV variance 425. The baseline HRV data 415 can be specific to each individual user and (e.g., over a rolling time period, such as the previous 21 days) can be calculated based on previously obtained physiological data from the user. In this case, the personalized baseline HRV data 415 can vary daily (e.g., due to the rolling reference window in which the user's baseline HRV data 415 is calculated) but can remain constant within each individual day. For example, on January 22, the user's baseline HRV data 415 can be calculated by taking the median or average (or some other measure) of the user's DHRV data collected between January 1 and January 21.

[0132] For example, for a user on a given date, the user's baseline HRV data 415 (e.g., DHRV baseline) can be calculated from directly measured DHRV values ​​by discarding DHRV instances with missing hrv_accuracy or hrv_accuracy=0 (e.g., using only DHRV data 405 that meets one or more measurement quality criteria) and discarding DHRV instances that overlap with activity or any sleep (e.g., using only DHRV data collected during periods when the user is awake and relatively still). In this example, the system can calculate the median DHRV for the user each day (e.g., the previous 21 days) within the baseline window and calculate a weighted average of the daily median DHRV within the baseline window.

[0133] Additionally, the DHRV data 405 for a user during a given daytime period can be compared with the expected DHRV variance 425. The term "HRV variance" and similar terms can refer to the degree to which a user's DHRV is expected to change or fluctuate throughout the day. Specifically, some users exhibit relatively constant DHRV throughout the day (e.g., low expected variance), while others may exhibit greater DHRV fluctuation (e.g., high expected variance). It has been found that users with higher nighttime HRV tend to have higher DHRV variation (e.g., higher expected variance), while users with lower nighttime HRV tend to have lower DHRV variability (e.g., lower expected variance). This means that a strong DHRV deviation from the baseline (e.g., the difference between DHRV data 405 and baseline HRV data 415) indicative of a strong response in users with higher HRV under natural conditions may be negligible in users with higher HRV under natural conditions.

[0134] In some cases, a user's expected DHRV variance 425 can be determined by evaluating the user's own baseline DHRV data and / or comparing the user's physiological data with other users (e.g., comparing the user with other users within a reference group). For example, in some cases, the expected DHRV variance 425 can be calculated based on a group of users within a "reference group" where users share one or more physiological or demographic characteristics (e.g., same sex, same / similar age, or activity level, etc.). In some cases, a user's mean nocturnal HRV (e.g., nocturnal HRV over the past 3 months) can be used to determine which reference group the user belongs to. After identifying the user's relevant reference group, the system can calculate a predefined percentile range of DHRV for each user within the reference group (e.g., the 40th and 60th percentiles), using only DHRV values ​​with high accuracy confidence (hrv_accuracy > 80). Subsequently, the system can calculate the expected DHRV variance 425 for the reference group.

[0135] Continuing with flowchart 400, the system can compare the deviation between the user's DHRV data 405 and the baseline HRV data with a stress threshold 430, a recovery threshold 435, or both (e.g., In some cases, a stress threshold 430, a recovery threshold 435, or both can be calculated for a reference user group based on DHRV data collected from individual users. Subsequently, based on the comparison, at 440, the system can perform a stress / recovery detection. In other words, the system can determine whether an instantaneous time period (e.g., the user's current acute stress level at that moment) can be classified as a stress period, a recovery period, a neutral period, etc.

[0136] users The value can be described as the "strength" of the user's stress / recovery response. If If a stress threshold of 430 is met, then a user's instantaneous time period (e.g., current acute stress level) can be classified as stress (e.g., stress period). In contrast, if... If the recovery threshold of 435 is met, the user's instantaneous time period (e.g., current acute stress level) can be classified as recovery (e.g., recovery period). Otherwise, the instantaneous time period can be classified as a neutral period.

[0137] Furthermore, the system can compare the deviation between the user's DHRV data 405 and the baseline HRV data with the user's expected DHRV variance 425 (e.g., This allows for the classification of time periods into stress response (e.g., stress period), recovery response (e.g., recovery period), etc. For example, if Then, the user's instantaneous time period (e.g., current acute stress level) can be classified as a stress response (e.g., stress period). In contrast, if Then, the user's instantaneous time period (e.g., current acute stress level) can be classified as a recovery response (e.g., recovery period). As noted earlier in this document, the thresholds used to classify stress responses and / or recovery responses may exhibit the same absolute value but opposite signs (e.g., ).

[0138] In some cases, the difference between a user's DHRV data 405 and the user's baseline HRV data 415 can be scaled based on the user's DHRV data and / or the natural limits of the saturation point. In other words, in some cases, the "intensity" of the difference between the user's DHRV data 405 and the user's baseline HRV data 415 ( The saturation point is based on stress and recovery. The saturation point may be based on the theoretical maximum and minimum DHRV values ​​for users within a certain reference group and can be used to scale the intensity of the user (e.g., This is to ensure that outlier DHRV values ​​do not significantly distort the determined acute stress levels of users.

[0139] Maximum and minimum DHRV values ​​for stress and recovery can be estimated based on a reference user group, as described earlier in this document. Specifically, the saturation point for the reference user group can be calculated as the 5th and 95th percentiles of the DHRV data collected for that user group (taking the average of the users in each reference group). In practice, the saturation point can be used as the theoretical maximum and minimum DHRV values ​​that cannot be exceeded; otherwise, the saturation point values ​​are used. In other words, if a user's DHRV data exceeds the 95th percentile of that user's DHRV data, then the 95th percentile of the DHRV data can be used to calculate or otherwise represent the intensity (…). This is to avoid significantly distorting the evaluation of users' stress / recovery responses. Stress and recovery saturation points can be summarized based on nighttime HRV data collected for users within a given reference group (where the reference group can be determined based on users' mean / median nighttime HRV data).

[0140] Subsequently, at point 445, the system can determine the user's acute stress level during this time interval. Specifically, the system can determine the user's acute stress level based on comparing the user's DHRV data 405 with baseline HRV data 415 and / or expected DHRV variance 425. As previously noted herein, the acute stress level can be correlated with the relative amount of stress experienced by the user throughout the time interval (e.g., the relative stress / recovery experienced by the user at that point in time).

[0141] In some implementations, the user's acute stress level can be calculated by wearable device 104, user device 106, one or more servers, or any combination thereof. In some cases, the system can enable the user device 106's GUI to display an indication of the acute stress level, such as... Figure 5 As shown and described.

[0142] Figure 5 An example of a GUI 500 according to various aspects of this disclosure is shown. Aspects of the GUI 500 may implement aspects of system 100, system 200, system 300, flowchart 400, or any combination thereof, or may be implemented by aspects of system 100, system 200, system 300, flowchart 400, or any combination thereof.

[0143] In some examples, GUI 500 illustrates what can be achieved via GUI 500 (e.g., Figure 2The GUI 275 (illustrated in the diagram) displays application page 505 to the user. Specifically, application page 505 illustrates information associated with the user's acute stress level. For example, application page 505 may include a timeline 510 illustrating how the user's acute stress level changes throughout the day. As previously noted herein, the system can calculate the user's acute stress level at regular or irregular intervals (e.g., every 10 minutes) and can categorize each moment / interval as representing a stress period, a recovery period, a neutral period, or any combination thereof. For example, the timeline illustrates how the user's acute stress level changes throughout the day between various stress / relaxation states (ranging from stressed, alert, neutral, relaxed, and recovering). Additionally or alternatively, the individual calculations / estimates of acute stress levels can be aggregated (e.g., averaged) to summarize the user's acute stress level within an interval (e.g., the average acute stress level over a ten-minute interval).

[0144] In some cases, application page 505 may include chart 515, which illustrates stress and / or recovery periods identified within a certain time period (such as last week). In this example, stress periods and recovery periods may be represented using different colors or shading within chart 515.

[0145] In some aspects, application page 505 may include instructions for users to modify one or more of their behaviors to adjust (e.g., improve) their acute stress levels. In other words, the system can provide insights into how users can improve their acute stress levels. For example, the system may suggest that users take a nap, go for a walk, or cycle to improve their acute stress levels. In some cases, the recommendations / insights provided by the system may be based on prior data obtained from the user. For example, the system may recognize that the user's acute stress levels have decreased when the user went for a walk in the past, and therefore may suggest that the user go for a walk based on that previously identified relationship. In other words, the system may determine when the user went for a walk in the past based on tags entered by the user, activity recorded in a third-party application, or activity detection performed by wearable device 104 or another component of the system. In this case, the system may also recognize that the user's acute stress levels decreased when the user went for a walk in the past. By another example, when a user engages in guided meditation within wearable application 250 or another third-party application, the system may determine that the user's acute stress levels have decreased.

[0146] Figure 6An example of a flowchart 600 for determining HRV data for user interpolation according to various aspects of this disclosure is shown. Aspects of flowchart 600 may be implemented by aspects of system 100, system 200, system 300, flowchart 400, GUI 500, or any combination thereof, or by aspects of system 100, system 200, system 300, flowchart 400, GUI 500, or any combination thereof. The various steps / functions illustrated in flowchart 600 may be implemented via wearable device 104, user device 106, one or more servers, or any combination thereof.

[0147] As mentioned earlier in this article Figure 4 As illustrated in flowchart 400, the techniques described herein can utilize DHRV data that meets one or more measurement quality criteria to evaluate various stress-related measures associated with a user. In other words, the system can be configured to determine that the DHRV data has sufficient quality and / or accuracy before performing other measurements or analyses using the DHRV data. However, movement and other conditions may cause DHRV data to fail to meet the corresponding measurement quality criteria. In this case, such low-quality DHRV data may result in a “gap” in the physiological measurements and stress-related measures calculated for the user.

[0148] Therefore, some aspects of this disclosure relate to determining a user's "imputed" HRV value when the acquired HRV data does not meet relevant measurement quality criteria. HRV imputation can be performed when direct HRV measurement is unsuccessful (e.g., when HRV values ​​are missing, or when the accuracy of HRV measurement is less than a certain threshold). In other words, the HRV imputation techniques described herein can be used to estimate or predict HRV values ​​to "fill in the gaps in the user's HRV data," and thus can be used to evaluate a user's physiological parameters when HRV cannot be calculated with sufficient quality by conventional means. In some aspects, the imputed HRV value can be predicted based on other physiological parameters such as heart rate, skin temperature, and others. In this respect, the HRV imputation techniques described herein can be used to calculate / predict a user's HRV value, which can be used to evaluate various physiological parameters of the user even when the acquired HRV data is inaccurate or otherwise unreliable.

[0149] For example, flowchart 600 illustrates a DHRV imputation model that can be used to imput / predict any missing DHRV values ​​based on other available metrics. The output is the predicted HRV value (milliseconds). In some cases, the system can implement the process / algorithm illustrated in flowchart 600 when new daytime HR data is available to the user. However, as discussed further in detail herein, if (a) a sufficiently accurate DHRV already exists, or if (b) the model input features do not meet the conditions for prediction / imputation, then... Figure 6 The process / algorithm illustrated in the diagram may be interrupted.

[0150] At 605, the system can (e.g., using a wearable device) acquire heart rate data from the user. At 610, the system can determine HRV data associated with the user based on the acquired heart rate data, and can determine whether the accuracy of the HRV data meets one or more thresholds. In other words, the system can determine whether the HRV data meets one or more measurement quality criteria. If the HRV data meets one or more measurement quality criteria (e.g., step 610 = Yes), then flowchart 600 proceeds to step 615, and the system uses the collected heart rate data (and corresponding HRV data) to perform physiological analysis. For example, if the HRV data meets one or more measurement quality criteria (e.g., step 610 = Yes), the system can use the HRV data as DHRV data 405 used to evaluate the user's acute stress level, as referenced. Figure 4 As described.

[0151] Conversely, if the HRV data does not meet one or more measurement quality criteria (e.g., step 610 = No), flowchart 600 proceeds to step 620 to perform HRV imputation. At 620, the system collects or identifies other physiological characteristics associated with the user, such as skin temperature data / characteristics, activity data / characteristics, respiratory data / characteristics, SpO2 data / characteristics, MET data / characteristics, etc. The physiological characteristics / data collected at 620 may be correlated with information about the user's physiological background and may be aggregated within specific time intervals (e.g., the time interval in which HRV values ​​failed to meet measurement quality criteria), and / or over longer time periods resulting in the predicted / imputation instance.

[0152] At point 625, the system can normalize and / or scale physiological data / features. For example, the system can normalize acquired heart rate data, acquired skin temperature data, or both. In some cases, physiological data can be normalized and / or scaled based on the user's baseline physiological data, baseline physiological data associated with a group of users who share one or more common demographic characteristics, or both.

[0153] At point 630, the system can input the collected (and / or normalized / scaled) physiological data / features into an imputation model, which may include some machine learning model (e.g., regression model, random forest model) configured to output imputed HRV data 635 (e.g., predicted HRV values) based on the input physiological data / features. The imputed HRV data 635 can then be used to "fill the gaps in the user's HRV data" and evaluate other physiological measures associated with the user. For example, the imputed HRV data 635 can be used as input to flowchart 400 to determine the user's acute stress level.

[0154] In some cases, the system can train a machine learning model (e.g., an imputation model) to imput / predict HRV values ​​by inputting “good” HRV data and corresponding physiological data into the model (e.g., outputting imputed HRV data 635). For example, to train the model, the system can input baseline HRV data that meets the corresponding measurement quality criteria (e.g., “good” or “high-quality” HRV data). Further, the system can input other physiological parameters of the user collected at the same or similar time as the baseline HRV data. In this respect, the model can “learn” that the values ​​of the corresponding physiological parameters result in the corresponding HRV values ​​(e.g., the model can “learn” that when a user exhibits skin temperature of X and heart rate of Y, their HRV is likely to be Z).

[0155] Figure 7 An example of a flowchart 700 for evaluating a user's cumulative stress according to various aspects of this disclosure is shown. The aspects of flowchart 700 may be implemented by aspects of system 100, system 200, system 300, flowchart 400, GUI 500, flowchart 600, or any combination thereof, or by aspects of system 100, system 200, system 300, flowchart 400, GUI 500, flowchart 600, or any combination thereof. The various steps / functions illustrated in flowchart 700 may be implemented via wearable device 104, user device 106, one or more servers, or any combination thereof.

[0156] Compared to a user's "acute stress level," which is a snapshot of the user's stress at specific time points during waking and sedentary periods, cumulative stress combines daytime stress sensing (e.g., acute stress levels) with nighttime / sleep data to provide a long-term assessment of how much stress a user has experienced over days, weeks, or months. In some aspects, techniques used for cumulative stress monitoring can be used for long-term stress surveillance and for the early detection or prediction of burnout and / or chronic stress conditions.

[0157] In this regard, the cumulative stress measure described herein can be used as an "all-encompassing" stress-recovery balance indicator based on (1) daytime stress (e.g., acute stress levels), (2) stress signs in the user's sleep data, and (3) recovery signs when the user is awake and / or asleep. In this respect, cumulative stress is based on both the user's DHRV and nighttime HRV data, compared to acute stress based on DHRV data during the user's awake and sedentary periods. That is, nighttime measurements (e.g., HRV when the user is asleep) can reflect stress levels, which is useful for determining cumulative stress.

[0158] At point 705, the system can (e.g., using wearable device 104) acquire baseline physiological data associated with the user. Baseline physiological data may include baseline HRV data, baseline skin temperature data, baseline SpO2 data, baseline respiratory rate data, or any combination thereof.

[0159] At point 710, the system can determine the user's baseline stress level. In some aspects, the system can determine the user's baseline stress level based on baseline physiological data acquired at point 710. Additionally or alternatively, the system can determine the user's baseline stress level based on inputs or responses received from the user. For example, the baseline stress level can be determined based on the user's answers to standardized survey questions (e.g., questionnaires) and information about demographic or stress-related characteristics associated with the user (e.g., previous diagnoses of anxiety or depression, results of stress tests, etc.).

[0160] At point 715, the system can determine a user's first baseline HRV value (e.g., daytime baseline HRV value) during the user's awake periods and a second baseline HRV value (e.g., nighttime baseline HRV value) during the user's sleeping periods. As noted earlier in this paper, a user's HRV changes throughout the day based on the user's circadian rhythm and therefore varies depending on whether the user is awake or asleep. Thus, by determining separate baseline HRV values, the techniques described herein can utilize daytime HRV values ​​(e.g., HRV data collected when the user is awake) and nighttime HRV values ​​(e.g., HRV data collected when the user is asleep) to assess the user's cumulative stress.

[0161] In contrast to acute stress using a “rolling” or “adaptive” baseline / reference window (e.g., the previous 21 days) to determine a user’s baseline HRV data 415, daytime and nighttime HRV values ​​can be determined over a longer time window (e.g., the previous 4 months) and are therefore less prone to change. In other words, the techniques described herein used to determine a user’s cumulative stress levels can utilize a longer data history to observe long-term trends in a user’s stress levels over periods of weeks, months, or years.

[0162] At point 720, the system can (e.g., using wearable device 104) acquire additional physiological data from the user. This additional physiological data may include HRV data, skin temperature data, SpO2 data, respiratory rate data, or any combination thereof. Furthermore, the additional physiological data can be collected during periods when the user is awake and during periods when the user is asleep. In this regard, the additional physiological data may include daytime HRV data / values ​​based on body data measurements (and / or interpolation) acquired while the user is awake and nighttime HRV data / values ​​based on body data measurements (and / or interpolation) acquired while the user is asleep. For example, the system can acquire additional physiological data by continuously collecting data from the user over periods of days, weeks, or months using a wearable device.

[0163] At point 725, the system can compare the daytime HRV data acquired / impregnated from the user with the daytime baseline HRV value. Similarly, at point 730, the system can compare the nighttime HRV data acquired / impregnated from the user with the nighttime baseline HRV value.

[0164] At 730, the system can determine the user's cumulative stress level over the entire time interval based on comparisons of daytime and nighttime HRV values / data performed at steps 725 and 730, respectively. For example, at 725, the system can determine one or more stress scores associated with the user throughout the entire time interval when the user is awake, based on a first comparison of daytime HRV data with the daytime baseline HRV value. Similarly, the system can determine one or more recovery scores associated with the user throughout the entire time interval when the user is asleep, based on a second comparison of nighttime HRV data at 730 with the nighttime baseline HRV value, where the cumulative stress level is based on one or more stress scores and one or more recovery scores.

[0165] In some aspects, the cumulative stress level can be based on the baseline stress level associated with the user, determined at 710. As described earlier herein, the cumulative stress level can be associated with the total amount of stress experienced by the user over the entire time interval, a trend in the user's stress level over the entire time interval, or both. For example, steps 720 to 735 can be performed on a daily basis to evaluate the user's stress level compared to the user's baseline stress level, wherein the cumulative stress level can be associated with a trend (e.g., increase or decrease) in the amount of stress experienced by the user over time.

[0166] In this regard, comparing cumulative stress levels with a user's baseline stress levels can be used to predict a user's burnout and / or chronic stress status. In other words, because cumulative stress levels are associated with the total amount of stress a user experiences over a time interval, cumulative stress levels can be compared with one or more thresholds (e.g., burnout threshold, chronic stress threshold) to assess the probability of a user experiencing burnout and / or the probability of a user being diagnosed with chronic stress. For the purposes of this disclosure, the term "chronic stress" can be used to refer to a physiological condition in which a user experiences relatively high levels of stress over an extended period of time, while the term "burnout" can refer to a physiological condition in which a user's stress levels impair their ability to function normally. In this regard, "burnout" may be caused by chronic stress or be a condition / symptom of chronic stress.

[0167] In some aspects, information related to a user's cumulative stress level can be displayed via the GUI of the user device 106, such as... Figure 5 As shown and described. For example, the user device may display how a user's cumulative stress level changes compared to their baseline stress level over weeks, months, or years. In some aspects, the user device 106 may display instructions for the user to modify one or more of their behaviors to adjust (e.g., improve) their cumulative stress level. In other words, the system can provide insights into how users can improve their cumulative stress levels to prevent burnout or chronic stress.

[0168] Figure 8 An example of a flowchart 800 for evaluating a user's compressive resilience according to various aspects of this disclosure is shown. The aspects of flowchart 800 may be implemented by aspects of system 100, system 200, system 300, flowchart 400, GUI 500, flowchart 600, flowchart 700, or any combination thereof, or by aspects of system 100, system 200, system 300, flowchart 400, GUI 500, flowchart 600, flowchart 700, or any combination thereof. The various steps / functions illustrated in flowchart 800 may be implemented via wearable device 104, user device 106, one or more servers, or any combination thereof.

[0169] As described earlier in this article, the term "stress resilience" can refer to a user's ability to cope with and recover from stress. Stress resilience acts as a defense mechanism protecting our ability to remain ready. If a user exhibits strong stress resilience, they can withstand stress without affecting their readiness score and / or other physiological characteristics. By enhancing their ability to cope with stress, users are able to handle larger amounts of stress and / or recover from it more effectively and efficiently.

[0170] The concept of resilience is based on maintaining a balance between the amount of stress a person experiences and their ability to recover from stress during the day and night. This balance is crucial to ensuring that stress does not have long-term negative impacts on a person's well-being. A person's recovery strength is a key factor in determining their resilience. A robust recovery system enables a person to recover quickly from stressful situations and return to a state of equilibrium.

[0171] In some aspects, stress resilience may be based on two main pillars: (1) how much stress the user experiences during the daytime stress-recovery balance? and (2) how effective is the recovery from sleep recovery? According to some aspects of this disclosure, the user wears a wearable device daily, and data points can be generated based on these two pillars in a two-dimensional coordinate plane. The x-axis may refer to the daytime stress-recovery balance (e.g., the first pillar), while the y-axis may refer to sleep recovery (the second pillar). Over time, the point cloud in these two-dimensional planes can be observed to evaluate the user's stress resilience metrics (e.g., stress resilience score, stress resilience level).

[0172] Now, refer to the process Figure 8 The flowchart in Figure 800 is shown in the diagram. At 805, the system can collect physiological data from the user over a period of time that includes multiple "sleep days," where each sleep day includes the user's awake intervals and the user's sleep intervals. As described herein, the physiological data can be measured from the user via wearable device 104 and can include HRV data, respiratory rate data, temperature data, activity data, SpO2 data, etc.

[0173] At 810, the system can determine three indices for the user during each sleep day: (1) a daytime stress index, (2) a daytime recovery index, and (3) a nighttime recovery index. The first two indices (e.g., the daytime stress index and the daytime recovery index) are associated with the first pillar of the daytime stress-recovery balance, while the third index is associated with the second pillar of sleep recovery, as described herein. In some respects, the daily index calculation performed at 810 can be triggered each time physiological data acquired from wearable device 104 is synchronized with user device 106 (e.g., each time the user opens wearable application 250 in the morning to check their sleep score and recovery score). That is, the daily index calculation can be triggered after each synchronization measuring long sleep (e.g., when the user sleeps for longer than a certain threshold amount of time between sleep intervals).

[0174] In some implementations, the system can determine whether all three indices for a given sleep day have been calculated. In other words, the system can check whether the daytime stress and recovery indices are available, as well as the nighttime recovery indices for the subsequent long sleep period. In other words, in some implementations, the daytime stress / recovery index for a given awake interval on a sleep day must be paired with the nighttime recovery indices for the immediately following sleep interval on the sleep day. That is, the system can ensure that data collected within a sleep day is not interchangeably paired with data from different sleep days.

[0175] In some respects, the corresponding indices can be determined by comparing the acquired physiological data with the user's own baseline physiological data, as described earlier in this document. For example, the daytime stress / recovery index can be calculated by comparing the user's DHRV data (e.g., the user's HRV data when awake) with the user's daytime baseline HRV value, and the nighttime recovery index can be calculated by comparing the user's nighttime HRV data (e.g., the user's HRV data when asleep) with the user's nighttime baseline HRV value, as described earlier in this document.

[0176] Furthermore, acute stress metrics calculated within each sleep day can be used to determine the daytime stress index and daytime recovery index for a given sleep day. For example, the daytime stress index and daytime recovery index (e.g., indices affecting the first pillar of the daytime stress-recovery balance) can be determined by examining the number of stress periods / recovery periods during the day (e.g., regarding...). Figure 4 As described), examine the intensity of the stress / recovery period (e.g., intensity / ,in It can be determined by either (or both).

[0177] For example, to aggregate a user's DHRV data into a daytime stress index and a daytime recovery index, the system can analyze the user's DHRV data across multiple "stress assessment instances" (e.g., acute stress assessment instances) throughout the sleep day, and quantify each stress assessment instance into different stress / recovery levels (e.g., high stress, moderate stress, low stress, neutral, low recovery, moderate recovery, high recovery). This is similar to the description of acute stress mentioned earlier in this article. Figure 4 As described, each instance can be classified into a level within a corresponding level based on how the user's DHRV data during the corresponding instance period compares to the user's baseline DHRV data. Subsequently, after quantifying the "stress assessment instances" into corresponding levels, the system can calculate a weighted sum of "stress assessment instances" across corresponding levels. For example, high stress / recovery instances can be associated with weight 4, medium stress / recovery instances with weight 3, low stress / recovery instances with weight 2, and neutral instances with weight 1.

[0178] Using these weights, the daytime stress index for the corresponding sleep day can be calculated as follows: Daytime Stress Index = 100 * (Weighted sum of stress instances) / (Weighted sum of stress instances + Weighted sum of recovery instances + Weighted sum of neutral instances). Similarly, the daytime recovery index can be calculated as follows: Daytime Recovery Index = 100 * (Weighted sum of recovery instances) / (Weighted sum of stress instances + Weighted sum of recovery instances + Weighted sum of neutral instances).

[0179] Continuing with the same example, the system can aggregate the number of instances where a user exhibits different levels of stress or recovery during a given sleep day (e.g., how many "neutral" instances show neither stress nor recovery, how many show low recovery, how many show low stress or recovery, how many show moderate stress or recovery, how many show high stress or recovery, and so on). These observed instances can be assigned corresponding weights and aggregated to obtain a total weighted sum of stress / recovery instances. This weighted sum of stress / recovery instances can then be used to calculate the user's daytime stress index.

[0180] Furthermore, in some cases, the sleep recovery index for a given sleep day can be determined based on the weighted average of the duration of the sleep intervals on the sleep day (e.g., the duration of the longest sleep interval during the sleep day), the sleep quality of the user during the sleep intervals on the corresponding sleep day (e.g., sleep score), the user's resting heart rate during the entire sleep intervals on the corresponding sleep day, and the HRV variance of the HRV data collected during the sleep intervals on the corresponding sleep day, or any combination thereof.

[0181] At point 815, if the system determines that any of the three indices are missing for a given sleep day (e.g., due to lack of data, or because the user is not wearing the wearable device 104), the system may discard or otherwise ignore data from that sleep day for the purpose of calculating the user's stress resilience. For example, a user might wear the wearable ring device throughout the day (e.g., throughout the entire waking interval of the sleep day) to allow the calculation of the daytime stress index and the daytime recovery index. However, the user might forget to charge the wearable ring device, preventing it from acquiring physiological data during the user's sleep that night (e.g., no data during the sleep interval of the sleep day). In this example, the system could determine that the daytime stress index and the daytime recovery index do not have a corresponding nighttime recovery index within the sleep day, and therefore, the daytime stress / recovery index within the sleep day can be avoided when calculating the user's stress resilience.

[0182] At point 825, the system can determine the weights of the corresponding indices calculated for each sleep day throughout the entire time interval. That is, for each sleep day for which the system can calculate all three indices (e.g., daytime stress index, daytime recovery index, nighttime recovery index), the system can determine the weights of the indices for that sleep day. In some respects, the weights of the individual indices can be based on their recentity. For example, the daytime stress index, daytime recovery index, and nighttime recovery index determined for the first sleep day can be given greater weight (e.g., having a greater impact on the user's stress resilience) compared to the same indices calculated for a more distant second sleep day. In other words, when determining a user's stress resilience, the most recently calculated indices may be given greater weight.

[0183] At point 825, the system can calculate a user's stress resilience metric (e.g., stress resilience score, stress resilience level) based on the indices calculated for each sleep day at point 810 and the weights determined at point 820. As described earlier in this document, the stress resilience metric can indicate a user's ability to cope with stress, recover from stress, or a relative ability of both, or be associated with a user's ability to cope with stress, recover from stress, or a relative ability of both. In some cases, if there is a sufficient number of sleep days (e.g., 5 sleep days, 14 sleep days, etc.) for which the system has already been able to calculate all three indices shown and described at point 810, the system can calculate only the user's stress resilience metric.

[0184] In some cases, the system can use a stress resilience level classification to calculate the stress resilience metric for each sleep day based on a comparison of the stress index, recovery index, and sleep recovery index corresponding to the respective sleep day. In other words, as described earlier in this document, the system can classify a day as a stress sleep day (e.g., a stress interval), a recovery sleep day (e.g., a recovery interval), a neutral sleep day (e.g., a neutral interval), or any combination thereof, where the aggregation of stress resilience levels across multiple sleep days is used to determine the stress resilience metric.

[0185] In some aspects, information related to the user's compressive strength measurement can be displayed via the GUI of the user equipment 106, as shown in the reference. Figure 9Further illustrated and described. For example, user device 106 may display how a user's stress resilience metric changes over time. For instance, user device 106 may display the stress resilience metric for each sleep day (e.g., seven different stress resilience metrics across seven different sleep days). In some aspects, user device 106 may display instructions for the user to modify one or more of their behaviors to adjust (e.g., improve) their stress resilience metric. In other words, the system can provide insights into how users can improve their stress resilience metric to better cope with and / or recover from stress.

[0186] Figure 9 An example of a GUI 900 according to aspects of this disclosure is shown. Aspects of the GUI 900 may implement aspects of system 100, system 200, system 300, flowchart 400, GUI 500, flowchart 600, flowchart 700, flowchart 800, or any combination thereof, or may be implemented by aspects of system 100, system 200, system 300, flowchart 400, GUI 500, flowchart 600, flowchart 700, flowchart 800, or any combination thereof.

[0187] In some examples, GUI 900 is illustrated as being accessible via GUI 900 (e.g., Figure 2 The GUI 275 shown in the diagram displays application page 905 to the user. Specifically, application page 905 illustrates information associated with the user's stress resilience metrics. For example, application page 905 may include dashboard 910, which illustrates the user's stress resilience metrics / scores (e.g., indications of how well the user copes with stress and / or recovers from it). In some cases, dashboard 910 may illustrate the most recent stress resilience metric calculated for the user (e.g., a calculated stress resilience metric based on the previous sleep day). In some cases, such as... Figure 9 As shown, dashboard 910 can indicate the values ​​of three corresponding indices (e.g., daytime stress index, daytime recovery index, and sleep recovery index) used to calculate the measure of compressive resilience.

[0188] In some respects, the system can be configured to take into account external conditions or factors (and / or the user's mental / emotional state) when evaluating a user's acute stress, cumulative stress, and / or resilience. Such external conditions or factors related to the user's mental / emotional state can be determined based on tags entered by the user, information received from other devices or third-party applications, or any combination thereof. For example, in some cases, the system can determine that the user is experiencing anxiety, is taking medication, has experienced a traumatic event, or has experienced the loss of a family member or friend. Such conditions or factors related to the user's mental / emotional state allow the system to gain a more comprehensive understanding of the user's mental / emotional state, thereby enabling it to be used more efficiently to determine the user's acute / cumulative stress levels and / or the degree to which the user can withstand stress.

[0189] Figure 10 A block diagram 1000 of a device 1005 supporting a technique for measuring compressive strength using wearable-based data, according to various aspects of this disclosure, is shown. Device 1005 may include an input module 1010, an output module 1015, and a wearable application 1020. Device 1005, or one of its multiple components (e.g., input module 1010, output module 1015, and wearable application 1020), may include at least one processor that may be coupled to at least one memory to support the described technique. Each of these components may communicate with each other (e.g., via one or more buses).

[0190] Input module 1010 may provide components for receiving information (such as data packets, user data, control information, or any combination thereof) associated with various information channels (e.g., control channels, data channels, information channels related to disease detection technologies). The information may be transmitted to other components of device 1005. Input module 1010 may utilize a single antenna or a group of multiple antennas.

[0191] Output module 1015 may provide components for transmitting signals generated by other components of device 1005. For example, output module 1015 may transmit information associated with various information channels (e.g., control channels, data channels, information channels related to disease detection technologies), such as data packets, user data, control information, or any combination thereof. In some examples, output module 1015 may coexist with input module 1010 in a transceiver module. Output module 1015 may utilize a single antenna or a group of multiple antennas.

[0192] For example, wearable application 1020 may include data acquisition component 1025, pressure-recovery index component 1030, compressive strength measurement component 1035, user interface component 1040, or any combination thereof. In some examples, wearable application 1020 or its various components may be configured to use input module 1010, output module 1015, or both, or otherwise cooperate with input module 1010, output module 1015, or both to perform various operations (e.g., receiving, monitoring, transmitting). For example, wearable application 1020 may receive information from input module 1010, send information to output module 1015, or integrate with input module 1010, output module 1015, or both to receive information, transmit information, or perform various other operations as described herein.

[0193] Data acquisition component 1025 may be configured or otherwise supported for acquiring physiological data from a user via a wearable device over multiple time intervals, wherein each time interval includes an awake interval and a sleep interval, the physiological data including at least HRV data. Stress-Recovery Index component 1030 may be configured or otherwise supported for determining, for multiple time intervals and at least in part based on HRV data, a stress index and a recovery index associated with an awake interval of the corresponding time interval, and a sleep recovery index associated with a sleep interval of the corresponding time interval. Stress Resilience Measurement component 1035 may be configured or otherwise supported for determining a user's stress resilience measurement based at least in part on a weighted sum of the stress index, recovery index, and sleep recovery index over multiple time intervals, wherein the weighted sum is associated with the recentity of the respective stress index, recovery index, and sleep recovery index, and wherein the stress resilience measurement indicates the user's relative ability to cope with stress, recover from stress, or both. User Interface component 1040 may be configured or otherwise supported for displaying a visual representation of the stress resilience measurement to the user via a GUI of the user device.

[0194] Figure 11A block diagram 1100 is shown of a wearable application 1120 supporting techniques for measuring compressive strength using wearable-based data, according to various aspects of this disclosure. Wearable application 1120 may be an example of a wearable application or wearable application 1020, or aspects of both, as described herein. Wearable application 1120 or its various components may be examples of structures for performing various aspects of techniques for measuring compressive strength using wearable-based data as described herein. For example, wearable application 1120 may include a data acquisition component 1125, a pressure-recovery index component 1130, a compressive strength measurement component 1135, a user interface component 1140, or any combination thereof. Components of each of these components or its sub-components (e.g., one or more processors, one or more memories) may communicate directly or indirectly (e.g., via one or more buses).

[0195] Data acquisition component 1125 may be configured or otherwise supported for acquiring physiological data from a user via a wearable device over multiple time intervals, wherein each time interval includes an awake interval and a sleep interval, the physiological data including at least HRV data. Stress-Recovery Index component 1130 may be configured or otherwise supported for determining, for multiple time intervals and at least in part based on HRV data, a stress index and a recovery index associated with an awake interval of the corresponding time interval, and a sleep recovery index associated with a sleep interval of the corresponding time interval. Stress Resilience Measurement component 1135 may be configured or otherwise supported for determining a user's stress resilience measurement based at least in part on a weighted sum of the stress index, recovery index, and sleep recovery index over multiple time intervals, wherein the weighted sum is associated with the recentity of the respective stress index, recovery index, and sleep recovery index, and wherein the stress resilience measurement indicates the user's relative ability to cope with stress, recover from stress, or both. User Interface component 1140 may be configured or otherwise supported for displaying a visual representation of the stress resilience measurement to the user via a GUI of the user device.

[0196] In some examples, the multiple time intervals include a second time interval comprising a first waking interval and a first sleeping interval following the first waking interval, and the data acquisition component 1125 may be configured or otherwise supported to include means for determining the absence of physiological data collected during the first waking interval or the first sleeping interval. In some examples, the multiple time intervals include a second time interval comprising a first waking interval and a first sleeping interval following the first waking interval, and the stress resilience measurement component 1135 may be configured or otherwise supported to avoid determining a stress index, recovery index, and sleep recovery index corresponding to the first time interval based at least in part on the absence of physiological data collected during the first waking interval or the first sleeping interval.

[0197] In some examples, the stress index and recovery index associated with the corresponding waking interval are at least partially based on a comparison of a first portion of HRV data collected during the waking interval with baseline daytime HRV data associated with the user during the user's waking hours. In some examples, the sleep recovery index associated with the corresponding sleeping interval is at least partially based on a comparison of a second portion of HRV data collected during the sleeping interval with baseline nighttime HRV data associated with the user during the user's sleeping hours.

[0198] In some examples, the sleep recovery index for a given time interval is determined at least in part based on the weighted average of the duration of the sleep interval for that time interval, the user’s sleep quality during the sleep interval for that time interval, the user’s resting heart rate during the entire sleep interval for that time interval, and the HRV variance of the HRV data collected during the sleep interval for that time interval.

[0199] In some examples, the data acquisition component 1125 may be configured or otherwise supported to classify each of a plurality of time intervals using stress resilience levels based at least in part on a comparison of stress index, recovery index, and sleep recovery index corresponding to the respective time intervals, wherein a visual representation of the stress resilience metric is displayed based at least in part on the classification.

[0200] In some examples, the user interface component 1140 may be configured or otherwise support a component for displaying, at least in part, multiple compressive toughness measures corresponding to multiple time intervals to a user via a GUI of a user device based on a classification.

[0201] In some examples, user interface component 1140 may be configured or otherwise supported to display to a user, via a user device's GUI, indications of stress index, recovery index, and sleep recovery index corresponding to time intervals among a plurality of time intervals.

[0202] In some examples, the user interface component 1140 may be configured or otherwise support components for displaying feedback to the user via a GUI of a user device and at least in part based on a determined compressive toughness metric, the feedback including instructions for maintaining one or more first actions of the user, modifying one or more second actions of the user, or both, wherein the instructions are configured to modify or maintain the compressive toughness metric.

[0203] In some examples, physiological data include heart rate data, respiratory rate data, skin temperature data, or any combination thereof.

[0204] In some examples, the multiple time intervals include a first time interval and a second time interval that is more recent than the first time interval. In some examples, the weighted sum includes a first weight associated with the first time interval and a second weight associated with the second time interval. In some examples, the second weight is greater than the first weight, at least in part, based on the fact that the second time interval is more recent than the first time interval.

[0205] In some examples, wearable devices include wearable ring devices.

[0206] Additionally or alternatively, wearable application 1120 may support determining a user's stress resilience based on examples disclosed herein. 1145 may be configured or otherwise support components for acquiring physiological data from a user via a wearable device over multiple time intervals, wherein each time interval includes an waking interval for the user and a sleeping interval for the user, the physiological data including at least HRV data. In some examples, 1145 may be configured or otherwise support components for determining stress indices and recovery indices associated with waking intervals of the respective time intervals and a sleep recovery index associated with sleeping intervals of the respective time intervals, based at least in part on HRV data, for multiple time intervals. In some examples, 1145 may be configured or otherwise support components for determining a user's stress resilience measure based at least in part on a weighted sum of stress indices, recovery indices, and sleep recovery indices over multiple time intervals, wherein the weighted sum is associated with the recentity of the respective stress indices, recovery indices, and sleep recovery indices, wherein the stress resilience measure indicates the user's relative ability to cope with stress, recover from stress, or both. In some examples, 1145 can be configured or otherwise supported as a component for displaying a visual representation of compressive toughness measures to a user via a user device's GUI.

[0207] Figure 12The illustration shows a system 1200 including a device 1205 supporting a technique for measuring compressive strength using wearable-based data, according to various aspects of this disclosure. Device 1205 may be an example of device 1005 as described herein or may include components of device 1005 as described herein. Device 1205 may include an example of user device 106 as previously described herein. Device 1205 may include components for bidirectional communication, including components for transmitting and receiving communication with wearable device 104 and server 110, such as wearable application 1220, communication module 1210, antenna 1215, user interface component 1225, database (application data) 1230, at least one memory 1235, and at least one processor 1240. These components may be electronically communicated or otherwise coupled via one or more buses (e.g., bus 1245) (e.g., operational ground, communication ground, functional ground, electronic ground, electrical ground).

[0208] The communication module 1210 can manage the input and output signals of the device 1205 via the antenna 1215. The communication module 1210 may include... Figure 2 An example of the communication module 220-b of the user equipment 106 shown and described herein. In this respect, the communication module 1210 can manage communication with the ring 104 and the server 110, such as Figure 2 As illustrated, communication module 1210 can also manage peripheral devices not integrated into device 1205. In some cases, communication module 1210 may represent a physical connection or port to an external peripheral device. In some cases, communication module 1210 may utilize an operating system such as iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS / 2®, UNIX®, LINUX®, or another known operating system. In other cases, communication module 1210 may represent or interact with a wearable device (e.g., ring 104), modem, keyboard, mouse, touchscreen, or similar device. In some cases, communication module 1210 may be implemented as part of processor 1240. In some examples, a user can interact with device 1205 via communication module 1210, user interface component 1225, or hardware components controlled by communication module 1210.

[0209] In some cases, device 1205 may include a single antenna 1215. However, in other cases, device 1205 may have more than one antenna 1215, which can concurrently transmit or receive multiple wireless transmissions. Communication module 1210 can communicate bidirectionally via one or more antennas 1215, a wired link, or a wireless link, as described herein. For example, communication module 1210 may represent a wireless transceiver and can communicate bidirectionally with another wireless transceiver. Communication module 1210 may also include a modem for modulating data packets, providing modulated data packets to one or more antennas 1215 for transmission, and demodulating data packets received from one or more antennas 1215.

[0210] User interface component 1225 can manage data storage and processing in database 1230. In some cases, users can interact with user interface component 1225. In other cases, user interface component 1225 can operate automatically without user interaction. Database 1230 can be an example of a single database, a distributed database, multiple distributed databases, a data repository, a data lake, or an emergency backup database.

[0211] Memory 1235 may include RAM and ROM. Memory 1235 may store computer-readable, computer-executable software, including instructions that, when executed, cause processor 1240 to perform the various functions described herein. In some cases, memory 1235 may include, among other things, a BIOS that controls basic hardware or software operations, such as interaction with peripheral components or devices.

[0212] Processor 1240 may include intelligent hardware devices (e.g., general-purpose processors, DSPs, CPUs, microcontrollers, ASICs, FPGAs, programmable logic devices, discrete gate or transistor logic components, discrete hardware components, or any combination thereof). In some cases, processor 1240 may be configured to use a memory controller to operate a memory array. In other cases, the memory controller may be integrated into processor 1240. Processor 1240 may be configured to execute computer-readable instructions stored in memory 1235 to perform various functions (e.g., functions or tasks supporting methods and systems for sleep grading algorithms).

[0213] For example, wearable application 1220 may be configured or otherwise support components for acquiring physiological data from a user via a wearable device over multiple time intervals, wherein each time interval includes an awake interval when the user is awake and a sleep interval when the user is asleep, the physiological data including at least HRV data. Wearable application 1220 may be configured or otherwise support components for determining stress indices and recovery indices associated with the awake intervals of the respective time intervals and a sleep recovery index associated with the sleep intervals of the respective time intervals, based at least in part on HRV data, for the multiple time intervals. Wearable application 1220 may be configured or otherwise support components for determining a user's stress resilience measure based at least in part on a weighted sum of the stress index, recovery index, and sleep recovery index over the multiple time intervals, wherein the weighted sum is associated with the recency of the respective stress index, recovery index, and sleep recovery index, wherein the stress resilience measure indicates the user's relative ability to cope with stress, recover from stress, or both. Wearable application 1220 may be configured or otherwise support components for displaying a visual representation of the stress resilience measure to the user via a GUI of the user device.

[0214] Additionally or alternatively, the wearable application 1220 may support determining a user's stress resilience based on examples disclosed herein. For example, the wearable application 1220 may be configured or otherwise support components for acquiring physiological data from a user via a wearable device over multiple time intervals, each time interval including an awake interval and a sleep interval, the physiological data including at least HRV data. The wearable application 1220 may be configured or otherwise support components for determining stress indices and recovery indices associated with the awake intervals of the respective time intervals and a sleep recovery index associated with the sleep intervals of the respective time intervals, based at least in part on HRV data, for the multiple time intervals. The wearable application 1220 may be configured or otherwise support components for determining a user's stress resilience measure based at least in part on a weighted sum of the stress index, recovery index, and sleep recovery index over the multiple time intervals, wherein the weighted sum is associated with the recentity of the respective stress index, recovery index, and sleep recovery index, wherein the stress resilience measure indicates the user's relative ability to cope with stress, recover from stress, or both. The wearable application 1220 can be configured or otherwise supported as a component for displaying a visual representation of compressive toughness measures to the user via a GUI on the user device.

[0215] Wearable application 1220 may include an application (e.g., an "app"), program, software, or other component configured to facilitate communication with ring 104, server 110, other user devices 106, etc. For example, wearable application 1220 may include an application executable on user device 106, configured to receive data (e.g., physiological data) from ring 104, perform processing operations on the received data, transmit and receive data with server 110, and cause data to be presented to user 102.

[0216] Figure 13 A flowchart illustrating a method 1300 for measuring compressive strength using wearable-based data, according to various aspects of this disclosure, is shown. Operation of method 1300 can be implemented by a user device or its components as described herein. For example, operation of method 1300 can be implemented by, as referenced... Figure 1-12 The user equipment described is used to perform this function. In some examples, the user equipment may execute a set of instructions to control the functional elements of the user equipment to perform the described function. Additionally or alternatively, the user equipment may use dedicated hardware to perform aspects of the described function.

[0217] At 1305, the method may include: acquiring physiological data from a user via a wearable device over multiple time intervals, wherein each time interval includes an awake interval of the user and a sleep interval of the user, and the physiological data includes at least HRV data. Operation of block 1305 can be performed according to examples disclosed herein. In some examples, aspects of operation of 1305 may be derived from references... Figure 11 The data acquisition component 1125 described herein is used for execution.

[0218] At 1310, the method may include: determining, for multiple time intervals and at least in part based on HRV data, a stress index and a recovery index associated with an awake interval for the corresponding time interval, and a sleep recovery index associated with a sleep interval for the corresponding time interval. The operation of block 1310 may be performed according to examples disclosed herein. In some examples, aspects of the operation of 1310 may be derived from references... Figure 11 The described stress-recovery index component 1130 is used to perform this.

[0219] At 1315, the method may include: determining a user's stress resilience measure based at least in part on a weighted sum of a stress index, a recovery index, and a sleep recovery index over multiple time intervals, wherein the weighted sum is correlated with the recentity of the respective stress index, recovery index, and sleep recovery index, and wherein the stress resilience measure indicates the user's relative ability to cope with stress, recover from stress, or both. The operation of box 1315 may be performed according to examples as disclosed herein. In some examples, aspects of the operation of 1315 may be derived from references... Figure 11 The compressive toughness measurement component 1135 described herein is used to perform this measurement.

[0220] At 1320, the method may include: displaying a visual representation of the compressive toughness measure to a user via a GUI of a user device. The operation of box 1320 can be performed according to examples disclosed herein. In some examples, aspects of the operation of 1320 may be derived from references... Figure 11 The user interface component 1140 described herein is used for execution.

[0221] It should be noted that the methods described above describe possible implementations, and the operations and steps can be rearranged or otherwise modified, and other implementations are also possible. Furthermore, aspects from two or more methods within a single approach can be combined.

[0222] A method implemented via a device is described. The method may include: acquiring physiological data from a user via a wearable device over multiple time intervals, each time interval including an awake interval and a sleep interval, the physiological data including at least HRV data; determining, for the multiple time intervals and at least in part based on the HRV data, a stress index and a recovery index associated with the awake interval of the corresponding time interval, and a sleep recovery index associated with the sleep interval of the corresponding time interval; determining a stress resilience measure of the user based at least in part on a weighted sum of the stress index, recovery index, and sleep recovery index over the multiple time intervals, wherein the weighted sum is associated with the recentity of the corresponding stress index, recovery index, and sleep recovery index, wherein the stress resilience measure indicates the user's relative ability to cope with stress, recover from stress, or both; and displaying a visual representation of the stress resilience measure to the user via a GUI of the user device.

[0223] An apparatus is described. The apparatus may include one or more memories storing processor-executable code and one or more processors coupled to the one or more memories. The one or more processors may be operable, individually or collectively, to execute code such that the apparatus: acquires physiological data from a user via a wearable device over multiple time intervals, each time interval including an awake interval and a sleep interval, the physiological data including at least HRV data; determines, within the multiple time intervals and at least in part based on the HRV data, a stress index and a recovery index associated with the awake interval of the respective time interval and a sleep recovery index associated with the sleep interval of the respective time interval; determines a stress resilience measure of the user based at least in part on a weighted sum of the stress index, recovery index, and sleep recovery index over the multiple time intervals, wherein the weighted sum is associated with the recentity of the respective stress index, recovery index, and sleep recovery index, wherein the stress resilience measure indicates the user's relative ability to cope with stress, recover from stress, or both; and displays a visual representation of the stress resilience measure to the user via a GUI of the user device.

[0224] Another apparatus is described. This apparatus may include components for acquiring physiological data from a user via a wearable device over multiple time intervals, each time interval including an awake interval and a sleep interval, the physiological data including at least HRV data; components for determining, for the multiple time intervals and at least in part based on the HRV data, a stress index and a recovery index associated with the awake interval of the respective time interval and a sleep recovery index associated with the sleep interval of the respective time interval; components for determining a stress resilience metric of the user based at least in part on a weighted sum of the stress index, recovery index, and sleep recovery index over the multiple time intervals, wherein the weighted sum is associated with the recentity of the respective stress index, recovery index, and sleep recovery index, wherein the stress resilience metric indicates the user's relative ability to cope with stress, recover from stress, or both; and components for displaying a visual representation of the stress resilience metric to the user via a GUI of the user device.

[0225] A non-transitory computer-readable medium is described, which stores code. The code may include instructions executable by a processor to: acquire physiological data from a user via a wearable device over multiple time intervals, each time interval including an awake interval and a sleep interval, the physiological data including at least HRV data; determine, for the multiple time intervals and at least in part based on the HRV data, a stress index and a recovery index associated with the awake interval of the corresponding time interval and a sleep recovery index associated with the sleep interval of the corresponding time interval; determine a stress resilience metric for the user based at least in part on a weighted sum of the stress index, recovery index, and sleep recovery index over the multiple time intervals, wherein the weighted sum is associated with the recentity of the corresponding stress index, recovery index, and sleep recovery index, wherein the stress resilience metric indicates the user's relative ability to cope with stress, recover from stress, or both; and display a visual representation of the stress resilience metric to the user via a GUI of the user device.

[0226] In some examples of the methods, apparatuses, and nontransitory computer-readable media described herein, multiple time intervals include a second time interval comprising a first waking interval and a first sleeping interval following the first waking interval, and the methods, apparatuses, and nontransitory computer-readable media may include other operations, features, components, or instructions for determining the absence of physiological data collected during the first waking interval or the first sleeping interval and avoiding determining stress indices, recovery indices, and sleep recovery indices corresponding to the first time interval based at least in part on the absence of physiological data collected during the first waking interval or the first sleeping interval.

[0227] In some examples of the methods, apparatuses and nontransitory computer-readable media described herein, the stress index and recovery index associated with the waking interval of the corresponding time interval may be based at least in part on a comparison of a first portion of HRV data collected during the waking interval with baseline daytime HRV data associated with the user during periods when the user is likely to be awake, and the sleep recovery index associated with the sleeping interval of the corresponding time interval may be based at least in part on a comparison of a second portion of HRV data collected during the sleeping interval with baseline nighttime HRV data associated with the user during periods when the user is likely to be asleep.

[0228] In some examples of the methods, apparatuses and nontransitory computer-readable media described herein, the sleep recovery index for a given time interval is determined at least in part based on the weighted average of the duration of the sleep interval for that time interval, the sleep quality of the user during the sleep interval for that time interval, the user's resting heart rate during the entire sleep interval for that time interval, and the HRV variance of the HRV data collected during the sleep interval for that time interval.

[0229] Some examples of the methods, apparatuses, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for classifying each of a plurality of time intervals using a compressive strength level based at least in part on a comparison of a pressure index, a recovery index, and a sleep recovery index corresponding to the respective time intervals, wherein a visual representation of the compressive strength measure may be based at least in part on the classification.

[0230] Some examples of the methods, apparatuses, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for displaying, at least in part, a number of compressive toughness measures corresponding to multiple time intervals to a user via a GUI of a user device, based on at least a classification.

[0231] Examples of the methods, apparatuses, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for displaying to a user, via a GUI of a user device, indications of stress indices, recovery indices, and sleep recovery indices corresponding to time intervals among a plurality of time intervals.

[0232] Some examples of the methods, apparatuses and non-transitory computer-readable media described herein may also include operations, features, components or instructions for displaying feedback to a user via a GUI of a user device and at least in part based on a determined compressive toughness metric, the feedback including instructions for maintaining one or more first actions of the user, modifying one or more second actions of the user, or both, wherein the instructions may be configured to modify or maintain the compressive toughness metric.

[0233] In some examples of the methods, apparatuses and non-transitory computer-readable media described herein, physiological data include heart rate data, respiratory rate data, skin temperature data or any combination thereof.

[0234] In some examples of the methods, apparatuses and non-transitory computer-readable media described herein, multiple time intervals include a first time interval and a second time interval that may be more recent than the first time interval, the weighted sum includes a first weight associated with the first time interval and a second weight associated with the second time interval, and the second weight may be greater than the first weight, at least in part, based on the fact that the second time interval is more recent than the first time interval.

[0235] In some examples of the methods, apparatuses, and non-transitory computer-readable media described herein, wearable devices include wearable ring devices.

[0236] A method for determining a user's stress resilience via a device is described. The method may include: acquiring physiological data from a user via a wearable device over multiple time intervals, each time interval including an waking interval and a sleeping interval, the physiological data including at least HRV data; determining, for the multiple time intervals and at least in part based on the HRV data, a stress index and a recovery index associated with the waking interval of the corresponding time interval, and a sleep recovery index associated with the sleeping interval of the corresponding time interval; determining a stress resilience metric for the user based at least in part on a weighted sum of the stress index, recovery index, and sleep recovery index over the multiple time intervals, wherein the weighted sum is associated with the recentity of the corresponding stress index, recovery index, and sleep recovery index, wherein the stress resilience metric indicates the user's relative ability to cope with stress, recover from stress, or both; and displaying a visual representation of the stress resilience metric to the user via a GUI of the user device.

[0237] An apparatus for determining a user's stress resilience is described. The apparatus may include one or more memories storing processor-executable code and one or more processors coupled to the one or more memories. The one or more processors may be operable, individually or collectively, to execute code such that the apparatus: acquires physiological data from a user via a wearable device over multiple time intervals, each time interval including an awake interval and a sleep interval, the physiological data including at least HRV data; determines, for the multiple time intervals and at least in part based on the HRV data, a stress index and a recovery index associated with the awake interval of the corresponding time interval and a sleep recovery index associated with the sleep interval of the corresponding time interval; determines a stress resilience metric for the user based at least in part on a weighted sum of the stress index, recovery index, and sleep recovery index over the multiple time intervals, wherein the weighted sum is associated with the recentity of the corresponding stress index, recovery index, and sleep recovery index, wherein the stress resilience metric indicates the user's relative ability to cope with stress, recover from stress, or both; and displays a visual representation of the stress resilience metric to the user via a GUI of the user device.

[0238] Another apparatus for determining a user's stress resilience is described. The apparatus may include components for acquiring physiological data from a user via a wearable device over multiple time intervals, each time interval including an waking interval and a sleeping interval, the physiological data including at least HRV data; components for determining, for the multiple time intervals and at least in part based on the HRV data, a stress index and a recovery index associated with the waking interval of the corresponding time interval and a sleep recovery index associated with the sleeping interval of the corresponding time interval; components for determining a measure of the user's stress resilience based at least in part on a weighted sum of the stress index, recovery index, and sleep recovery index over the multiple time intervals, wherein the weighted sum is associated with the recentity of the corresponding stress index, recovery index, and sleep recovery index, wherein the stress resilience measure indicates the user's relative ability to cope with stress, recover from stress, or both; and components for displaying a visual representation of the stress resilience measure to the user via a GUI of the user device.

[0239] A non-transitory computer-readable medium is described, storing code for determining a user's stress resilience. The code may include instructions executable by a processor to: acquire physiological data from a user via a wearable device over multiple time intervals, each time interval including an awake interval and a sleep interval, the physiological data including at least HRV data; determine, for the multiple time intervals and at least in part based on the HRV data, a stress index and a recovery index associated with the awake interval of the corresponding time interval and a sleep recovery index associated with the sleep interval of the corresponding time interval; determine a stress resilience metric for the user based at least in part on a weighted sum of the stress index, recovery index, and sleep recovery index over the multiple time intervals, wherein the weighted sum is associated with the recentity of the corresponding stress index, recovery index, and sleep recovery index, wherein the stress resilience metric indicates the user's relative ability to cope with stress, recover from stress, or both; and display a visual representation of the stress resilience metric to the user via a GUI of the user device.

[0240] A method for measuring a user's acute stress via a device is described. The method may include: acquiring physiological data from a user via a wearable device over an entire time interval, the physiological data including at least heart rate data and motion data; determining, at least in part, that the user was awake and sedentary throughout the time interval based on the heart rate data meeting a heart rate threshold and the motion data meeting a motion threshold; determining, at least in part, the user's daytime HRV value during the time interval based on determining that the user was both sedentary and awake throughout the time interval; comparing the user's daytime HRV value with a baseline daytime HRV data associated with the user, the baseline daytime HRV data being determined based on additional physiological data acquired from the user over an entire reference time interval encompassing multiple days prior to the time interval, wherein the baseline daytime HRV data was collected during the period of the reference time interval in which the user was awake and sedentary; determining, at least in part, the user's acute stress level during the time interval based on comparing the daytime HRV value with the baseline daytime HRV data, the acute stress level being associated with a relative amount of stress experienced by the user throughout the time interval; and displaying a visual representation of the acute stress level to the user via a GUI of the user device.

[0241] An apparatus for measuring a user's acute pressure is described. The apparatus may include one or more memories storing processor-executable code and one or more processors coupled to the memories. The one or more processors may be operable, individually or collectively, to execute code that causes the device to: acquire physiological data from a user via a wearable device over an entire time interval, the physiological data including at least heart rate data and motion data; determine, at least in part, that the user was awake and sedentary throughout the time interval based on the heart rate data meeting a heart rate threshold and the motion data meeting a motion threshold; determine, at least in part, that the user was both sedentary and awake throughout the time interval based on the determination that the user was both awake and sedentary throughout the time interval; compare the user's daytime HRV value with a baseline daytime HRV data associated with the user, the baseline daytime HRV data being determined based on additional physiological data acquired from the user over an entire reference time interval encompassing multiple days prior to the time interval, wherein the baseline daytime HRV data was collected during the period of the reference time interval in which the user was awake and sedentary; determine, at least in part, the user's acute stress level during the time interval based on the comparison of the daytime HRV value with the baseline daytime HRV data, the acute stress level being associated with the relative amount of stress experienced by the user throughout the time interval; and display a visual representation of the acute stress level to the user via a GUI of the user device.

[0242] Another apparatus for measuring a user's acute stress is described. The apparatus may include components for acquiring physiological data from the user via a wearable device over an entire time interval, the physiological data including at least heart rate data and motion data; components for determining, at least in part, that the user was awake and sedentary throughout the time interval based on the heart rate data meeting a heart rate threshold and the motion data meeting a motion threshold; components for determining the user's daytime HRV value during the time interval based at least in part on determining that the user was both sedentary and awake throughout the time interval; components for comparing the user's daytime HRV value with a baseline daytime HRV data associated with the user, the baseline daytime HRV data being determined based on additional physiological data acquired from the user over an entire reference time interval encompassing multiple days prior to the time interval, wherein the baseline daytime HRV data was collected during the period of the reference time interval in which the user was awake and sedentary; components for determining the user's acute stress level during the time interval based at least in part on comparing the daytime HRV value with the baseline daytime HRV data, the acute stress level being associated with a relative amount of stress experienced by the user throughout the time interval; and components for displaying a visual representation of the acute stress level to the user via a GUI of the user device.

[0243] A non-transitory computer-readable medium is described, which stores code for determining acute stress in a user. The code may include instructions that can be executed by a processor to: acquire physiological data from a user via a wearable device over an entire time interval, the physiological data including at least heart rate data and motion data; determine, at least in part, that the user was awake and sedentary throughout the time interval based on the heart rate data meeting a heart rate threshold and the motion data meeting a motion threshold; determine, at least in part, the user's daytime HRV value during the time interval based on the determination that the user was both sedentary and awake throughout the time interval; compare the user's daytime HRV value with a baseline daytime HRV data associated with the user, the baseline daytime HRV data being determined based on additional physiological data acquired from the user over an entire reference time interval encompassing multiple days prior to the time interval, wherein the baseline daytime HRV data was collected during the period of the reference time interval in which the user was awake and sedentary; determine, at least in part, the user's acute stress level during the time interval based on the comparison of the daytime HRV value with the baseline daytime HRV data, the acute stress level being associated with the relative amount of stress experienced by the user throughout the time interval; and display a visual representation of the acute stress level to the user via a GUI of the user device.

[0244] Some examples of the methods, apparatuses, and non-transitory computer-readable media described herein may also include operations, characteristics, components, or instructions for determining the expected daytime HRV variance associated with a user based at least in part on baseline nighttime HRV data obtained from the user while the user may be sleeping, and for determining acute stress levels based at least in part on comparing the difference between the daytime HRV value and the baseline daytime HRV data with the expected daytime HRV variance.

[0245] Some examples of the methods, apparatuses and nontransitory computer-readable media described herein may also include operations, features, components or instructions for determining the expected intraday HRV variance associated with users based at least in part on multiple intraday HRV values ​​of multiple users associated with a set of features that may be common to multiple users.

[0246] Examples of the methods, apparatuses, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for determining whether heart rate data meets one or more measurement quality criteria, wherein the determination of daytime HRV values ​​may be based at least in part on the heart rate data meeting one or more measurement quality criteria.

[0247] Examples of the methods, apparatuses, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for determining that heart rate data fails to meet one or more measurement quality criteria, normalizing heart rate data, skin temperature data, or both obtained from a user via a wearable device, inputting the normalized heart rate data, normalized skin temperature data, or both into a machine learning model, and obtaining an interpolated daytime HRV value as an output of the machine learning model based at least in part on the input of the normalized heart rate data, normalized skin temperature data, or both into the machine learning model, the interpolated daytime HRV value including daytime HRV values ​​that can be compared with baseline daytime HRV data.

[0248] Some examples of the methods, apparatuses, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for scaling normalized heart rate data, normalized skin temperature data, or both, at least in part based on baseline heart rate data and baseline skin temperature data associated with a user, wherein input of normalized heart rate data, normalized skin temperature data, or both may be at least in part based on scaling.

[0249] Some examples of the methods, apparatuses, and non-transitory computer-readable media described herein may also include operations, features, devices, or instructions for inputting motion data into a machine learning model, wherein obtaining interpolated daytime HRV values ​​from the machine learning model may be based at least in part on inputting motion data into the machine learning model.

[0250] Examples of the methods, apparatuses, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for inputting baseline daytime HRV data, baseline heart rate data corresponding to the baseline daytime HRV data, baseline skin temperature data corresponding to the baseline daytime HRV data, or any combination thereof into a machine learning model and training the machine learning model to generate, at least in part, imputed daytime HRV values ​​associated with a user based on the input baseline daytime HRV data, baseline heart rate data, baseline skin temperature data, or any combination thereof, wherein obtaining the imputed daytime HRV values ​​may be at least in part based on training the machine learning model.

[0251] Some examples of the methods, apparatuses, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for classifying time intervals into one of a stress period, a recovery period, or a neutral period based at least in part on the acute stress level satisfying a stress threshold metric, a recovery threshold metric, or both, wherein a visual representation of the acute stress level may be based at least in part on the classification.

[0252] In some examples of the methods, apparatuses and non-transitory computer-readable media described herein, feedback is provided to a user via a user device's GUI, including instructions for modifying one or more user behaviors corresponding to the user's acute stress level.

[0253] Examples of the methods, apparatuses, and nontransitory computer-readable media described herein may also include operations, features, components, or instructions for determining a user's second daytime HRV value based at least in part on acquiring second physiological data throughout the second time interval, comparing the second daytime HRV value with baseline daytime HRV data, determining a user's second acute stress level during the second time interval based at least in part on comparing the second daytime HRV value with baseline daytime HRV data, and providing a second visual representation of the second acute stress level to the user via a GUI of the user device.

[0254] In some examples of the methods, apparatuses and nontransitory computer-readable media described herein, the second visual representation indicates the relative change between an acute pressure level associated with a time interval and a second acute pressure level associated with a second time interval.

[0255] In some examples of the methods, apparatuses and non-transitory computer-readable media described herein, the time intervals include multiple seconds, minutes or hours within a day.

[0256] In some examples of the methods, apparatuses, and non-transitory computer-readable media described herein, wearable devices include wearable ring devices.

[0257] A method for measuring a user's acute stress via a device is described. The method may include: acquiring physiological data from the user via a wearable device over an entire time interval, the physiological data including at least heart rate data and motion data; determining, at least in part, that the user was awake and sedentary throughout the time interval based on the heart rate data and motion data; determining, at least in part, the user's daytime HRV value during the time interval based on determining that the user was both sedentary and awake throughout the time interval; comparing the user's daytime HRV value with a baseline daytime HRV data associated with the user, the baseline daytime HRV data being determined based on additional physiological data acquired from the user over an entire reference time interval encompassing multiple days prior to the time interval, wherein the baseline daytime HRV data was collected during the period of the reference time interval in which the user was awake and sedentary; determining, at least in part, the user's acute stress level during the time interval based on comparing the daytime HRV value with the baseline daytime HRV data, the acute stress level being associated with a relative amount of stress experienced by the user throughout the time interval; and displaying a visual representation of the acute stress level to the user via a GUI of the user device.

[0258] An apparatus for measuring a user's acute pressure is described. The apparatus may include one or more memories storing processor-executable code and one or more processors coupled to the memories. The one or more processors may be operable, individually or collectively, to execute code that causes the device to: acquire physiological data from a user via a wearable device over an entire time interval, the physiological data including at least heart rate data and motion data; determine, at least in part, that the user was awake and sedentary over the entire time interval based on the heart rate data and motion data; determine, at least in part, the user's daytime HRV value over the time interval based on the determination that the user was both sedentary and awake over the entire time interval; compare the user's daytime HRV value with a baseline daytime HRV data associated with the user, the baseline daytime HRV data being determined based on additional physiological data acquired from the user over an entire reference time interval encompassing multiple days prior to the time interval, wherein the baseline daytime HRV data was collected during the period of the reference time interval in which the user was awake and sedentary; determine, at least in part, the user's acute stress level during the time interval based on the comparison of the daytime HRV value with the baseline daytime HRV data, the acute stress level being associated with the relative amount of stress experienced by the user over the entire time interval; and display a visual representation of the acute stress level to the user via a GUI of the user device.

[0259] Another apparatus for measuring acute stress in a user is described. The apparatus may include components for acquiring physiological data from the user via a wearable device over an entire time interval, the physiological data including at least heart rate data and motion data; components for determining, at least in part, whether the user was awake and sedentary during the entire time interval based on the heart rate data and motion data; components for determining the user's daytime HRV value during the time interval based at least in part on determining that the user was both sedentary and awake during the entire time interval; components for comparing the user's daytime HRV value with a baseline daytime HRV data associated with the user, the baseline daytime HRV data being determined based on additional physiological data acquired from the user over an entire reference time interval encompassing multiple days prior to the time interval, wherein the baseline daytime HRV data was collected during the period of the reference time interval when the user was awake and sedentary; components for determining the user's acute stress level during the time interval based at least in part on comparing the daytime HRV value with the baseline daytime HRV data, the acute stress level being associated with a relative amount of stress experienced by the user over the entire time interval; and components for displaying a visual representation of the acute stress level to the user via a GUI of the user device.

[0260] A non-transitory computer-readable medium is described, storing code for determining acute stress in a user. The code may include instructions executable by a processor to: acquire physiological data from the user via a wearable device over an entire time interval, the physiological data including at least heart rate data and motion data; determine, at least in part, that the user was awake and sedentary throughout the time interval based on the heart rate and motion data; determine, at least in part, the user's daytime HRV value during the time interval based on the determination that the user was both sedentary and awake throughout the time interval; compare the user's daytime HRV value with a baseline daytime HRV data associated with the user, the baseline daytime HRV data being determined based on additional physiological data acquired from the user over an entire reference time interval encompassing multiple days prior to the time interval, wherein the baseline daytime HRV data was collected during the period of the reference time interval in which the user was awake and sedentary; determine, at least in part, the user's acute stress level during the time interval based on the comparison of the daytime HRV value with the baseline daytime HRV data, the acute stress level being associated with a relative amount of stress experienced by the user throughout the time interval; and display a visual representation of the acute stress level to the user via a GUI of the user device.

[0261] A method implemented via a device is described. The method may include: acquiring baseline physiological data from a user via a wearable device; determining a baseline stress level associated with the user based at least in part on the baseline physiological data, one or more user inputs received via the user device, or both; determining a first baseline HRV value for the user during periods of wakefulness and a second baseline HRV value for the user during periods of sleep, based at least in part on the baseline physiological data; acquiring additional physiological data from the user via the wearable device over an entire time interval spanning multiple days and multiple nights; determining a first set of HRV values ​​for the user during periods of wakefulness and a second set of HRV values ​​for the user during periods of sleep, based at least in part on the additional physiological data; determining a cumulative stress level for the user over the entire time interval based at least in part on a first comparison of the first HRV value set with a first baseline HRV value and a second comparison of the second HRV value set with a second baseline HRV value, wherein the cumulative stress level is at least in part based on the baseline stress level, and wherein the cumulative stress level is associated with the total amount of stress experienced by the user over the entire time interval, a trend in the user's stress level over the entire time interval, or both; and displaying a visual representation of the cumulative stress level to the user via a GUI of the user device.

[0262] An apparatus is described. The apparatus may include one or more memories storing processor-executable code and one or more processors coupled to the one or more memories. The one or more processors may be operable, individually or collectively, to execute code such that the apparatus: acquires baseline physiological data from a user via a wearable device; determines a baseline stress level associated with the user based at least in part on the baseline physiological data, one or more user inputs received via a user device, or both; determines a first baseline HRV value for the user during periods of wakefulness and a second baseline HRV value for the user during periods of sleep, based at least in part on the baseline physiological data; acquires additional physiological data from the user via the wearable device over an entire time interval spanning multiple days and multiple nights; and determines the user's stress level during periods of wakefulness based at least in part on the additional physiological data. The system comprises: a first set of HRV values ​​during a time interval in which the user is asleep and a second set of HRV values ​​during a time interval in which the user is asleep; determining the user's cumulative stress level over the entire time interval based at least in part on a first comparison of the first set of HRV values ​​with a first baseline HRV value and a second comparison of the second set of HRV values ​​with a second baseline HRV value, wherein the cumulative stress level is at least in part based on the baseline stress level, and wherein the cumulative stress level is associated with the total amount of stress experienced by the user over the entire time interval, a trend in the user's stress level over the entire time interval, or both; and displaying a visual representation of the cumulative stress level to the user via a GUI of the user device.

[0263] Another apparatus is described. This apparatus may include: components for acquiring baseline physiological data from a user via a wearable device; components for determining a baseline stress level associated with the user based at least in part on the baseline physiological data, one or more user inputs received via a user device, or both; components for determining a first baseline HRV value of the user during periods of wakefulness and a second baseline HRV value of the user during periods of sleep, based at least in part on the baseline physiological data; components for acquiring additional physiological data from the user via the wearable device over an entire time interval spanning multiple days and nights; and components for determining the user's time frame during periods of wakefulness based at least in part on the additional physiological data. The device includes: a first set of HRV values ​​for a period of time and a second set of HRV values ​​for a period of time during which the user is asleep; a device for determining the user's cumulative stress level over the entire time interval based at least in part on a first comparison of the first set of HRV values ​​with a first baseline HRV value and a second comparison of the second set of HRV values ​​with a second baseline HRV value, wherein the cumulative stress level is at least in part based on the baseline stress level and wherein the cumulative stress level is associated with the total amount of stress experienced by the user over the entire time interval, a trend in the user's stress level over the entire time interval, or both; and a device for displaying a visual representation of the cumulative stress level to the user via a GUI of the user device.

[0264] A non-transitory computer-readable medium is described, which stores code. The code may include instructions that can be executed by a processor to: acquire baseline physiological data from a user via a wearable device; determine a baseline stress level associated with the user based at least in part on the baseline physiological data, one or more user inputs received via the user device, or both; determine a first baseline HRV value for the user during periods when the user is awake and a second baseline HRV value for the user during periods when the user is asleep, based at least in part on the baseline physiological data; acquire additional physiological data from the user via the wearable device over an entire time interval spanning multiple days and multiple nights; determine a first set of HRV values ​​for the user during periods when the user is awake and a second set of HRV values ​​for the user during periods when the user is asleep, based at least in part on the additional physiological data; determine a cumulative stress level for the user over the entire time interval based at least in part on a first comparison of the first set of HRV values ​​with a first baseline HRV value and a second comparison of the second set of HRV values ​​with a second baseline HRV value, wherein the cumulative stress level is at least in part based on the baseline stress level and wherein the cumulative stress level is associated with the total amount of stress experienced by the user over the entire time interval, a trend in the user's stress level over the entire time interval, or both; and display a visual representation of the cumulative stress level to the user via a GUI of the user device.

[0265] Examples of the methods, apparatuses, and nontransitory computer-readable media described herein may also include operations, features, components, or instructions for determining one or more stress scores associated with the user during a period of time in which the user may be awake, based at least in part on a first comparison of a first set of HRV values ​​with a first baseline HRV value, and for determining one or more recovery scores associated with the user during a period of time in which the user may be asleep, based at least in part on a second comparison of a second set of HRV values ​​with a second baseline HRV value, wherein the cumulative stress level may be based at least in part on one or more stress scores and one or more recovery scores.

[0266] Some examples of the methods, apparatuses, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for determining a baseline stress level associated with a user, at least in part, based on baseline physiological data, including heart rate data, respiratory rate data, skin temperature data, or any combination thereof, and wherein determining the cumulative stress level may be based at least in part on the baseline stress level.

[0267] Some examples of the methods, apparatuses, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for predicting a user’s burnout, chronic stress, or both, based at least in part on a comparison between a cumulative stress level and a baseline stress level associated with the user, and causing the user device’s GUI to display an alarm associated with the burnout, chronic stress, or both.

[0268] Some examples of the methods, apparatuses, and non-transitory computer-readable media described herein may also include operations, features, components, or instructions for receiving from a user equipment one or more user inputs including one or more characteristics associated with a user, wherein determining a baseline pressure level may be based at least in part on the user input.

[0269] Some examples of the methods, apparatuses and non-transitory computer-readable media described herein may also include operations, features, components or instructions for acquiring additional baseline physiological data associated with multiple users and a set of features common to multiple users, and for determining multiple baseline stress levels associated with multiple users, wherein determining the baseline stress levels associated with users may be based at least in part on determining the multiple baseline stress levels and comparing the baseline physiological data associated with users with the additional baseline physiological data associated with multiple users.

[0270] In some examples of the methods, apparatuses and nontransitory computer-readable media described herein, acquiring baseline physiological data may include operations, features, components or instructions for providing instructions to a user via a GUI of a user device to allow the user to modify one or more behaviors associated with the user’s target stress level, receiving multiple physiological measurements associated with one or more behaviors from a wearable device and at least in part based on providing instructions to the user, and determining a baseline stress level associated with the user based at least in part on comparing the multiple physiological measurements with baseline physiological data, wherein determining the cumulative stress level may be at least in part based on determining the baseline stress level.

[0271] In some examples of the methods, apparatuses and non-transitory computer-readable media described herein, visual representations indicate the relative change between a user's baseline stress level and a cumulative stress level.

[0272] Some examples of the methods, apparatuses and non-transitory computer-readable media described herein may also include operations, features, components or instructions for classifying multiple time periods within a time interval during which a user may be awake or asleep into one of a stress period, a recovery period or a neutral period, based at least in part on a comparison of a first set of HRV values ​​with a first baseline HRV value and a comparison of a second set of HRV values ​​with a second baseline HRV value, wherein determining the cumulative stress level may be based at least in part on the classification.

[0273] In some examples of the methods, apparatuses and non-transitory computer-readable media described herein, feedback is provided to a user via a user device's GUI, including instructions for modifying one or more user behaviors configured to modify the user's cumulative stress level.

[0274] In some examples of the methods, apparatuses and nontransitory computer-readable media described herein, baseline physiological data can be acquired over an entire second time interval preceding the first time interval, which includes a second number of days and a second number of nights.

[0275] In some examples of the methods, apparatuses, and non-transitory computer-readable media described herein, wearable devices include wearable ring devices.

[0276] A method for measuring a user's cumulative pressure over time using a device is described. The method may include: acquiring baseline physiological data from a user via a wearable device; determining a baseline stress level associated with the user based at least in part on the baseline physiological data, one or more user inputs received via the user device, or both; determining a first baseline HRV value for the user during periods when the user is awake and a second baseline HRV value for the user during periods when the user is asleep, based at least in part on the baseline physiological data; acquiring additional physiological data from the user via the wearable device over an entire time interval spanning multiple days and multiple nights; determining a first set of HRV values ​​for the user during periods when the user is awake and a second set of HRV values ​​for the user during periods when the user is asleep, based at least in part on the additional physiological data; determining a cumulative stress level for the user over the entire time interval based at least in part on a first comparison of the first set of HRV values ​​with a first baseline HRV value and a second comparison of the second set of HRV values ​​with a second baseline HRV value, wherein the cumulative stress level is at least in part based on the baseline stress level, and wherein the cumulative stress level is associated with the total amount of stress experienced by the user over the entire time interval, a trend in the user's stress level over the entire time interval, or both; and displaying a visual representation of the cumulative stress level to the user via a GUI of the user device.

[0277] A device for measuring the cumulative stress of a user over time is described. The device may include one or more memories storing processor-executable code and one or more processors coupled to the one or more memories. The one or more processors may be operable, individually or collectively, to execute code such that the device: acquires baseline physiological data from the user via a wearable device; determines a baseline stress level associated with the user based at least in part on the baseline physiological data, one or more user inputs received via a user device, or both; determines a first baseline HRV value for the user during periods of wakefulness and a second baseline HRV value for the user during periods of sleep, based at least in part on the baseline physiological data; acquires additional physiological data from the user via the wearable device over an entire time interval spanning multiple days and multiple nights; and determines the user's stress level during periods of wakefulness based at least in part on the additional physiological data. The system comprises: a first set of HRV values ​​during a time interval in which the user is asleep and a second set of HRV values ​​during a time interval in which the user is asleep; determining the user's cumulative stress level over the entire time interval based at least in part on a first comparison of the first set of HRV values ​​with a first baseline HRV value and a second comparison of the second set of HRV values ​​with a second baseline HRV value, wherein the cumulative stress level is at least in part based on the baseline stress level, and wherein the cumulative stress level is associated with the total amount of stress experienced by the user over the entire time interval, a trend in the user's stress level over the entire time interval, or both; and displaying a visual representation of the cumulative stress level to the user via a GUI of the user device.

[0278] Another apparatus for measuring cumulative stress in a user over time is described. The apparatus may include: components for acquiring baseline physiological data from the user via a wearable device; components for determining a baseline stress level associated with the user based at least in part on the baseline physiological data, one or more user inputs received via a user device, or both; components for determining a first baseline HRV value for the user during periods of wakefulness and a second baseline HRV value for the user during periods of sleep, based at least in part on the baseline physiological data; components for acquiring additional physiological data from the user via the wearable device over an entire time interval spanning multiple days and nights; and components for determining the user's stress level during periods of wakefulness based at least in part on the additional physiological data. The device includes: a first set of HRV values ​​for a period of time and a second set of HRV values ​​for a period of time during which the user is asleep; a device for determining the user's cumulative stress level over the entire time interval based at least in part on a first comparison of the first set of HRV values ​​with a first baseline HRV value and a second comparison of the second set of HRV values ​​with a second baseline HRV value, wherein the cumulative stress level is at least in part based on the baseline stress level and wherein the cumulative stress level is associated with the total amount of stress experienced by the user over the entire time interval, a trend in the user's stress level over the entire time interval, or both; and a device for displaying a visual representation of the cumulative stress level to the user via a GUI of the user device.

[0279] A non-transitory computer-readable medium is described, which stores code for measuring the cumulative stress of a user over time. The code may include instructions that can be executed by a processor to: acquire baseline physiological data from a user via a wearable device; determine a baseline stress level associated with the user based at least in part on the baseline physiological data, one or more user inputs received via the user device, or both; determine a first baseline HRV value for the user during periods when the user is awake and a second baseline HRV value for the user during periods when the user is asleep, based at least in part on the baseline physiological data; acquire additional physiological data from the user via the wearable device over an entire time interval spanning multiple days and multiple nights; determine a first set of HRV values ​​for the user during periods when the user is awake and a second set of HRV values ​​for the user during periods when the user is asleep, based at least in part on the additional physiological data; determine a cumulative stress level for the user over the entire time interval based at least in part on a first comparison of the first set of HRV values ​​with a first baseline HRV value and a second comparison of the second set of HRV values ​​with a second baseline HRV value, wherein the cumulative stress level is at least in part based on the baseline stress level and wherein the cumulative stress level is associated with the total amount of stress experienced by the user over the entire time interval, a trend in the user's stress level over the entire time interval, or both; and display a visual representation of the cumulative stress level to the user via a GUI of the user device.

[0280] The description herein, illustrated with reference to the accompanying drawings, describes exemplary configurations and does not represent all examples that can be implemented or that are within the scope of the claims. The term "exemplary" as used herein means "serving as an example, instance, or illustration," and not "preferred" or "superior to other examples." Detailed descriptions include specific details for the purpose of providing an understanding of the described techniques. However, these techniques can be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form to avoid obscuring the concepts of the described examples.

[0281] In the accompanying drawings, similar components or features may have the same reference numerals. Furthermore, various components of the same type can be distinguished by a reference numeral marked with a dash and a second numeral to differentiate them. If only the first reference numeral is used in the specification, the description applies to any of the similar components having the same first reference numeral, without regard to the second reference numeral.

[0282] The information and signals described herein can be represented using any of a variety of different techniques and means. For example, data, instructions, commands, information, signals, bits, symbols, and chips referred to in the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or light particles, or any combination thereof.

[0283] The various illustrative boxes and modules described in connection with this disclosure may be implemented or performed using a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but alternatively, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors incorporating a DSP core, or any other such configuration).

[0284] The functions described herein can be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions can be stored on or transmitted via a computer-readable medium as one or more instructions or code. Other examples and implementations are within the scope of this disclosure and the appended claims. For example, due to the nature of software, the functions described above can be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Features implementing the functions can also be physically located in various locations, including being distributed such that different parts of the functions are implemented in different physical locations. Furthermore, as used herein, including in the claims, the word "or" as used in a list of items (e.g., a list of items beginning with phrases such as "at least one of" or "one or more") indicates an inclusive list, such that, for example, a list of at least one of A, B, or C means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Moreover, as used herein, the phrase "based on" should not be construed as referring to a set of closing conditions. For example, without departing from the scope of this disclosure, an exemplary step described as "based on condition A" may be based on both condition A and condition B. In other words, as used herein, the phrase "based on" should be interpreted in the same way as the phrase "at least partially based on".

[0285] Computer-readable media includes both non-transitory computer storage media and communication media, including any media that facilitates the transfer of a computer program from one place to another. Non-transitory storage media can be any available medium accessible by a general-purpose or special-purpose computer. By way of example, and not limitation, non-transitory computer-readable media may include RAM, ROM, electrically erasable programmable ROM (EEPROM), disc-on-CD ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other means of carrying or storing desired program code in the form of instructions or data structures and accessible by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Furthermore, any connection is appropriately referred to as computer-readable media. For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of media. As used in this article, disks and optical discs include CDs, laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, where disks typically reproduce data magnetically, while optical discs use lasers to reproduce data optically. Combinations of these are also included within the scope of computer-readable media.

[0286] The description herein is provided to enable those skilled in the art to make or use this disclosure. Various modifications to this disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the scope of this disclosure. Therefore, this disclosure is not limited to the examples and designs described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining a user's compressive toughness, comprising: Physiological data is acquired from a user via a wearable device at multiple time intervals, wherein each time interval includes an awake interval when the user is awake and a sleep interval when the user is asleep, and the physiological data includes at least heart rate variability (HRV) data. For the plurality of time intervals and based at least in part on the HRV data, determine the stress index and recovery index associated with the waking interval of the corresponding time interval and the sleep recovery index associated with the sleeping interval of the corresponding time interval; The user's stress resilience metric is determined at least in part based on a weighted sum of the stress index, the recovery index, and the sleep recovery index over the plurality of time intervals, wherein the weighted sum is associated with the recentity of the respective stress index, recovery index, and sleep recovery index, and wherein the stress resilience metric indicates the user's relative ability to cope with stress, recover from stress, or both. as well as A visual representation of the compressive toughness measure is displayed to the user via a graphical user interface (GUI) on the user equipment.

2. The method of claim 1, wherein the plurality of time intervals includes a second time interval, the second time interval including a first waking interval and a first sleeping interval following the first waking interval, the method further comprising: It was determined that the physiological data collected during the first waking interval or the first sleeping interval did not exist; as well as Avoid determining the stress index, recovery index, and sleep recovery index corresponding to the first time interval based at least in part on the absence of the physiological data collected during the first awake interval or the first sleep interval.

3. The method as described in claim 1, The stress index and recovery index associated with the waking interval of the corresponding time interval are based at least in part on a comparison of a first portion of the HRV data collected during the waking interval with baseline daytime HRV data associated with the user during the user's waking period, and The sleep recovery index associated with the sleep interval of the corresponding time interval is based at least in part on a comparison of a second portion of the HRV data collected during the sleep interval with baseline nighttime HRV data associated with the user during the period when the user was asleep.

4. The method of claim 1, wherein the sleep recovery index of the corresponding time interval is determined at least in part based on the weighted average of the duration of the sleep interval of the corresponding time interval, the sleep quality of the user during the sleep interval of the corresponding time interval, the resting heart rate of the user during the entire sleep interval of the corresponding time interval, and the HRV variance of the HRV data collected during the sleep interval of the corresponding time interval.

5. The method of claim 1, further comprising: Each of the plurality of time intervals is classified using a stress resilience level, at least in part, based on a comparison of the stress index, the recovery index, and the sleep recovery index corresponding to the respective time intervals, wherein the visual representation of the stress resilience measure is at least in part based on the classification.

6. The method of claim 5, further comprising: Based at least in part on the classification, multiple compressive toughness measures corresponding to the multiple time intervals are displayed to the user via the GUI of the user device.

7. The method of claim 1, further comprising: The user displays to the user, via the GUI of the user device, indications of the stress index, the recovery index, and the sleep recovery index corresponding to the time intervals among the plurality of time intervals.

8. The method of claim 1, further comprising: Feedback is displayed to the user via the GUI of the user device and at least in part based on the determination of the compressive toughness metric. The feedback includes instructions for maintaining one or more first behaviors of the user, modifying one or more second behaviors of the user, or both, wherein the instructions are configured to modify or maintain the compressive toughness metric.

9. The method of claim 1, wherein the physiological data includes heart rate data, respiratory rate data, skin temperature data, or any combination thereof.

10. The method of claim 1, wherein: The plurality of time intervals includes a first time interval and a second time interval that is more recent than the first time interval. The weighted sum includes a first weight associated with the first time interval and a second weight associated with the second time interval. The second weight is greater than the first weight, at least in part, based on the fact that the second time interval is more recent than the first time interval.

11. The method of claim 1, wherein the wearable device includes a wearable ring device.

12. An apparatus for determining a user's compressive toughness, comprising: At least one processor; At least one memory, said at least one memory being coupled to said at least one processor; as well as Instructions, which are stored in the at least one memory and can be executed by the at least one processor, to cause the device to: Physiological data is acquired from a user via a wearable device at multiple time intervals, wherein each time interval includes an awake interval when the user is awake and a sleep interval when the user is asleep, and the physiological data includes at least heart rate variability (HRV) data. For the plurality of time intervals and based at least in part on the HRV data, determine the stress index and recovery index associated with the waking interval of the corresponding time interval and the sleep recovery index associated with the sleeping interval of the corresponding time interval; The user's stress resilience metric is determined at least in part based on a weighted sum of the stress index, the recovery index, and the sleep recovery index over the plurality of time intervals, wherein the weighted sum is associated with the recentity of the respective stress index, recovery index, and sleep recovery index, and wherein the stress resilience metric indicates the user's relative ability to cope with stress, recover from stress, or both. as well as A visual representation of the compressive toughness measure is displayed to the user via a graphical user interface (GUI) on the user equipment.

13. A non-transitory computer-readable medium storing code for determining a user's compressive resilience, the code including instructions executable by a processor to: Physiological data is acquired from a user via a wearable device at multiple time intervals, wherein each time interval includes an awake interval when the user is awake and a sleep interval when the user is asleep, and the physiological data includes at least heart rate variability (HRV) data. For the plurality of time intervals and based at least in part on the HRV data, determine the stress index and recovery index associated with the waking interval of the corresponding time interval and the sleep recovery index associated with the sleeping interval of the corresponding time interval; The user's stress resilience metric is determined at least in part based on a weighted sum of the stress index, the recovery index, and the sleep recovery index over the plurality of time intervals, wherein the weighted sum is associated with the recentity of the respective stress index, recovery index, and sleep recovery index, and wherein the stress resilience metric indicates the user's relative ability to cope with stress, recover from stress, or both. as well as A visual representation of the compressive toughness measure is displayed to the user via a graphical user interface (GUI) on the user equipment.

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