Fitness fatigue score determination and management techniques

Abstract

The present disclosure provides a fitness fatigue score for a user on that day. The fitness fatigue score may be determined based at least in part on first data obtained for a first time period, second data obtained for a second time period shorter than the first time period, and third data obtained for a third time period longer than the first time period. The first data includes heart rate measurements of the user over a first time period. The second data includes one or more sleep indicators of a plurality of sleep events of the user for a second time period. The third data comprises the heart rate variability of the user in the third time period. Additionally, recommendations may be generated regarding exercise of the day based on the fitness fatigue score.

Description

Related Applications

[0001] This application claims priority to U.S. Patent Application No. 17,891,428, filed on August 19, 2022, which is incorporated herein by reference in its entirety. Background Art

[0002] Recent technological advances—including those available through consumer devices—have provided corresponding advances in health detection and monitoring. For example, devices such as fitness trackers and smart watches are able to determine information related to the pulse and movement of the person wearing the device. However, due to the capabilities of conventional devices, there are technical problems associated with the amount and type of health information that can be determined using such devices because the collection of such information is limited. In particular, conventional devices are limited in being able to detect parameters that accurately and automatically indicate stress for a user.

[0003] Therefore, the present disclosure relates to a technical solution / benefit to the aforementioned technical problem. Therefore, the present disclosure relates to a system and method for calculating a stress score of a user of a wearable device. In a specific embodiment, for example, the stress score can be calculated using EDA data collected from an electrodermal activity (EDA) sensor while the user is wearing the wearable device. More specifically, the sympathetic nervous system can trigger micro-sweating throughout the human body, so the conductance between the EDA sensor on the wearable device and the user's hand or fingertip will increase as the sweat level increases. Therefore, when the user's palm or finger is placed adjacent to the EDA sensor, the EDA sensor can generate data that the wearable device can use to accurately and automatically calculate the user's stress. Summary of the invention

[0004] Aspects and advantages of the invention will be set forth in part in the following description, or may be obvious from the description, or may be learned through practice of the invention.

[0005] In one aspect, according to an embodiment of the present disclosure, a method for determining a user's fitness fatigue score is provided. The method includes obtaining first data of the user in a first time period. The first data includes multiple heart rate measurements of the user in the first time period. The method includes obtaining second data of the user in a second time portion having a shorter duration than the first time period. The second data includes one or more sleep indicators of multiple sleep events of the user in the second time period. The method includes obtaining third data of the user in a third time period having a longer duration than the first time period. The third data includes heart rate variability data of the user in the third time period. The method includes determining the user's fitness fatigue score for the day based at least in part on the first data, the second data, and the third data. The method includes causing a display screen of an electronic device to display the fitness fatigue score.

[0006] In some embodiments, the one or more sleep indicators may include a duration of each sleep event in the plurality of sleep events.Alternatively or additionally, the one or more sleep indicators may include a time of day at which each sleep event in the sleep events occurred.

[0007] In some embodiments, the first time period may include the 14 days immediately before the current day. Alternatively or additionally, the second time period may include the 7 days immediately before the current day. In some embodiments, the third time period may include the 30 days immediately before the current day.

[0008] In some embodiments, the method includes determining a recommendation about exercise for the day based at least in part on the determined fitness fatigue score for the day. The method includes causing a display screen of an electronic device to display a recommendation about exercise. In some embodiments, when the fitness fatigue score for the day is below a first threshold, the recommendation may be to have the user give up exercise for the day. In some implementations, when the fitness fatigue score for the day is greater than a first threshold and less than a second threshold, the recommendation may be to have the user participate in a first physical activity for the day. In some embodiments, when the fitness fatigue score for the day is greater than a second threshold, the recommendation may be to have the user participate in a second physical activity for the day. The second physical activity may be more intense than the first physical activity.

[0009] In some embodiments, determining the user's fitness fatigue score for the day includes determining a first sub-score based on the first data. Determining the user's fitness fatigue score further includes determining a second sub-score based on the second data, and determining the user's third sub-score based at least in part on the third data. In addition, determining the fitness fatigue score includes determining the user's fitness fatigue score for the day based at least in part on the first sub-score, the second sub-score, and the third sub-score. In some embodiments, the first sub-score is weighted more heavily than each of the second sub-score and the third sub-score.

[0010] In some embodiments, determining the fitness fatigue score includes determining that the first sub-score is less than a threshold value and determining the fitness fatigue score for the day to be the first sub-score.

[0011] In some embodiments, determining the first sub-score based on the first data includes determining a reserve value for each of a plurality of heart rate measurements included in the first data. Determining the first sub-score further includes determining that the reserve value for one or more of the heart rate measurements is greater than a threshold. Determining the first sub-score further includes determining the first sub-score based at least in part on the one or more heart rate measurements whose reserve value is greater than the threshold.

[0012] In some embodiments, determining the reserved value for each of the heart rate measurements includes determining a resting heart rate of the user based at least in part on the first data, and determining a maximum heart rate of the user based at least in part on an age of the user. Determining the reserved value further includes determining the reserved value for each of the plurality of heart rate measurements based at least in part on a comparison of each of the corresponding heart rate measurements to the resting heart rate of the user and the maximum heart rate of the user.

[0013] In some embodiments, the heart rate variability data includes heart rate variability measurements taken during each sleep event of a plurality of sleep events occurring within a third time period. In addition, determining a third sub-score based at least in part on the heart rate variability data includes determining a root mean square of consecutive differences of heart rate variability measurements associated with a first sleep event of the plurality of sleep events. The first sleep event may correspond to a sleep event that is most recent in time among the plurality of sleep events. The method includes determining a third sub-score based at least in part on a comparison of the heart rate variability measurement associated with the first sleep event with a heart rate variability measurement of at least one other sleep event of the plurality of sleep events. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Various embodiments according to the present disclosure will be described with reference to the accompanying drawings, in which:

[0015] Figure 1A and Figure 1B Example devices that may be used to obtain and analyze user health information according to various embodiments are shown.

[0016] Figure 2 An example set of devices capable of communicating in accordance with various embodiments is shown.

[0017] Figure 3 An example stress score algorithm that may be utilized in accordance with various embodiments is shown.

[0018] Figure 4 Example interfaces that may be provided in accordance with various embodiments are shown.

[0019] Figure 5 Example interfaces that may be provided in accordance with various embodiments are shown.

[0020] Figure 6 Example interfaces that may be provided in accordance with various embodiments are shown.

[0021] Figure 7 An example process for determining a stress score that may be utilized in accordance with various embodiments is shown.

[0022] Figure 8An example process for monitoring stress in a user that may be utilized in accordance with various embodiments is shown.

[0023] Fig. 9 An example environment is shown in which aspects of various embodiments may be implemented.

[0024] Fig.10 A flow chart of a method for determining a fitness fatigue score according to some embodiments of the present disclosure is shown.

[0025] Fig.11 A flow chart of a method for determining a first sub-score of a fitness fatigue score according to some embodiments of the present disclosure is depicted.

[0026] Fig.12 Depicted is a flow chart of a method for determining a third sub-score of a fitness fatigue score, according to some embodiments of the present disclosure.

[0027] Fig.13 A graphical user interface for displaying a fitness fatigue score on a display screen of an electronic device according to some embodiments of the present disclosure is depicted.

[0028] Fig.14 Depicted is a graphical user interface that displays a fitness fatigue score on a display screen of a wearable computing device in accordance with some embodiments of the present disclosure.

[0029] Fig.15 A graphical user interface depicting a fitness fatigue score on a display screen of a mobile computing device according to some embodiments of the present disclosure.

[0030] Fig.16 Depicted is a graphical user interface that displays exercise recommendations for a user based on the user's fitness fatigue score in accordance with some embodiments of the present disclosure.

[0031] Fig.17 Depicted is a graphical user interface displaying a user's fitness fatigue score over a period of time, according to some embodiments of the present disclosure. DETAILED DESCRIPTION

[0032] In the following description, various embodiments will be described. For the purpose of explanation, specific configurations and details will be set forth in order to provide a thorough understanding of the embodiments. However, it will be apparent to those skilled in the art that the embodiments may be practiced without the specific details. In addition, well-known features may be omitted or simplified in order not to obscure the described embodiments.

[0033] As computing devices become more and more popular and portable, the field of health monitoring and diagnosis shows many advantages. Computing devices, particularly computing devices that can be worn or carried by a user, can include one or more sensors to detect physiological information about the user and / or the environment around the user. This information can be used to observe, detect or diagnose various health conditions outside of traditional clinic or laboratory environments. For example, in the context of monitoring stress, a portable or wearable electronic device may be able to detect when the user's sympathetic nervous system triggers micro-sweating throughout the user's body. Additionally, the computing device may be able to record and interpret the detected information about the user and / or the environment to determine a health assessment. As in the previous example, the wearable electronic device may be able to record a relatively high frequency of skin conductance response (SCR), and generate an assessment that the user is experiencing stress, the SCR observing the number of spikes in skin conductance within a sliding time window.

[0034] Methods according to various embodiments provide for determining, predicting, and / or monitoring factors that may indicate stress or other such conditions of a user. In at least one embodiment, the user may be based at least in part on a user's experience of the user's stress or other such conditions. Figure 1A In various embodiments discussed herein, a person may wear or utilize a device 100 that is capable of automatically measuring or determining at least some aspects of the person's health or wellbeing. In particular, as Figure 1A , device 100 is a smartwatch 104, but other devices such as smart or connected fitness bands or trackers, watches, rings, earbuds, phones, clothing, etc. may also be utilized within the scope of various embodiments. In this example, a person may wear device 100 on arm 102 or wrist and may view content, which may include health information, on the device's display 106. In many embodiments, display 106 is a touch-sensitive display that allows a person to enter or annotate information about the person's health or status, as discussed elsewhere herein.

[0035] Reference now Figure 1B, the device 100 may include various measurement components 152, 154 (also referred to herein as sensors) as shown in the rear view 150 of the device 100. More specifically, as shown, the device 100 may include one or more internal sensors 152 and / or one or more external sensors 154, which may include any suitable type of sensor that can be used to measure or detect information about the user, such as an EDA sensor and / or a motion and temperature sensor. Further, in an embodiment, the measurement components 152, 154 may include or relate to an optical measurement subsystem. In this example, the optical measurement subsystem includes at least one optical transmitter and at least one optical receiver. The transmitter may emit one or more wavelengths of light that may be reflected from the surface of the wearer's skin, or diffusely reflected after propagating under the surface and detected by at least one of the transmitters. Such optical assemblies may enable the smart watch to measure various types of information during the time a person wears the device. In yet another embodiment, the external sensor 154 may include an EDA sensor that is easily accessible by the user's palm or finger, so that the user can easily contact the EDA sensor so that the sensor can generate data that can be used by the device 100 to accurately and automatically calculate the user's stress. Therefore, the present disclosure is closely related to the practical application of accurately and automatically calculating the user's stress through the EDA sensor 154.

[0036] Figure 2 An example environment 200 is shown in which aspects of various embodiments may be implemented. In this example, a person may have multiple different devices that can communicate using at least one wireless communication protocol. In this example, a user may have a smart watch 202 or a fitness tracker that the user wants to be able to communicate with a smart phone 204 and a tablet computer 206. The ability to communicate with multiple devices may enable the user to obtain information from the smart watch 202 using an application installed on the smart phone 204 or tablet computer 206, such as heart rate data captured using a sensor on the smart watch. The user may also want the smart watch 202 to be able to communicate with a service provider 208 or other such entity that can obtain and process data from the smart watch and provide functionality that may not otherwise be available on the smart watch or application installed on the individual device. The smart watch 202 may be able to communicate with the service provider 208 via at least one network 210 such as the Internet or a cellular network, or may communicate with one of the individual devices via a wireless connection such as Bluetooth®, which may then communicate via at least one network. In various embodiments, there may be a variety of other types or reasons for communication.

[0037] In addition to simply being able to communicate, users may also want devices to be able to communicate in a variety of ways or in certain aspects. For example, users may want communications between devices to be secure, especially when the data may include personal health data or other such communications. Devices or application providers may also need to protect this information in at least some cases. Users may want devices to be able to communicate with each other concurrently rather than sequentially. This may be especially true in the case of pairing, because users may prefer that each device be paired at most once or do not require manual pairing. Users may also expect that communication is as standard-based as possible, so that not only little manual intervention on the part of the user is required, but also that the device can communicate with as many other types of devices as possible, which is usually not possible with various proprietary formats. Therefore, users may expect to be able to carry a device around the room and have such a device automatically communicate with another target device with little effort on the part of the user. In various conventional methods, a device will use a communication technology such as Wi-Fi to communicate with other devices using a wireless local area network (WLAN). Smaller or lower capacity devices such as many Internet of Things (IoT) devices instead use communication technologies such as Bluetooth®, especially low-power Bluetooth (BLE) with very low power consumption.

[0038] In a further embodiment, Figure 2 The environment 200 shown in enables data to be captured, processed, and displayed in a variety of different ways. For example, data may be captured using sensors on a smartwatch 202, but due to limited resources on the smartwatch, the data may be transmitted to a smartphone 204 or a service provider 208 (or cloud resources) for processing, and the results of the processing may then be presented back to the user on the smartwatch 202, smartphone 204, or another such device associated with the user, such as a tablet computer 206. In at least some embodiments, the user may also be able to use interfaces on any of these devices to provide input, such as health data, which may then be considered when making the determination.

[0039] In at least one embodiment, data determined for a user may be used to determine status information, such as may relate to the user's current stress level or status. At least some of the data may be determined using sensors or components capable of measuring or detecting aspects of the user, while other data may be manually input by the user or otherwise obtained. In at least one embodiment, a stress determination algorithm may be utilized that takes as input a plurality of different inputs, where the different inputs may be obtained manually, automatically, or otherwise. In at least one embodiment, such an algorithm may take various types of factors and use them to generate a stress score. Such a score may be used, for example, to determine the stress level or status of the user. Figure 3Such a stress score is calculated as shown in method 300 of . In this example, the stress score is calculated as a weighted sum of different types of factors. In at least one embodiment, these types of factors may include sleep characteristics (e.g., restlessness, fragmentation), activity characteristics (e.g., relative AZM, relative steps), and / or cardiac characteristics (e.g., HF / LF HRV, elevated resting heart rate) and inputs such as electrodermal activity (EDA), where EDA can be used to measure electrodermal response. In at least one embodiment, these factors can be normalized and then weighted by testing, machine learning, or other such methods.

[0040] In at least one embodiment, these factors may consist of at least twelve different indicators. However, it should be understood that different numbers, selections, types or variations may be used with different algorithms according to various embodiments. The first feature type associated with sleep features may include features such as the following. A sleep score restlessness value may provide a measure of the amount of movement during sleep, which sleep score restlessness value may be normalized for a person over a period such as 30 days. Restless sleep is a well-known physiological stress marker. A fragmentation feature may indicate the number of times a person has been awake for more than a threshold amount of time such as 30 minutes (WASO), which may be normalized to an average over a period such as 30 days. Fragmented sleep is also known to be a physiological stress marker. A sleep reservoir level indicates how well a person slept in, for example, the past week, where the farther away the day is, the less impact it has on the score, and the sleep reservoir level is normalized relative to the group level. This indicator analyzes the user's sleep over the past week to take into account the fact that multiple nights of poor sleep are not always recoverable by one night of sleep. Overall sleep duration can also lead to poor mood regulation, and thus, situations that may not be stressful after a good night's sleep may become stressful. In at least one embodiment, a set of constants may be determined and / or utilized to calculate the rate at which a user's sleep savings level is reduced. The sleep savings level may represent the cumulative amount of restful sleep that the user has had over a recent period of time, such as the last seven days of sleep. If the user has had somewhat fitful sleep, very light sleep, or very short sleep, the savings level will decrease, approaching zero, but at a different rate. If the user is very well rested, having had at least seven hours of sleep each of the last seven days, the value may be closer to a maximum normalized value of one (1). Such methods may map sleep calculations into a normalized sleep savings table using specific constants that may determine the rate at which a user is reducing his or her sleep, and how particular sleep periods may be accumulated in terms of sleep. In one embodiment, the first sleep period is not counted as restful sleep, and only sleep beyond this initial period is determined to be counted as restful sleep. After this initial period, it may be determined that the user is accumulating sleep. The sleep state or type after this initial period may then determine the amount or rate at which restfulness is being restored.

[0041] The deep and / or REM sleep duration may indicate, for example, the number of minutes of deep sleep and the percentage of REM (rapid eye movement) sleep in the most recent night, normalized using a population feature group average over a period such as the past 30 days. The deep sleep latency feature may indicate the number of minutes before the first entry into deep sleep after falling asleep, normalized using, for example, an individual average over the past 30 days. Longer latencies between the onset of sleep and the first deep sleep period are known to be physiological stress markers.

[0042] In at least one embodiment, there may be a plurality of different activity features. This may include an activity zone minutes (AZM) feature, which includes the use of a weekly activity zone minutes normalized relative to, for example, a recommended population level of 150 / week. When the AZM is above or below a range of about 150, the feature value may increase. In at least some embodiments, sensors such as motion and heart rate sensors on the device may be used to determine these minutes. Too little activity per week may lead to increased cortisol levels and make it easier for people to perceive events as stressors because mild to moderate exercise acts as a "barrier" against stress. Too much activity per week may lead to fatigue because exercise is a stressor by definition. A stressor represents a stimulus or event that causes anxiety or a state of tension, and thus results in a stress response. A stress response or symptom represents the physiological and psychological changes that the body experiences in response to a stressor in an attempt to maintain homeostasis. A single stressor may trigger a variety of stress responses. Psychological stress response represents the relationship between a person and the environment, which is evaluated by a person as burdening or exceeding his or her resources and endangering his or her physical and mental health. Another feature can be a step count value, which corresponds to the total number of steps in a period such as every day. This can be normalized using a population feature group mean value in a period such as 30 days. Too few steps per day can lead to increased cortisol levels, and make people more likely to perceive events as stressors, because mild to moderate exercise serves as a "barrier" against stress. Too many steps per day can lead to fatigue, because exercise is a stressor by definition. Other factors indicating activity may also be present, wherein different types of data are analyzed to determine different types or amounts of activity.

[0043] In at least one embodiment, various cardiac features may be included in the stress determination. One cardiac feature is the deep sleep high frequency (HF) to low frequency (LF) heart rate value (HRV). In at least one embodiment, this captures the sympathovagal balance between sympathetic / parasympathetic activity and can be normalized by a population feature group average over a period of time such as 30 days. When deep and REM sleep deprivation occurs, emotional regulation becomes more difficult, and therefore, situations that may not cause stress after a good night's sleep may become stressful. Heart rate variability (HRV) quantifies the temporal variability between heartbeats. There are many different HRV indicators, and low frequency (LF) to high frequency (HF) power quantifies the sympathovagal balance, where larger numbers mean more SNS activity and lower numbers mean more PNS activity. This indicator only considers LF / HR HRV during the deep sleep period, when the SNS is highest during the day because this is the most restful sleep. Another feature may involve an elevated resting heart rate (HR), which may correspond to the amount and magnitude of the resting HR above a threshold, which may be normalized using an individual average over a period of time, such as 30 days. An elevated heart rate at rest during the day is a sign of increased sympathovagal balance, meaning that the sympathetic nervous system (SNS) is overactive and the parasympathetic nervous system (PNS) is underactive. While stress leads to increased sympathovagal balance, many other factors may also cause an increased heart rate, including anemia, caffeine, alcohol, fever, high or low blood pressure, electrolyte imbalances (possibly due to dehydration), hyperthyroidism, smoking, or medications.

[0044] A sleeping heart rate above resting heart rate (RHR) can indicate the percentage of time during sleep that the user's HR is above resting HR, which can be normalized using an absolute metric as a raw percentage. Similar to elevated resting HR, this is also a measure of sympathovagal balance, but quantifies HR during sleep rather than during a day. While many factors can make this metric high, drinking alcohol before bed can make it high in particular. A fitness fatigue score can be calculated that balances the fatigue effects of exercise with the fitness effects of exercise into a single score that can be normalized using a personal minimum / maximum range over a period of time such as 30 to 90 days. This score measures the dual contribution of exercise to a person's long-term fitness level as well as short-term fatigue, which is measured by heart rate. Similar to weekly activity and daily steps, high fatigue levels are by definition a stress that requires rest. Fitness fatigue can treat the human heart as a linear system. By measuring heart rate over a period of time, a cumulative representation of the amount of fatigue on the human body can be obtained, which is quantified by a fitness fatigue score. When a user experiences a high heart rate event such as exercise, there will be a time when this score will drop, reflecting the fatigue effect of the exercise. After this period, there will be a period where the score will go up to reflect the fact that the body has become fitter and more resilient to exercise and is better prepared for future exercise events.

[0045] In at least one embodiment, EDA data may also be considered as a stress determining factor as discussed above. A method for capturing or determining EDA data is described in a co-pending application entitled DETECTION AND RESPONSE TO AROUSAL ACTIVATIONS, which is incorporated herein in its entirety for all purposes. Sympathetic nervous system activity causes sweating, and EDA measures the amount of electrical conductance across the user's skin to quantify the current amount of SNS activity. In at least some embodiments, unlike other heart-related indicators, this indicator does not measure PNS, but only measures SNS activity, and therefore, it may not provide a true measure of sympathetic vagal balance. Feature values ​​may relate to EDA activity during meditation and self-checking (check-in) sessions. For example, if a user has used an EDA session to log a meditation or "self-checking" session, their average EDA score across all sessions can be compared with their baseline value, and the feature can be scored, for example, 0, 1, or 2 points. If no session is logged, they can get 1 point. In one embodiment, users who do not log EDA for the day receive 1 point, users who do a guided EDA session receive 2 points (regardless of alignment), and users who do an unguided EDA session use the average of active EDA sessions relative to the past 30 days of sessions. If the sessions are above the average, those users receive 0 points. If the sessions are around the average, the user receives 1 point. If the sessions are below the average, the user receives 2 points.

[0046] In at least one embodiment, the weight of EDA in the stress score calculation can vary, which can depend in part on factors such as the accuracy of the EDA value or the relative importance of the data to individual users. In at least one embodiment, EDA can be used as a proxy to quantify the amount of sympathetic nervous system activity. The data can represent that the activity occurs instantly. The sympathetic nervous system can trigger micro-sweating throughout the human body, so the conductance between the sensor and the user's hand or fingertip will increase with the increase in sweating level. When a greater conductance is detected, this can represent a higher sympathetic nervous system activity. In at least one embodiment, at least two different EDA indicators can be monitored. The first indicator is a tonal indicator called skin conductance level, which observes the absolute level over time and whether the value increases or decreases. The second indicator represents the skin conductance response (SCR), which observes the number of spikes in skin conductance within a sliding time window such as a window with a duration of one minute. Therefore, SCR can represent the count per minute of spikes in the EDA conductance level. In at least one embodiment, continuous EDA determinations may be made for such purposes, whereas discrete or periodic EDA signals may not support determinations of such granularity.

[0047] In at least one embodiment, the algorithm can utilize at least some of these features and / or other such features to generate a stress score representing the user's current, past, or future stress state. A system or service utilizing such a score can provide real-time indicators that can be related to physical stress and / or perceived stress. Such a system can provide manual input from the user, such as data collection related to physical stress or perceived stress. In at least one embodiment, such a system can capture the results of a Perceived Stress Scale (PSS) survey that measures chronic stress of widely distributed users. Such a system can also generate daily physiological stress and / or stress tolerance (stress capacity) indicators. In at least one embodiment, an algorithm can calculate a stress score determined by "expert guidance" representing physiological stress, which can be reversed to reflect stress tolerance and / or resilience. In at least one embodiment, a low stress tolerance or resilience score can indicate a high level of stress, while a high resilience score can indicate readiness for cognitive / emotional / physical demands of the day.

[0048] Figure 4 An example display or interface that can be provided to a user according to at least one embodiment is shown. In this example, a first interface 400 provides information including a stress score field, and an option to obtain additional stress data. A second interface 410 provides a set of stress scores drawn over time, in this example, a set of stress scores within a certain week. Such information can help users identify trends, and helps to associate events of different days with different stress levels or scores. As shown, users can provide feedback for different levels of stress, and the feedback provided can also be drawn by day for comparison. A third interface 420 can provide other information within the week or a period of time, such as mood. In at least one embodiment, various sub-scores or components of stress scores can be present, which can relate to reaction power, consumption balance or sleep patterns or scores. A fourth interface 430 can enable users to provide feedback about their current mood or state.

[0049] Figure 5Additional interfaces that may be provided to a user according to another embodiment are shown. In this example, each interface presents information related to stress resilience, rather than a stress score that may be interpreted by the user as an absolute level of stress. A first interface 500 may present a stress resilience score, as well as the last stress state presented (and the time when that state was presented). A second interface 510 may be presented to a user who has not yet begun entering or obtaining stress resilience data, such as from whom at least some data or permission may be required prior to presentation. A third interface 520 provides a different view of stress resilience over time, including data for individual stress resilience components as discussed previously. Such interfaces may also allow a user to provide new, additional, or updated feedback that may be used in determining such values ​​for presentation.

[0050] Figure 6 Additional interfaces that may be provided to a user according to yet another embodiment are shown. A first interface page 600 presents another view of stress resilience data over time, specifically presenting a distribution of stress levels or states on different days, rather than presenting resilience values. A second interface 610 presents information that helps the user understand these scores, what these scores encompass, and what different scores represent. Various other data and interfaces may also be used according to various embodiments.

[0051] In at least one embodiment, data can be decomposed into three stress score groups, i.e., consumption group, heart group, and sleep mode group. In such embodiments, the consumption group may include data of weekly activity, daily steps, and fitness fatigue score or consumption balance. The heart group may include elevated resting HR, sleep HR higher than RHR, deep sleep HRV, and potential EDA. The data may represent the user's nervous system, reaction force, neural stimulation, neural measurement, and stimulation. The sleep mode group may include data on sleep restlessness, sleep fragmentation, deep sleep latency, depth and REM sleep duration, and sleep savings level.

[0052] In at least one embodiment, the stress score may have a value ranging from 0 to 100, where a value of 100 may mean that the user's body has shown a lot of signs of stress. Alternatively, a stress resilience score of 100 may mean that the user shows little or no signs of stress. Other values ​​or indicators may also be used.

[0053] Such interfaces and information may attempt to provide an overall management tool. Therefore, such methods may analyze both physical stress and mental stress. An interface may be provided that quickly enables users to obtain their daily or current stress scores and other scores such as sleep scores. Such information may also help users reflect their moods and remind users to log or provide this information. As described above, users may also access additional data such as a stress details page, in which users may obtain additional information about the current stress state or score, as well as various components for the determination. In at least some embodiments, users may be able to drill down into data components to obtain additional information of interest. In some embodiments, there may also be multiple available views, such as when users want to view trends for a day, a week, or a month, etc. As shown in various embodiments, sensors and devices may be able to provide data about physical stress, but users may be the best source of mental stress data, at least for certain types of mental stress data. Users may be able to log current moods or perceived stress levels, or provide other such information. In at least one embodiment, users may be prompted to enter mood or stress data after an EDA scan to provide improved relevance. Such methods may help incorporate EDA data into a holistic view of a user's physical and mental health.In some embodiments, a device may provide periodic or continuous EDA measurements that may be used to dynamically update stress determinations over time.

[0054] In at least one embodiment, the stress score determination algorithm considers various factors that may affect the user's stress level. The weights of these factors can be determined across users or different types of users. In at least some embodiments, these weights can also be adapted for individual users to improve accuracy. For example, a sleep stage algorithm can be used to distinguish between sleep periods, which can have a significant impact on the stress level of some users. Further, observing factors such as HRV in a specific sleep state compared to HRV throughout the night can provide additional insights. For example, combined deep and REM sleep duration can provide valuable insights because these periods are generally more conducive to recovery. There may be a significant difference between getting eight hours of light sleep and getting four hours of light sleep and four hours of deep and REM sleep. Further, mental stress factors may affect different users more seriously than physical stress factors, and such algorithms can take both into account. Additional benefits can be obtained by observing different types of indicators across different areas of exercise or consumption, sleep, and alternative indicators of autonomic nervous system activity with both heart rate / heart rate variability and EDA or other such data. A useful benefit for users can also be to compare physical stress levels with perceived stress levels, so users can better understand their stress.

[0055] In at least some embodiments, stress determination can be a linear or nonlinear combination of weighted and normalized factors, where those factors can be normalized based on individuals, group characteristics, or other such groupings. In some embodiments, some factors may need to be reversed, where some factors represent higher stress levels and other factors represent lower stress levels. For example, in one embodiment, the reversed factors include deep and REM sleep duration, sleep savings level, and fitness fatigue scores. Some factors can utilize a z-score-based approach, where a z-score of 1 represents one standard deviation above the mean, etc. As described above, factors can also be normalized over different time periods and different groupings (such as each user or each group characteristic group). In some embodiments, a range function can be applied after the z-score to place all values ​​within a determined range, such as between 0 and 1. In some embodiments, this can involve linear correction and interpolation.

[0056] In at least one embodiment, the stress score may be determined such that a single score, such as 80 out of 100, represents the same stress level or stress resilience for all users. In other embodiments, the values ​​may mean different things to different users, such as where a first user may have an average stress level of 50, while another user may have an average stress level of 80, such that a value of 70 may mean different things to those two users.

[0057] Further, different factors may be considered in different algorithms or for different users. For example, very active users may utilize different algorithms from inactive users, and different algorithms may also be utilized for different devices or users where different types of input are available, where they may have the option of disabling certain sensors or limiting the collection of certain types of data. In one embodiment, at least three types of factors may be collected, including sleep characteristics, activity characteristics, and cardiac characteristics (or "responsiveness" characteristics). In some embodiments, EDA or similar data may also be utilized. In some embodiments, the factors within these categories that may be utilized may include restlessness, fragmentation, sleep savings level, deep / REM sleep duration, deep sleep latency, activity zone minutes or activity level, step count or movement, deep sleep HRV, elevated resting HR, sleep HR higher than RHR, and fitness fatigue scores. Other factors may include various exercise or activity indicators, and consumption balance indicators. If available, additional factors may include blood pressure, blood composition, respiratory rate, temperature, metabolic data, blood sugar level, weight or body composition, psychological state, perceived stress, depression, activity type, or current movement mode (e.g., gait). Other factors may include nightmares, vocal prosody / pitch / stress, blood cortisol / adrenaline / norepinephrine levels, low-density lipoprotein (LDL) levels, BMI x exercise, gender-specific values, or mood diary data.

[0058] Reference now Figure 7, a flowchart of one embodiment of an example process 700 for determining a stress score for a user that can be utilized is shown. It should be understood that, unless otherwise specifically stated, within the scope of various embodiments, there may be additional, fewer, or alternative steps in the processes discussed herein that are performed in a similar or alternative order or at least partially in parallel. In this embodiment, as shown at (702), the process 700 includes activating a stress determination for a user associated with a wearable device. The activation may come from the user, the wearable device, or an associated device, as well as other such sources. As part of the determination, as shown at (704), the process 700 includes receiving data related to the state of the user from one or more sensors or components on the wearable device, such as may relate to heart rate, sleep state, EDA, activity, or other such information discussed and suggested herein. As shown at (706), the process 700 includes obtaining additional data provided by the user, such as may relate to the user's sensory state data. As shown at (708), using at least some of this data and other related data, the process 700 includes calculating a stress score (or stress resilience score) for the user. The stress score or value may relate to various stress indicators, such as the user's current stress level or stress resilience level. In this example, as shown at (710), process 700 includes performing at least one action on the wearable device (or associated device) based at least in part on the calculated score. These actions may include any of a variety of different actions, which may involve generating an interface, presenting data, providing a notification, updating or modifying the operation of the wearable device, or performing another such action.

[0059] Figure 8 Another example process 800 for monitoring stress of a user that may be utilized according to various embodiments is shown. In this example, as shown at (802), process 800 includes activating stress monitoring of a user associated with a wearable device. An initial stress determination may be performed, such as regarding Figure 7As described. During the monitoring, as shown at (804), process 800 includes receiving new or updated sensor data and / or user-provided data. As shown at (806), process 800 includes calculating an updated stress score based at least in part on the new or updated data. As shown at (808), process 800 includes analyzing changes in the stress score, such as determining with respect to an immediately preceding value. As shown at (810), process 800 includes determining whether the change is an actionable change, such as a change that exceeds a change threshold, falls outside an acceptable range, or satisfies an action threshold. If not, process 800 can continue to receive new and updated data. If the change is determined to be an actionable change, at least one action corresponding to the change can be performed, as shown at (812), wherein those actions can include the actions discussed above with respect to the initial stress score calculation.

[0060] Fig. 9 Components of an example system 900 that can be utilized according to various embodiments are shown. In this example, the system 900 includes at least one processor 902, such as a central processing unit (CPU) or a graphics processing unit (GPU), for executing instructions that can be stored in a memory device 904, such as a flash memory or DRAM, and other such options. As will be apparent to those of ordinary skill in the art, the device may include many types of memory, data storage, or computer-readable media, such as data storage for program instructions executed by the processor. The same or separate storage may be used for images or data, removable memory may be used to share information with other devices, and any number of communication methods may be used to share with other devices. In addition, as shown, the system 900 includes any suitable display 906, such as a touch screen, an organic light emitting diode (OLED), or a liquid crystal display (LCD), but the device may convey information via other means, such as by an audio speaker or a projector.

[0061] The tracker or similar device includes at least one motion detection sensor, which may include at least one input / output (I / O) element 910 of the device as shown. Such sensors can determine and / or detect the orientation and / or movement of the system 900. Such elements may include, for example, accelerometers, inertial sensors, altimeters, or gyroscopes, which are operable to detect the movement of the device (e.g., rotational movement, angular displacement, tilt, positioning, orientation, movement along a non-linear path, etc.). The orientation determination element may also include an electronic or digital compass, which may indicate the direction (e.g., north or south) in which the device is determined to be pointing (e.g., relative to the main axis or other such aspects). The I / O element 910 may also be used to determine the location of the device (or the user of the device). Such positioning elements may include GPS or similar position determination elements that are operable to determine the relative coordinates of the positioning of the device. The positioning element may include a wireless access point, base station, etc. that can broadcast location information or implement triangulation of signals to determine the location of the device. Other positioning elements may include QR codes, barcodes, RFID tags, NFC tags, etc. that enable the device to detect and receive location information, or identifiers that enable the device to obtain location information (e.g., by mapping the identifier to a corresponding location). Various embodiments may include one or more such elements in any appropriate combination. I / O element 910 may also include one or more biometric sensors, optical sensors, air pressure sensors (e.g., altimeters, etc.), etc.

[0062] As mentioned above, some embodiments use elements to track the position and / or movement of the user. After determining the initial positioning of the device (e.g., using GPS), the device of some embodiments can track the position of the device by using elements or in some cases by using orientation determination elements or combinations thereof as mentioned above. It should be understood that the algorithm or mechanism for determining positioning and / or orientation can depend at least in part on the selection of elements available to the device. The example device also includes one or more wireless components 912, which are operable to communicate with one or more electronic devices within the communication range of a specific wireless channel. The wireless channel can be any appropriate channel for enabling the device to perform wireless communication, such as Bluetooth, cellular, NFC or Wi-Fi channels. It should be understood that the system 900 can have one or more conventional wired communication connections known in the art. The system 900 also includes one or more power supply components 908, such as batteries that can be operable to recharge by conventional plug-in methods or by other methods, such as capacitive charging by approaching a power pad or other such devices. In some embodiments, the system 900 may include at least one additional I / O device 910 capable of receiving conventional input from the user. The conventional input may include, for example, a button, a touch pad, a touch screen, a roller, a joystick, a keyboard, a mouse, a keypad, or any other such device or element, whereby a user may input commands to the device. In some embodiments, these I / O devices may also be connected via wireless infrared or Bluetooth or other links. Some devices may also include a microphone or other audio capture element that accepts voice or other audio commands. For example, a device may not include any buttons at all, but may be controlled only by a combination of visual and audio commands so that a user may control the device without having to contact the device.

[0063] As described above, many embodiments include at least some combination of one or more emitters 916 and one or more detectors 918 for measuring data of one or more indicators of a human body (such as a person wearing the tracker device). In some embodiments, this may involve at least one imaging element, such as one or more cameras capable of capturing images of the surrounding environment and capable of imaging a user, person, or object near the device. The image capture element may include any appropriate technology, such as a CCD image capture element with sufficient resolution, focal range, and viewing area to capture an image of the user while the user is operating the device. Methods of capturing images using camera elements with computing devices are well known in the art and will not be discussed in detail herein. It should be understood that image capture may be performed using a single image, multiple images, periodic imaging, continuous image capture, image streaming, etc. Further, the device may include the ability to start and / or stop image capture, such as when a command from a user, application, or other device is received.

[0064] In one example, Fig. 9 The emitter 916 and detector 918 may also be capable of obtaining optical photoplethysmography (PPG) measurements. Some PPG techniques rely on detecting light at a single spatial location, or adding signals acquired from two or more spatial locations. Both approaches produce a single spatial measurement from which a heart rate (HR) estimate (or other physiological indicator) can be determined. In some embodiments, the PPG device employs a single light source (i.e., a single light path) coupled to a single detector. Alternatively, the PPG device may employ multiple light sources (i.e., two or more light paths) coupled to a single detector or multiple detectors. In other embodiments, the PPG device employs multiple detectors (i.e., two or more light paths) coupled to a single light source or multiple light sources. In some cases, the light source may be configured to emit one or more of green light, red light, and / or infrared light. For example, the PPG device may employ a single light source and two or more light detectors, each light detector being configured to detect a specific wavelength or wavelength range. In some cases, each detector is configured to detect a wavelength or wavelength range that is different from one another. In other cases, two or more detectors are configured to detect the same wavelength or wavelength range. In yet another case, one or more detectors are configured to detect a specific wavelength or wavelength range that is different from one or more other detectors. In embodiments employing multiple light paths, the PPG device may determine an average of the signals generated from the multiple light paths before determining an HR estimate or other physiological indicator. Such a PPG device may not be able to resolve the individual light paths or to separately utilize the individual signals generated by the multiple light paths.

[0065] Still refer to Fig. 9 , the system 900 may further include one or more processors 902, which are coupled to a memory device 904, a display 906, a bus, one or more input / output (I / O) elements 910, and a wireless network component 912, as well as other such options. However, in some embodiments, the display and / or I / O device may be omitted. In an embodiment, the system 900 may be part of a wristband, and the display 906 is configured so that the display faces away from the outside of the user's wrist when the user wears the wristband. In other embodiments, the display may be omitted, and the data detected by the system 900 may be transmitted to a host computer 922 via near field communication (NFC), Bluetooth, Wi-Fi, or other suitable wireless communication protocols through at least one network 920 using a wireless networking interface for analysis, display, reporting, or other such purposes.

[0066] The memory 904 may include RAM, ROM, FLASH memory or other non-transitory digital data storage, and may include a control program that includes a sequence of instructions that, when loaded from the memory and executed using the processor 902, causes the processor 902 to perform the functions described herein. The transmitter 916 and the detector 918 may be coupled to the bus directly or indirectly using a driver circuit system, through which the processor 902 can drive the transmitter 916 and obtain a signal from the detector 918. The host computer 922 may communicate with the wireless networking component 912 via one or more networks 920, which may include one or more local area networks, wide area networks, and / or an internet network using any terrestrial or satellite links. In some embodiments, the host computer 922 executes a control program and / or application configured to perform some of the functions described herein.

[0067] In some embodiments, each emitter 916 may be individually controlled, or each detector 918 may be individually read out when multiple detectors are used, and in such embodiments, PPG sensor data along several different light paths may be collected. The control program may utilize the collected data to provide a more accurate estimate or HR and / or other physiological indicators. In related aspects, the processor 902 and other components of the PPG device may be implemented as a system on a chip (SoC), which may include one or more central processing unit (CPU) cores using one or more reduced instruction set computing (RISC) instruction sets, and / or other software and hardware for supporting the PPG device.

[0068] In various embodiments, the emitter 916 (or light source) may include an electronic semiconductor light source such as an LED, or use any of a filament, a phosphor, or a laser to generate light. In some implementations, each of the light sources emits light having the same center wavelength or within the same wavelength range. In other cases, at least one light source may emit light having a center wavelength different from another light source in the light source. The center wavelength of the light emitted by the light source may be in the range of 495 nm to 570 nm. For example, a specific green light source may emit light having a center wavelength of 528 nm. In other embodiments, one or more of the light sources may emit red light (e.g., a 660 nm center wavelength) or IR light (e.g., a 940 nm center wavelength). In some embodiments, one or more of the light sources may emit light having a peak wavelength typically in the range of 650 nm to 940 nm. For example, in various embodiments, a specific red light source may emit light having a peak wavelength of 660 nm, and one or more infrared light sources may emit light having a peak wavelength in the range of 750 nm to 1700 nm. As an example and not limitation, a specific infrared light source can emit light with a peak wavelength of 730 nm, 760 nm, 850 nm, 870 nm or 940 nm. In some cases, commercial light sources such as LEDs can provide output at intervals of about 20 nm, wherein the center wavelength tolerance is + / -10 nm of the manufacturer's specified wavelength, and therefore a possible range of the useful peak wavelength of the light source is 650 nm to 950 nm. A green light source can be configured to emit light with a wavelength within the range of 495 nm to 570 nm. For example, a specific green light source can emit light with a wavelength of 528 nm. The green light source can be spaced equidistantly from the photodetector as a paired red and infrared light source. For example, if the distance between the center of the photodetector and the first red light source is 2 mm, the distance between the photodetector and the green light source can also be 2 mm (e.g., equidistant). In some other cases, the distance between the photodetector and one or more light sources is not equidistant. Further, in some embodiments, one or more of the light sources may include a single LED package that emits multiple wavelengths, such as green, red, and infrared wavelengths, at the same or substantially the same (e.g., less than 1 mm difference) positions relative to the multiple detectors. Such an LED may include multiple semiconductor elements co-located in a single package using a single die.

[0069] The spacing of the light sources can be measured from the side of the light source or the center of the light source. For example, the light source can be configured so that the center of each light source is at a first distance from the edge of the nearest light detector in the light detector. In some embodiments, the first distance can be 2 mm. In some implementations, each light source is located at a second distance from the nearest light source in the light source, and each light detector is located at a third distance from the nearest light detector in the light detector. In some embodiments, the second distance and the third distance are the same as the first distance. In other embodiments, each of the second distance and the third distance is different from the first distance. The second distance may be the same or different from the third distance. The specific amplitude of the spacing may depend on multiple factors, and the present disclosure does not limit the embodiments to any specific spacing. For example, in various embodiments, a spacing in the range of 1 mm (or less) to 10 mm will be feasible.

[0070] In some embodiments, independent control of all light sources is provided. In other embodiments, several light sources are controlled together as a group or a row. As further discussed herein, the benefit of independent control of each light source or independent readout from each of a plurality of detectors (e.g., obtaining independent signals based on the same or different wavelengths of light from each of a plurality of detectors) is that a multi-light path approach can be used to improve the estimation of HR and / or other physiological indices.

[0071] The light detector may include one or more sensors adapted to detect the wavelength of light emitted from the light source. The specific light source combined with the specific detector may include a sensor such as a PPG sensor. The first PPG sensor and the second PPG sensor may share components such as the same light source and / or detector, or have different components, and thus the term "PPG sensor" may refer to any such arrangement in addition to its ordinary meaning, but the actual embodiment may use multiple components when implementing the PPG sensor. In addition to having its ordinary meaning, the term "PPG device" may also refer to a device including a PPG sensor. In an embodiment, the light detector may include one or more detectors for detecting each different wavelength of light used by the light source. For example, the first detector may be configured to detect light having a wavelength of 560 nm, the second detector may be configured to detect light having a wavelength of 940 nm, and the third detector may be configured to detect light having a wavelength of 528 nm. Examples include photodiodes made of semiconductor materials and having optical filters that only allow light of a specific wavelength or wavelength range. The light detector may include any one of a photodiode, a phototransistor, a charge coupled device (CCD), a thermopile detector, a micro-bolometer, or a complementary metal oxide semiconductor (CMOS) sensor. As further described herein, the light detector may include a plurality of detector elements.One or more of the detectors may include a bandpass filter circuit.

[0072] In other embodiments, the detector may include one or more detectors configured to detect multiple wavelengths of light. For example, a single detector may be configured to tune to different frequencies based on data received from an electronic digital microprocessor coupled to the detector. In another manner, a single detector may include multiple active areas, each of which is sensitive to a given range of wavelengths. In an embodiment, a single detector is configured to detect light having wavelengths in the red and IR frequencies, and a second detector is configured to detect light having wavelengths in the green frequency. Further, each of the light sources may use any of the one or more different wavelengths of light as described above.

[0073] In an embodiment, the photodetector may be mounted in a housing having one or more filters configured to filter out light of wavelengths other than the wavelength emitted by the light source. For example, a portion of the housing may be covered with a filter that removes ambient light other than the wavelength emitted by the light source. For example, a signal from the light source may be received at the photodetector through an ambient light filter that filters out ambient light sources that generate ambient light having a wavelength different from the wavelength detected by the detector. Although LEDs and photodiodes are used as examples of light sources and photodetectors, respectively, the technology described herein may be extended to other types of light sources. For example, edge emitting lasers, surface emitting lasers, LED-pumped phosphors that generate broadband light. The technology described herein may be extended to other combinations of light sources and detectors. For example, a PPG device may include (i) a single or multiple LEDs and a multi-element photodetector (e.g., a camera sensor), (ii) an LED array and a single or multiple photodiodes, (iii) a broadband LED-pumped phosphor and a detector array with a wavelength selective filter on each detector, (iv) a spatial light modulator (SLM) (e.g., a digital micromirror device [DMD] or a liquid crystal on silicon [LCoS] device) and a single or multiple LEDs, other combinations thereof, or other configurations of light sources and detectors.

[0074] Certain flow charts are presented herein to illustrate various methods that may be performed by example embodiments. The flow charts illustrate example algorithms that may be programmed using any suitable programming environment or language to create machine code that can be executed by a CPU or microcontroller of a PPG device. In other words, the flow charts, together with the written descriptions in this document, are a disclosure of algorithms for various aspects of the claimed subject matter presented at the same level of detail that is typically used to convey the subject matter among those skilled in the art to which the present disclosure pertains. Various embodiments may be coded using assembly, C, Objective-C, C++, JAVA, or other human-readable languages, and then compiled, assembled, or otherwise converted into machine code that may be loaded into a ROM, EPROM, or other recordable memory of an activity monitoring device coupled to a CPU or microcontroller, and then executed by the CPU or microcontroller.

[0075] In an embodiment, a computer program may be used to process the signals obtained from the multiple optical paths to filter or reject signal components associated with the user's motion, to identify the motion component of the signal and to remove the identified motion component from the composite signal, leaving the cardiac component as a remaining or final signal.

[0076] In embodiments, signals may be collected during a variety of activities during the day or night, such as periods that may involve walking, exercising, or sleeping. Other on-device sensors including accelerometers, gyroscopes, or altimeters may be used to classify or detect activity or human posture as a basis for developing appropriate filters. These filters or signal processing methods may be used to specifically reduce variability in PPG data with multiple light paths. As an example and not limitation, accelerometer data may be used to develop signal processing methods to filter the data and focus on specific postures, thereby removing other body orientations. This can help reduce noise in the data and better assess corresponding physiological variables for corresponding light paths.

[0077] In various embodiments, the methods discussed herein may be performed by one or more of: firmware running on a monitoring or tracker device or an auxiliary device (such as a mobile device paired with the monitoring device), a server, a host computer, etc. For example, the monitoring device may perform operations related to generating a signal that is uploaded or otherwise communicated to a server that performs operations for removing motion components and creating final estimates of HR, SpO2, and / or other physiological indicators. Alternatively, the monitoring device may perform operations related to generating a monitoring signal and removing motion components to produce final estimates of HR, SpO2, and / or other physiological indicators local to the monitoring device. In this case, the final estimate may be uploaded or otherwise communicated to a server, such as a host computer, that performs other operations using the value.

[0078] An example monitoring or tracker device can collect one or more types of physiological and / or environmental data from one or more sensors and / or external devices, and communicate or relay such information to other devices (e.g., a host computer or another server), thereby permitting, for example, viewing the collected data using a web browser or a web-based application. For example, when worn by a user, a tracker device can perform biometric monitoring via the use of one or more sensors to calculate and store the user's step count. The tracker device can transmit data representing the user's step count to an account on a web service (e.g., www.fitbit.com), a computer, a mobile phone, and / or a health station, where the data can be stored, processed, and / or visualized by the user. In addition to or in lieu of the user's step count, the tracker device can measure or calculate other physiological indicators. The tracker device can measure or calculate other physiological indicators in addition to or in lieu of the user's step count. Such physiological indicators may include, but are not limited to: energy expenditure, such as calories burned; number of floors climbed and / or descended; HR; heartbeat waveform; HR variability; HR recovery; respiration, SpO2, blood volume, blood glucose, skin moisture and skin pigmentation levels, location and / or orientation (e.g., via GPS, Global Navigation Satellite System (GLONASS) or similar systems); altitude; walking speed and / or travel distance; number of laps swam; detected swimming stroke types and counts; bicycle distance and / or speed; blood glucose; skin conductance; skin and / or body temperature; muscle state measured via electromyography; brain activity measured by electroencephalography; weight; body fat; calorie intake; nutrient intake from food; medication intake; sleep period (e.g., clock time, sleep stage, sleep quality and / or duration); pH level; hydration level; respiratory rate; and / or other physiological indicators.

[0079] An example tracker or monitoring device may also measure or calculate metrics related to the environment surrounding the user (e.g., using one or more environmental sensors), such as, for example, barometric pressure, weather conditions (e.g., temperature, humidity, pollen count, air quality, rain / snow conditions, wind speed), light exposure (e.g., ambient light, ultraviolet (UV) light exposure, time and / or duration spent in darkness), noise exposure, radiation exposure, and / or magnetic fields. In addition, the tracker device (and / or the host computer and / or another server) may collect data from one or more sensors of the device and may calculate metrics derived from such data. For example, the tracker device may calculate a user's stress or relaxation level based on a combination of HR variability, skin conductance, noise pollution, and / or sleep quality. In another example, the tracker device may determine the efficacy of a medical intervention (e.g., a medication) based on a combination of data related to medication intake, sleep, and / or activity. In yet another example, the tracker device may determine the efficacy of an allergy medication based on a combination of data related to pollen levels, medication intake, sleep, and / or activity. These examples are provided for illustration only and are not intended to be limiting or exhaustive.

[0080] An example monitoring device may include a computer-readable storage medium reader, a communication device (e.g., a modem, a network card (wireless or wired), an infrared communication device), and a working memory, as described above. A computer-readable storage medium reader may be connected to or configured to receive a computer-readable storage medium, which represents a remote, local, fixed, and / or removable storage device and a storage medium for temporarily and / or more permanently containing, storing, transmitting, and retrieving computer-readable information. Monitoring systems and various devices will also typically include a plurality of software applications, modules, services, or other elements located within at least one working memory device, including an operating system and an application program, such as a client application or a Web browser. It should be appreciated that alternative embodiments may have numerous variations different from those described above. For example, custom hardware may also be used, and / or specific elements may be implemented in hardware, software (including portable software, such as applets), or both. Further, connections to other computing devices such as network input / output devices may be employed.

[0081] Storage media and other non-transitory computer-readable media for containing code or portions of code may include any suitable media known or used in the art, such as but not limited to volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data), including RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage, magnetic cassettes, tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by the system device. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will understand other ways and / or methods of implementing various embodiments.

[0082] Reference now Fig.10 According to an embodiment of the present disclosure, a flowchart of a computer-implemented method 1000 for determining a user's fitness fatigue score for a given day (eg, today) is provided. The method 1000 may be implemented by, for example, the method described above with reference to Fig. 9 More specifically, one or more steps of the method 1000 may be implemented by a processor 902 (eg, a processor 902) on a wearable computing device (eg, a smartwatch 104 or 202). Fig. 9 Alternatively or additionally, one or more steps of method 1000 may be performed by a computing device remote from the smart watch (e.g., Fig. 9 The host computer 922) is used to execute. Fig.10 The steps are depicted as being performed in a particular order for purposes of illustration and discussion. A person of ordinary skill in the art using the disclosure provided herein will understand that the various steps of method 1000 may be adapted, modified, rearranged, performed simultaneously, or modified in various ways without departing from the scope of the present disclosure.

[0083] At (1002), method 1000 may include obtaining first data for a user during a first time period. The first data may include a plurality of heart rate measurements of the user during the first time period. In some embodiments, the first time period may include 14 days (e.g., two weeks) immediately preceding a day for which a fitness fatigue score is being determined.

[0084] At (1004), method 1000 may include obtaining second data for the user during a second time period that is shorter in duration than the first time period. The second data may include one or more sleep metrics for a plurality of different sleep events of the user during the second time period. For example, in some embodiments, the one or more sleep metrics may include a duration of each of the sleep events and a time of day at which each of the sleep events occurred. In some embodiments, the second time period may include the 7 days (e.g., a week) immediately preceding the day for which a fitness fatigue score is being determined.

[0085] At (1006), method 1000 may include obtaining third data for the user during a third time period having a longer duration than the first time period. The third data may include heart rate variability of the user during the third time period. In some embodiments, the third time period may include the 30 days (e.g., one month) immediately preceding the day for which the fitness fatigue score is being determined.

[0086] At (1008), method 1000 may include determining a fitness fatigue score for the user for the day based at least in part on the first data obtained at (1002), the second data obtained at (1004), and the third data obtained at (1006). In some embodiments, the fitness fatigue score may include a value ranging from 0 to 100.

[0087] At (1010), method 1000 may include causing a display screen of an electronic device to display the fitness fatigue score determined at (1008). For example, in some embodiments, the fitness fatigue score may be displayed on a wearable device (e.g., Figure 1A Smart Watch 104 and Figure 2 Alternatively or additionally, the fitness fatigue score may be displayed on a display of a smart watch 202 in the wearable computing device. Figure 2 on a display screen of a smart phone 204 or tablet computer 206).

[0088] At (1012), method 1000 may include determining a recommendation regarding exercise for the day based at least in part on the fitness fatigue score for the day determined at (1010). For example, in some embodiments, the recommendation may vary depending on the fitness fatigue score. When the fitness fatigue score determined at (1008) is below a first threshold (e.g., approximately 30), the recommendation may include forgoing exercise for the day (e.g., resting). Alternatively, when the fitness fatigue score determined at (1008) is greater than the first threshold and less than a second threshold (e.g., approximately 65), the recommendation may include a first exercise activity (e.g., a low heart rate exercise). Further, when the fitness fatigue score determined at (1008) is greater than the second threshold, the recommendation may include a second exercise activity that is more intense than the first exercise activity (e.g., high intensity aerobic exercise).

[0089] Reference now Fig.11 , a flowchart of a method 1100 for determining a fitness fatigue score according to some embodiments of the present disclosure is provided. The method 1100 may be performed by, for example, Fig. 9 More specifically, one or more steps of the method 1000 may be implemented by a processor 902 (eg, a processor 902) on a wearable computing device (eg, a smartwatch 104 or 202). Fig. 9 Alternatively or additionally, one or more steps of method 1000 may be performed by a computing device remote from the smart watch (e.g., Fig. 9 The host computer 922) is used to execute. Fig.11 The steps are depicted as being performed in a particular order for purposes of illustration and discussion. A person of ordinary skill in the art using the disclosure provided herein will understand that the various steps of method 1100 may be adapted, modified, rearranged, performed simultaneously, or modified in various ways without departing from the scope of the present disclosure.

[0090] At (1102), method 1100 may include, based at least in part on Fig.10, determining a first sub-score (e.g., an H-score) based on first data obtained at (1002) of method 1000 depicted in FIG. 1000 . For example, the first sub-score may be determined based at least in part on a plurality of heart rate measurements captured by one or more biometric sensors of a wearable computing device worn by a user over a first time period (e.g., approximately 14 days). In some embodiments, the first sub-score may be determined based at least in part on an amount of time that the heart rate measurements collected for the user over the first time period were above a threshold indicating that the user was stressed (e.g., due to exercise). For example, the number of minutes that the user's heart rate was above the threshold may be determined, and then the first sub-score may be determined from the number of minutes. More particularly, if yesterday's heart rate data was above the threshold for a threshold amount of time, this may indicate that the user participated in strenuous physical activity (e.g., running, swimming, etc.) yesterday, and may therefore have been fatigued by the physical activity. It should be understood that the threshold heart rate may be user-specific based at least in part on the user's resting heart rate and the user's maximum heart rate as a function of the user's age.

[0091] At (1104), method 1100 may include, based at least in part on Fig.10 , and determining a second sub-score (e.g., an S score) based on the second data obtained at (1004) of method 1000 depicted in . For example, the second sub-score can be determined at least in part based on one or more sleep indicators for each sleep event of a plurality of sleep events of the user that occurred during a second time period (e.g., approximately 7 days). In some embodiments, the one or more sleep indicators may include the duration of each sleep event of the plurality of sleep events. Alternatively or in addition, the one or more sleep indicators may include the time of day at which each sleep event of the sleep events starts and ends. For example, in some embodiments, the second sub-score may be high when the user has at least a threshold number of hours (e.g., approximately 7 hours) of sleep per night. Conversely, the second sub-score may be low when the user has less than a threshold number of hours of sleep per night.

[0092] In some implementations, the second sub-score may be a function of the amount of time the user spends in one or more sleep stages for one or more sleep events in a plurality of sleep events that occur within a second time period (e.g., 7 days). For example, the second sub-score may be high when the user spends a threshold amount of time in a restorative stage of sleep (e.g., deep sleep or REM) for one or more sleep events in the sleep events. It should be understood that the threshold amount of time required for a restorative stage of sleep may be a function of the user's age.

[0093] In some implementations, the second sub-score may be a function of the time of day of one or more of the sleep events during the second time period (e.g., 7 days). For example, if the second data indicates that the user naps during the day and therefore sleeps less at night, the second sub-score may be lower than if the user does not nap and sleeps longer each night.

[0094] At (1106), method 1100 may include, based at least in part on Fig.10 The third sub-score (eg, T-score) is determined from the third data obtained at (1006) of the method 1000 depicted in FIG. 1 . The details of this step will be referred to in Fig.13 for a more detailed discussion.

[0095] At (1108), method 1100 may include determining a fitness fatigue score based at least in part on the first sub-score (e.g., H-score), the second sub-score (e.g., S-score), and the third sub-score (e.g., T-score). In some embodiments, the first sub-score, the second sub-score, and the third sub-score may each include a numerical value ranging from 0 to 100.

[0096] In some embodiments, the first sub-score determined at (1102), the second sub-score determined at (1104), and the third sub-score determined at (1106) may each be weighted differently. For example, in some embodiments, the first sub-score (e.g., H-score) may be weighted more heavily than the second sub-score (e.g., S-score) and the third sub-score (T-score). For example, in some embodiments, the first sub-score may account for half of the fitness fatigue score. Further, in some embodiments, the fitness fatigue score may be the first sub-score. For example, when the first sub-score is below a threshold value (e.g., about 30), the fitness fatigue score may be the first sub-score. It should be understood that in such implementations, when the first sub-score is determined to be below the threshold value, the second sub-score and the third sub-score become irrelevant.

[0097] Reference now Fig.12 According to an embodiment of the present disclosure, a flowchart of a method 1200 for determining a first sub-score (eg, H-score) of a fitness fatigue score is provided. The method 1200 may be performed by, for example, the method described above with reference to Fig. 9 The system 900 discussed is implemented. Fig.12 The steps are depicted as being performed in a particular order for purposes of illustration and discussion. One of ordinary skill in the art will appreciate, using the disclosure provided herein, that the various steps of method 1200 may be adapted, modified, rearranged, performed simultaneously, or modified in various ways without departing from the scope of the present disclosure.

[0098] At (1202), method 1200 may include determining a resting heart rate of a user based at least in part on a plurality of heart rate measurements included in first data obtained during a first time period.

[0099] At (1204), method 1200 may include determining a maximum heart rate of the user based at least in part on the age of the user. For example, in some embodiments, the maximum heart rate of the user may be determined by subtracting the age of the user from 220 beats per minute. For example, if the user is 40 years old, the maximum heart rate of the user may be 180 beats per minute.

[0100] At (1206), method 1200 may include determining a reserve value for each of a plurality of heart rate measurements included in the first data obtained during the first time period. The reserve value may be used to distinguish heart rate measurements taken when the user is sedentary (e.g., sleeping, sitting, etc.) from heart rate measurements taken when the user is engaged in physical activity (e.g., running, walking, bicycling, yoga, etc.). For example, in some embodiments, the heart rate reserve value may be determined based at least in part on the user's resting heart rate determined at (1202), the user's maximum heart rate determined at (1204), and the user's instantaneous heart rate (i.e., one of the plurality of heart rate measurements). In some embodiments, the reserve value may be a numerical value ranging from 0 to 1.

[0101] At (1208), method 1200 may determine that a subset of the plurality of heart rate measurements is associated with physical activity based at least in part on the retention value determined at (1206). For example, in some embodiments, heart rate measurements having a retention value below a threshold value (e.g., approximately 0.4) may be ignored, while a subset of heart rate measurements having a retention value equal to or greater than the threshold value may be used to determine the first sub-score. In this manner, the retention value may reduce the amount of data (e.g., heart rate measurements) that must be considered to determine the first sub-score indicating fatigue of the user due to physical activity during the first time period.

[0102] At (1210), method 1200 may include determining a first sub-score based at least in part on a subset of the heart rate measurements included in the first data. For example, in some embodiments, a duration that a heart rate retention value for the user's heart rate was above a threshold may be determined. Additionally, in some embodiments, an amount of time that has passed since the heart rate retention value for the user's heart rate was above the threshold may be determined. In some embodiments, the first sub-score may be determined based at least in part on one or both of these parameters (e.g., a duration that the heart rate retention value was above the threshold, a duration since the heart rate retention value for the user's heart rate was last above the threshold). Alternatively or additionally, in some embodiments, the first sub-score includes a numerical value ranging from 0 to 100 to indicate how fatigued the user is due to physical activity occurring during the first time period.

[0103] Reference now Fig.13 According to an embodiment of the present disclosure, a flowchart of a method 1300 for determining a third sub-score (eg, T-score) of a fitness fatigue score is provided. The method 1300 may be performed by, for example, the method described above with reference to Fig. 9 More specifically, one or more steps of the method 1000 may be implemented by a processor 902 (eg, a processor 902) on a wearable computing device (eg, a smartwatch 104 or 202). Fig. 9 Alternatively or additionally, one or more steps of method 1000 may be performed by a computing device remote from the smart watch (e.g., Fig. 9 The host computer 922) is used to execute. Fig.13 The steps are depicted as being performed in a particular order for purposes of illustration and discussion. One of ordinary skill in the art will appreciate, using the disclosure provided herein, that the various steps of method 1300 may be adapted, modified, rearranged, performed simultaneously, or modified in various ways without departing from the scope of the present disclosure.

[0104] At (1302), method 1300 may include determining a root mean square difference (RMSSD) of a heart rate variability measure (HRV) of the user for each of a plurality of sleep events occurring within a third time period. It should be understood that the RMSSD of the HRV measure may be determined based on biometric data (e.g., a PPG signal) obtained from one or more biometric sensors (e.g., a PPG sensor) of the wearable computing device.

[0105] At (1304), method 1300 may include comparing the RMSSD of the user's HRV during the user's most recent sleep event during the third time period to the RMSSD of the HRV of at least one preceding sleep event that occurred during the third time period.

[0106] At (1306), method 1300 may include determining a third sub-score based at least in part on the comparison that occurred at (1304). In some embodiments, when the RMSSD of the user's HRV during the user's most recent sleep event is lower than the RMSSD of the user's HRV during one of the preceding sleep events that occurred during the third time period, the third sub-score may be 0. Conversely, when the RMSSD of the user's HRV during the user's most recent sleep event is higher than the RMSSD of the user's HRV during one of the preceding sleep events that occurred during the third time period, the third sub-score may be 100.

[0107] Reference now Fig.14, a fitness fatigue score (eg, a daily readiness score) may be displayed on a wearable computing device (eg, Figure 2 202). As shown, the fitness fatigue score can be a single numerical value. For example, the fitness fatigue score 98 for the day can indicate that the user is ready to participate in physical activity such as high-intensity exercise. If the fitness fatigue score for the day is lower (e.g., about 50 or about 20), the recommendation generated based on the fitness fatigue score will be different. For example, the recommendation will be for the user to participate in low-intensity exercise, or alternatively, to forgo physical activity for the day.

[0108] Reference now Figures 15 to 17 According to some embodiments of the present disclosure, a method is provided for executing the above-mentioned operation on a mobile computing device (e.g., Figure 2 1504). As shown, screen 1500 may indicate the user's daily readiness score (e.g., fitness fatigue score) for the day. In addition, screen 1500 may show sub-scores of the daily readiness score as activity (e.g., a first sub-score), recent sleep (e.g., a second sub-score), and heart rate variability (e.g., a third sub-score). Screen 1502 may include recommended exercises for the user to try that day based on the user's daily readiness score. Additionally, screen 1504 may show the daily readiness scores determined for the user over the past week. In this way, the user can see how their readiness scores have changed over the past week.

[0109] Wearable computing devices according to example aspects of the present disclosure can provide many technical effects and benefits. For example, the wearable computing device can generate and display a user's fitness fatigue score for the day. In addition, the wearable computing device can generate a notification about the user's exercise for the day based at least in part on the user's fitness fatigue score. In particular, when the fitness fatigue score indicates that the user is well rested, the wearable computing device can generate a recommendation for the user to participate in a vigorous activity (e.g., running). On the contrary, when the fitness fatigue score is not well rested, the wearable computing device can generate a recommendation for the user to participate in a less vigorous activity (e.g., walking) or give up exercise. In addition, since the fitness fatigue score is determined based on biometric data collected by the biometric sensor of the wearable computing device, the fitness fatigue score can be a better indicator of the user's current energy level than the user's own assessment of his or her energy level. For example, it may happen that even if the user is actually tired and therefore not ready to participate in a vigorous activity, the user may still feel ready to participate in a vigorous activity. However, since the fitness fatigue score is determined based on biometric data rather than how the user feels, the fitness fatigue score can provide a more objective indicator of the user's readiness to participate in a vigorous activity. In this way, a wearable computing device according to the present disclosure may provide recommendations regarding exercise based on objective metrics (e.g., a fitness fatigue score), and may therefore reduce or eliminate situations where a user feels ready to engage in strenuous activity when the user's biometrics indicate otherwise.

[0110] In addition to the above description, controls may be provided to the user, which allow the user to make a choice about whether and when the system, program or feature described herein can enable the collection of user information (e.g., information about the user's social network, social actions or activities, occupation, user's preferences or the user's current location) and whether to send content or communication from the server to the user. In addition, certain data may be processed in one or more ways before it is stored or used so that personally identifiable information is removed. For example, the user's identity may be processed so that the user's personally identifiable information cannot be determined, or the user's geographic location in which location information is obtained may be generalized (such as to the city, zip code or state level) so that the user's specific location cannot be determined. Therefore, the user can control what information is collected about the user, how the information is used, and what information is provided to the user. To this end, any information related to the user collected as described herein will be kept confidential and will not be improperly used or published.

[0111] Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention as set forth in the claims.

Claims

1. A computer-implemented method for determining a user's fitness fatigue score, the computer-implemented method comprising: obtaining first data of the user in a first time period, the first data comprising a plurality of heart rate measurements of the user in the first time period; obtaining second data of the user in a second time period shorter than the first time period, the second data comprising one or more sleep indicators of a plurality of sleep events of the user in the second time period; obtaining third data of the user in a third time period having a duration longer than the first time period, the third data comprising heart rate variability data of the user in the third time period; determining the fitness fatigue score of the user for the day based at least in part on the first data, the second data, and the third data; The display screen of the electronic device is enabled to display the fitness fatigue score.

2. The computer-implemented method of claim 1 , further comprising: determining a recommendation regarding exercise for the day based at least in part on the fitness fatigue score for the day; as well as The display screen of the electronic device is caused to display the recommendation.

3. The computer-implemented method of claim 2, wherein: When the fitness fatigue score of the day is lower than a first threshold, the recommendation is to let the user give up exercising on the day; When the fitness fatigue score of the day is greater than the first threshold but less than a second threshold, the recommendation is to let the user participate in a first physical activity on the day; and When the fitness fatigue score of the day is greater than the second threshold, the recommendation is to let the user participate in a second physical activity on the day, where the second physical activity is more intense than the first physical activity for the user.

4. The computer-implemented method of claim 1 , wherein determining the fitness fatigue score of the user for the day comprises: determining a first sub-score based at least in part on the first data; determining a second sub-score based at least in part on the second data; determining a third sub-score based at least in part on the third data; and The fitness fatigue score of the user for the day is determined based at least in part on the first sub-score, the second sub-score, and the third sub-score. 5 . The computer-implemented method of claim 4 , wherein the first sub-score is weighted more heavily than each of the second sub-score and the third sub-score.

6. The computer-implemented method of claim 4, wherein determining the fitness fatigue score comprises: determining that the first sub-score is less than a threshold; as well as Determining the fitness fatigue score of the day is the first sub-score.

7. The computer-implemented method of claim 4, wherein: Determining the first sub-score based on the first data includes: determining a retention value for each of the plurality of heart rate measurements included in the first data; determining that the retention value of one or more heart rate measurements of the plurality of heart rate measurements collected during the first time period is greater than a threshold; and The first sub-score is determined based at least in part on the one or more heart rate measurements.

8. The computer-implemented method of claim 7, wherein determining the reserved value for each of the heart rate measurements comprises: determining a resting heart rate of the user based at least in part on the first data; determining a maximum heart rate of the user based at least in part on an age of the user; The reserved value for each heart rate measurement in the plurality of heart rate measurements is determined based at least in part on a comparison of each corresponding heart rate measurement to the resting heart rate of the user and the maximum heart rate of the user.

9. The computer-implemented method of claim 4, the heart rate variability data comprising heart rate variability measurements taken during each of a plurality of sleep events occurring during the third time period; as well as Determining the third sub-score based at least in part on the heart rate variability data includes: determining a root mean square of consecutive differences of heart rate variability measures associated with a first sleep event in the plurality of sleep events, the first sleep event being a most recent sleep event in the plurality of sleep events; The third sub-score is determined based at least in part on a comparison of the heart rate variability measure associated with the first sleep event with a heart rate variability measure associated with at least one other sleep event of the plurality of sleep events.

10. The computer-implemented method of claim 1, wherein the one or more sleep metrics include a duration of each of the plurality of sleep events and a time of day at which each of the plurality of sleep events occurred.

11. The computer-implemented method of claim 1 , wherein: The first period includes the 14 days immediately preceding the said day; The second period includes the 7 days immediately preceding the current day; and The third time period includes the 30 days immediately before the current day.

12. The computer-implemented method of claim 1, wherein the electronic device comprises a wearable computing device or a mobile computing device worn by the user.

13. A wearable computing device comprising: Display screen; one or more biometric sensors; as well as One or more processors configured to: obtaining first data of a user during a first time period, the first data comprising a plurality of heart rate measurements of a user wearing the wearable computing device; obtaining second data of the user in a second time period shorter than the first time period, the second data comprising one or more sleep indicators of a plurality of sleep events of the user; obtaining third data of the user in a third time period having a duration longer than the first time period, the third data comprising heart rate variability data of the user; receiving a fitness fatigue score for the user for the day based at least in part on the first data, the second data, and the third data; as well as The display screen is caused to display the fitness fatigue score of the user.

14. The wearable computing device of claim 13, wherein the one or more processors are further configured to: The display screen is caused to display a recommendation regarding exercise based at least in part on the fitness fatigue score of the user.

15. The wearable computing device of claim 14, wherein: When the fitness fatigue score of the day is lower than a first threshold, the recommendation is to let the user give up exercising on the day; When the fitness fatigue score of the day is greater than the first threshold but less than a second threshold, the recommendation is to let the user participate in a first physical activity on the day; and When the fitness fatigue score of the day is greater than the second threshold, the recommendation is to let the user participate in a second physical activity on the day, where the second physical activity is more intense than the first physical activity for the user.

16. The wearable computing device of claim 13, wherein: The first period includes the 14 days immediately preceding the said day; The second period includes the 7 days immediately preceding the current day; and The third time period includes the 30 days immediately before the current day. 17 . The wearable computing device of claim 13 , wherein the one or more sleep metrics include a duration of each sleep event in the plurality of sleep events and a time of day at which each sleep event in the plurality of sleep events occurred.

18. The wearable computing device of claim 13, wherein the fitness fatigue score comprises a numerical value ranging from 0 to 100.

19. A non-transitory computer-readable medium storing instructions executable by one or more processors of an electronic device, the instructions comprising: instructions for obtaining first data of a user during a first time period, the first data comprising a plurality of heart rate measurements of the user during the first time period; instructions for obtaining second data of the user during a second time period of shorter duration than the first time period, the second data comprising one or more sleep metrics of a plurality of sleep events of the user during the second time period; instructions for obtaining third data of the user during a third time period having a duration longer than the first time period, the third data including heart rate variability data of the user during the third time period; instructions for determining the user's fitness fatigue score for the day based at least in part on the first data, the second data, and the third data; Instructions for causing the one or more processors of the electronic device to cause display of the fitness fatigue score on a display screen.

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