Time before deep sleep to improve sleep quality

Abstract

According to one embodiment, a computing device may include one or more processors and one or more computer-readable media storing instructions that, when executed by the processor(s), cause the computing device to perform operations. The operations may include obtaining a plurality of sleep stages associated with a sleep session of a user. The sleep session may be defined at least in part by the user's estimated bedtime. The operations may further include identifying one or more defined sleep stages, among the plurality of sleep stages, that indicate a defined sleep state of the user. The operations may further include calculating a time before deep sleep (TBSS) metric based at least in part on the user's estimated bedtime and the start time of the defined sleep stage(s). The operations may further include performing one or more operations based at least in part on the TBSS metric.

Description

[Technical Field]

[0001] The present disclosure relates generally to assessing and modifying sleep quality, and more particularly to calculating time before deep sleep metrics and using the time before deep sleep metrics to facilitate assessing and modifying sleep quality. [Background technology]

[0002] Assessing the time it takes a user to fall asleep is challenging for both traditional sleep detection techniques and wearable devices, such as wrist-worn physiological monitoring devices. Some existing wrist-worn physiological monitoring devices use accelerometer data to determine a user's estimated bedtime (e.g., the time a user enters a quiet pre-sleep state) and / or use a combination of heart rate and accelerometer data to determine a user's sleep stages.

[0003] A problem with such wrist-worn physiological monitoring devices discussed above is that challenges exist in attempting to accurately and consistently distinguish between when a user is in a relaxed, stationary state (e.g., watching television or reading in bed) and when the user is truly asleep. That is, for example, such wrist-worn physiological monitoring devices may not accurately and consistently distinguish between the user's estimated bedtime and the time the user enters a deep sleep state. Thus, another problem with such wrist-worn physiological monitoring devices is that they often do not reflect the user's perception of their own sleep onset latency (i.e., the time it takes the user to fall asleep after the user's estimated bedtime). Summary of the Invention

[0004] Aspects and advantages of embodiments of the present disclosure will be set forth in part in the description that follows, or may be learned from the description, or may be learned by practice of the embodiments.

[0005] According to one exemplary embodiment, a computing device may include one or more processors and one or more computer-readable media storing instructions that, when executed by the one or more processors, cause the computing device to perform operations. The operations may include obtaining a plurality of sleep stages associated with a sleep session of a user. The sleep session may be defined at least in part by the user's estimated bedtime. The operations may further include identifying one or more defined sleep stages, among the plurality of sleep stages, that indicate a defined sleep state of the user. The operations may further include calculating a time before deep sleep metric based at least in part on the user's estimated bedtime and a start time of the one or more defined sleep stages. The operations may further include performing one or more operations based at least in part on the time before deep sleep metric.

[0006] According to another exemplary embodiment, a computer-implemented method for assessing sleep quality and facilitating sleep quality changes may include obtaining, by a computing device operably coupled to one or more processors, a plurality of sleep stages associated with a sleep session of a user. The sleep session may be defined at least in part by the user's estimated bedtime. The computer-implemented method may further include identifying, by the computing device, one or more defined sleep stages among the plurality of sleep stages that indicate a defined sleep state of the user. The computer-implemented method may further include calculating, by the computing device, a time before deep sleep metric based at least in part on the user's estimated bedtime and the start time of the one or more defined sleep stages. The computer-implemented method may further include performing, by the computing device, one or more actions based at least in part on the time before deep sleep metric.

[0007] According to another exemplary embodiment, one or more computer-readable media may store instructions that, when executed by one or more processors of the computing device, cause the computing device to perform operations. The operations may include obtaining a plurality of sleep stages associated with a sleep session of a user. The sleep session may be defined at least in part by the user's estimated bedtime. The operations may further include identifying one or more defined sleep stages, among the plurality of sleep stages, that indicate a defined sleep state of the user. The operations may further include calculating a time before deep sleep metric based at least in part on the user's estimated bedtime and a start time of the one or more defined sleep stages. The operations may further include performing one or more operations based at least in part on the time before deep sleep metric.

[0008] These and other features, aspects, and advantages of various embodiments of the present disclosure will become better understood with reference to the following detailed description and the appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the present disclosure and, together with the detailed description, serve to explain associated principles.

[0009] Detailed descriptions of embodiments directed to those skilled in the art are set forth herein with reference to the accompanying drawings. [Brief explanation of the drawings]

[0010] [Figure 1] 1 shows a perspective view of an exemplary, non-limiting wearable device according to one or more exemplary embodiments of the present disclosure. [Figure 2] 1 shows a perspective view of an exemplary, non-limiting wearable device according to one or more exemplary embodiments of the present disclosure. [Figure 3] 1 shows a perspective view of an exemplary, non-limiting wearable device according to one or more exemplary embodiments of the present disclosure. [Figure 4] 1 shows a block diagram of an exemplary, non-limiting device according to one or more exemplary embodiments of the present disclosure. [Figure 5] 1 shows a diagram of an exemplary, non-limiting sleep quality management system, according to one or more exemplary embodiments of the present disclosure. [Figure 6] 1 shows a diagram of an exemplary, non-limiting sleep quality management system, according to one or more exemplary embodiments of the present disclosure. [Figure 7] 1A-C each show an exemplary, non-limiting diagram of sleep stages, according to one or more exemplary embodiments of the present disclosure. [Figure 8] 1 illustrates a flow diagram of an exemplary, non-limiting computer-implemented method according to one or more exemplary embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0011] Repeat use of reference characters and / or numerals in the present specification and / or figures is intended to represent the same or similar features, elements, or operations of the present disclosure. Repeated descriptions of reference characters and / or numerals repeated herein have been omitted for the sake of brevity.

[0012] overview As referred to herein, the terms "includes" and "including" are intended to be inclusive, similar to the term "comprising." As referred to herein, the terms "or" and "and / or" are generally intended to be inclusive, i.e., that is, (i.e.,) "A or B" or "A and / or B" are each intended to mean "A or B or both." As referred to herein, the terms "first," "second," "third," etc. can be used interchangeably to distinguish one component or entity from another and are not intended to denote the location, function, or importance of the individual components or entities. As referred to herein, the terms “couple,” “couples,” “coupled,” and / or “coupling” refer to a chemical coupling (e.g., a chemical bond), a communicative coupling, an electrical coupling and / or an electromagnetic coupling (e.g., a capacitive coupling, an inductive coupling, a direct coupling, and / or a connective coupling), a mechanical coupling, an operative coupling, an optical coupling, and / or a physical coupling.

[0013] As referred to herein, the term "system" may refer to hardware (e.g., application-specific hardware), computer logic executing on a general-purpose processor (e.g., a central processing unit (CPU)), and / or some combination thereof. In some embodiments, a "system" described herein may be implemented in hardware, application-specific circuitry, firmware, and / or software controlling a general-purpose processor. In some embodiments, a "system" described herein may be implemented as program code files stored on a storage device, loaded into memory, and executed by a processor, and / or may be provided from a computer program product, such as, for example, computer-executable instructions stored on a tangible computer-readable storage medium (e.g., random access memory (RAM), hard disk, optical medium, magnetic medium).

[0014] Exemplary aspects of the present disclosure are directed to assessing a user's sleep quality and facilitating alteration (e.g., improvement) of the user's sleep quality based on such assessment. More specifically, exemplary embodiments described herein are directed to calculating a time before deep sleep (TBSS) metric associated with a user, and further using the TBSS metric to perform one or more actions that can facilitate alteration (e.g., improvement) of the user's sleep quality.

[0015] As referred to herein, a "time before deep sleep (TBSS) metric" or "TBSS metric" is defined as the period between a user's estimated bedtime and the time the user enters a deep sleep state. As used herein, the term "estimated bedtime" of a user is defined as the estimated time the user enters a quiet pre-sleep state (e.g., a relaxed, still state). As referred to herein, the term "deep sleep state" is defined as a continuous and / or high-quality sleep state (e.g., deep sleep, rapid eye movement (REM) sleep, and / or a relatively high-quality sleep state in which the user is not moving (e.g., not tossing and turning)). For example, as referred to herein, the term "deep sleep state" is defined as any one of: 1) the first instance of deep sleep; 2) the first instance of REM sleep; or 3) the first instance of light sleep with a light sleep stage duration of, for example, 20 minutes or more, uninterrupted by other sleep states (e.g., uninterrupted by deep sleep stages, REM sleep stages, or wake stages).

[0016] According to one or more exemplary embodiments of the present disclosure, a computing device, such as, for example, wearable device 100 described below with reference to the exemplary embodiments illustrated in FIGS. 1 , 2 , 3 , and 4 , can assess a user's sleep quality and facilitate altering (e.g., improving) the user's sleep quality based on such assessment. More specifically, in at least one embodiment described herein, such a computing device can calculate a TBSS metric associated with a user and further use such TBSS metric to perform one or more operations that can facilitate altering (e.g., improving) the user's sleep quality. In some embodiments, the computing device can constitute and / or include, for example, a physiological monitoring device, a wearable computing device, a wearable physiological monitoring device (e.g., a wrist-worn device, a chest strap device), and / or other computing device that can calculate a TBSS metric associated with a user and further use such TBSS metric to perform one or more operations that can facilitate altering (e.g., improving) the user's sleep quality.

[0017] In at least one embodiment of the present disclosure, to assess a user's sleep quality and facilitate alteration (e.g., improvement) of the user's sleep based on such assessment, the above-described computing device (e.g., wearable device 100) may perform operations including, but not limited to, acquiring a plurality of sleep stages associated with the user's sleep session, the sleep session being defined at least in part by the user's estimated bedtime; identifying one or more sleep stages among the plurality of sleep stages that indicate the user's defined sleep state; calculating a time before deep sleep (TBSS) metric based at least in part on the user's estimated bedtime and the start time of the one or more defined sleep stages; and performing one or more operations based at least in part on the TBSS metric. In this embodiment or other embodiments, the TBSS metric may indicate a period between the user's estimated bedtime and the time the user enters a defined sleep state. In this embodiment or other embodiments, the user's estimated bedtime may constitute the estimated bedtime defined above, and the defined sleep state may constitute the deep sleep state defined above.

[0018] In one or more embodiments described herein, the multiple sleep stages that may be associated with a user's sleep session (e.g., a sleep session lasting one or more hours) may include, for example, a wake stage, a light sleep stage, a deep sleep stage, a REM sleep stage, and / or other sleep stages. For example, in one embodiment, the multiple sleep stages may include a wake stage in which the user is awake, a light sleep stage in which the user is in a quiet pre-sleep state and / or a relaxed, still state, a deep sleep stage in which the user is in a true, continuous and / or high quality sleep state, a REM sleep stage in which the user is experiencing REM and therefore in a true, continuous and / or high quality sleep state, and / or other sleep stages.

[0019] In at least one embodiment of the present disclosure, the one or more defined sleep stages that may indicate the user's defined sleep state (e.g., the deep sleep state defined above) may comprise and / or include one or more defined amounts of sleep stages in the plurality of sleep stages described above. For example, in one embodiment, the one or more defined sleep stages may comprise and / or include a defined amount (e.g., 10, 20, 30) of light sleep stages, a defined amount (e.g., 1, 2) of deep sleep stages, and / or a defined amount (e.g., 1, 2) of REM sleep stages.

[0020] In some embodiments described herein, each sleep stage of the plurality of sleep stages and each defined sleep stage of the one or more defined sleep stages may be defined by a specific time interval (e.g., 30 seconds, 1 minute, 2 minutes) such that all sleep stages are defined by the same duration (e.g., 30 seconds, 1 minute, 2 minutes). For example, in one embodiment, each sleep stage of the plurality of sleep stages and each defined sleep stage of the one or more defined sleep stages may have a duration of 1 minute. In some embodiments described herein, each sleep stage of the plurality of sleep stages and each defined sleep stage of the one or more defined sleep stages may be defined by a specific time interval (e.g., 30 seconds, 1 minute, 2 minutes) corresponding to a discrete time interval (e.g., 30 seconds, 1 minute, 2 minutes) in the user's sleep session.

[0021] In one or more embodiments of the present disclosure, the time at which a particular light sleep stage (e.g., a first light sleep stage) begins in a user's sleep session can correspond to the user's estimated bedtime as defined above. In some embodiments, the time at which one or more defined sleep stages begin can correspond to the time at which the user enters a defined sleep state (e.g., a deep sleep state as defined above).

[0022] In one embodiment of the present disclosure, the one or more defined sleep stages described above may constitute and / or include a specified amount of light sleep stages, such as, for example, 20 light sleep stages (e.g., 20 consecutive light sleep stages). In this or other embodiments, the one or more defined sleep stages described above may constitute and / or include 20 consecutive uninterrupted light sleep stages in the user's sleep session. Thus, in this or other embodiments, the one or more defined sleep stages may constitute and / or include a 20-minute period of uninterrupted light sleep. In this or other embodiments, the time at which the first light sleep stage begins in the user's sleep session may correspond to the user's estimated bedtime as defined above. In this or other embodiments, the time at which the first light sleep stage begins within the period of 20 consecutive uninterrupted light sleep stages may correspond to the time at which the user enters the deep sleep state as defined above. That is, for example, in this or other embodiments, the start time of the first light sleep stage within a period of 20 consecutive uninterrupted light sleep stages may correspond to the time the user enters the deep sleep state defined above.

[0023] In other embodiments of the present disclosure, the one or more defined sleep stages described above may constitute and / or include a defined amount of deep sleep stages, such as, for example, one deep sleep stage. In this or other embodiments, the time at which a first light sleep stage begins in a user's sleep session may correspond to the user's estimated bedtime as defined above. In this or other embodiments, the time at which a deep sleep stage (e.g., a first deep sleep stage) begins in a user's sleep session may correspond to the time at which the user enters a deep sleep state as defined above.

[0024] In other embodiments of the present disclosure, the one or more defined sleep stages described above may constitute and / or include a defined amount of REM sleep stages, such as, for example, one REM sleep stage. In this or other embodiments, the time at which a first light sleep stage begins in a user's sleep session may correspond to the user's estimated bedtime as defined above. In this or other embodiments, the time at which a REM sleep stage (e.g., a first REM sleep stage) begins in a user's sleep session may correspond to the time at which the user enters deep sleep as defined above.

[0025] In one embodiment of the present disclosure, the computing device (e.g., wearable device 100) can obtain the sleep stages from another computing device, which can generate such sleep stages using physiological data corresponding to the user (e.g., accelerometer data, heart rate data, pulse-related data, respiration-related data) captured and / or collected (e.g., via an accelerometer, photoplethysmography (PPG) sensor) during the user's sleep session. For example, in at least one embodiment, the computing device (e.g., wearable device 100) can obtain the sleep stages from external computing devices 504, 504a, 504b, and / or 504c and / or server system 604, described below with reference to the exemplary embodiments shown in FIGS. 5 and 6. In other embodiments, the computing device (e.g., wearable device 100) can generate the sleep stages (e.g., as described below) using physiological data corresponding to the user, which can be captured and / or collected by the computing device during the user's sleep session.

[0026] In some embodiments described herein, a computing device (e.g., wearable device 100) may include and / or be communicatively coupled to one or more physiological sensors, such as, for example, an accelerometer and / or a PPG sensor, each of which may capture accelerometer data (e.g., movement data) and / or heart rate data (e.g., pulse-related data) corresponding to a user. In these or other embodiments, the computing device may operate the accelerometer and / or PPG sensor during the user's sleep session to capture accelerometer data (e.g., movement data) and / or heart rate data (e.g., pulse-related data) corresponding to the user. In these or other embodiments, to generate the above-described multiple sleep states, the computing device may implement one or more classification modules, processes, techniques, algorithms, and / or models (e.g., machine learning models) that can classify and / or otherwise label periods within the user's sleep session (e.g., 30-second, 1-minute, 2-minute periods) with particular sleep stages (e.g., awake, light, deep, REM) based on one or more features derived from the user's accelerometer data (e.g., movement data) and / or heart rate data (e.g., pulse-related data). In these or other embodiments, the computing device may generate the multiple sleep stages using a classifier (e.g., machine learning algorithms and / or models), such as, for example, a nearest neighbor classifier, a random forest classifier, a support vector machine, a decision tree, a neural network, a linear discriminant classifier, and / or other classifiers.

[0027] In at least one embodiment of the present disclosure, the classifier(s) that a computing device (e.g., wearable device 100) can use to generate sleep stages can be trained on a known set of annotated sleep logs that can be generated from multiple sleep sessions of different users. For example, in this or other embodiments, such classifier(s) can be trained using features that can be extracted from accelerometer data (e.g., movement data) and / or heart rate (e.g., pulse-related data) that can be collected during sleep sessions of different users. In this or other embodiments, each sleep session of a different user can be conducted under the supervision of an expert and / or trained sleep scorer, using specialized equipment and / or detailed analysis of each time interval evaluated during each sleep session. Thus, in this or other embodiments, features that can be extracted from such accelerometer data and / or heart rate data of different users collected during periods that the sleep scorer determines to be deep sleep stages can be used to train the classifier to assign deep sleep stage classifications to specific periods of a user's sleep sessions that have features that meet (e.g., match) the criteria of the classifier developed during such training.

[0028] In at least one embodiment in which a computing device (e.g., wearable device 100) generates the above-mentioned multiple sleep stages associated with a user's sleep session, the computing device may use the above-mentioned classifier(s) to generate (e.g., classify, categorize, define, characterize) one or more sleep stages using certain physiological data of the user and to generate one or more other sleep stages using other physiological data of the user. For example, in one embodiment in which a computing device generates multiple sleep stages, the computing device may use such classifier(s) to generate (e.g., classify, categorize, define, characterize) light sleep stage(s) and / or may determine the user's estimated bedtime using, for example, the user's accelerometer (e.g., movement data). In other embodiments in which the computing device generates multiple sleep stages, the computing device may, for example, use such classifier(s) to generate (e.g., classify, categorize, define, characterize) light sleep stage(s), deep sleep stage(s), and / or REM sleep stage(s) using the user's accelerometer data (e.g., movement data), heart rate data (e.g., pulse-related data), and / or respiration data (breathing data).

[0029] According to at least one embodiment of the present disclosure, based at least in part on (e.g., in response to) obtaining a plurality of sleep stages associated with a user's sleep session, a computing device (e.g., wearable device 100) may calculate a TBSS metric based on the user's estimated bedtime and the start time of one or more defined sleep stages. In this embodiment or other embodiments, to calculate the TBSS metric, the computing device may calculate the time difference between the user's estimated bedtime and the start time of one or more defined sleep stages. For example, in this embodiment or other embodiments, to calculate the TBSS metric, the computing device may subtract the user's estimated bedtime from the start time of one or more defined sleep stages to determine the time difference between the user's estimated bedtime and the start time of one or more defined sleep stages.

[0030] In embodiments in which the one or more defined sleep stages constitute and / or include a defined amount of one or more sleep stages among the plurality of sleep stages, as described above, the computing device (e.g., wearable device 100) may calculate the TBSS metric by subtracting the user's estimated bedtime from the start time of the defined amount of such sleep stage(s) among the plurality of sleep stages. For example, in one embodiment in which the one or more defined sleep stages constitute and / or include, for example, 20 or more consecutive uninterrupted light sleep stage periods (e.g., 20 or more consecutive uninterrupted light sleep periods), the computing device may calculate the TBSS metric by subtracting the user's estimated bedtime from the start time of the 20 or more consecutive uninterrupted light sleep stage periods. For example, in this embodiment, the computing device may calculate the TBSS metric by subtracting the user's estimated bedtime from the start time of the first light sleep stage within the 20 or more consecutive uninterrupted light sleep stage periods.

[0031] In other embodiments where the one or more defined sleep stages constitute and / or include, for example, a deep sleep stage, the computing device may calculate the TBSS metric by subtracting the user's estimated bedtime from the start time of the first deep sleep stage of the user's sleep session. In other embodiments where the one or more defined sleep stages constitute and / or include, for example, a REM sleep stage, the computing device may calculate the TBSS metric by subtracting the user's estimated bedtime from the start time of the first REM sleep stage of the user's sleep session.

[0032] As described above, in an exemplary embodiment of the present disclosure, based at least in part on (e.g., in response to) calculating the TBSS metric, a computing device (e.g., wearable device 100) can perform one or more actions based at least in part on (e.g., using) the TBSS metric. For example, in one embodiment, based at least in part on (e.g., in response to) calculating the TBSS metric, the computing device can generate an intelligent notification (e.g., a visual notification and / or an audio notification), which can include and / or indicate the TBSS metric. In this or other embodiments, the computing device can further provide such intelligent notification to a user and / or other computing devices (e.g., different computing devices external to the computing device described above). For example, in one embodiment, the computing device can provide the intelligent notification and / or the TBSS metric to a user using one or more data output devices, such as, for example, a display device (e.g., monitor, screen, display) and / or a speaker, which can be included in, coupled to, and / or otherwise associated with the computing device.

[0033] In other embodiments described herein, a computing device (e.g., wearable device 100) can provide the above-described intelligent notifications and / or TBSS metrics to other computing devices (e.g., external and / or remote computing devices), such as, for example, other client computing devices, other computers, other laptops, other tablets, other smartphones, other physiological monitoring devices, other wearable computing devices, other wearable physiological monitoring devices (e.g., other wrist-worn devices, other chest strap devices), etc. In some embodiments, a computing device can provide the intelligent notifications and / or TBSS metrics to other computing devices and / or computing entities (e.g., modules, models, algorithms, agents) that function as and / or may associate with medical professionals and / or sleep counseling professionals (e.g., doctors, psychiatrists, sleep counselors).

[0034] In at least one embodiment of the present disclosure, based at least in part on (e.g., in response to) calculating the TBSS metric, a computing device (e.g., wearable device 100) can generate one or more sleep quality recommendations, for example, based at least in part on (e.g., using) the TBSS metric. For example, in this or other embodiments, the computing device can use the TBSS metric to determine (e.g., calculate) a suggested wind-down time during which a user should begin winding down, relaxing, and / or otherwise preparing to sleep to ensure the user will be able to fall asleep by a specific time. In this or other embodiments, the computing device can further provide to the user and / or other computing devices (e.g., different computing devices external to the computing device described above) intelligent notifications (e.g., visual and / or audio notifications) that can include and / or indicate the TBSS metric and / or one or more sleep quality recommendations. For example, in this or other embodiments, the computing device can provide to the user and / or other computing devices intelligent notifications that can include and / or indicate the TBSS metric and / or the suggested wind-down time described above in the same manner as described above.

[0035] In at least one embodiment described herein, based at least in part on (e.g., in response to) calculating the TBSS metric, the computing device (e.g., wearable device 100) may implement and / or facilitate the implementation of one or more sleep-promoting features of the computing device and / or other computing devices (e.g., different computing devices external to the computing device described above), e.g., based at least in part on the TBSS metric. For example, in this or other embodiments, the computing device may implement and / or facilitate the implementation of one or more sleep-promoting features of the computing device and / or other computing devices at the suggested wind-down times described above when the user should begin winding down, relaxing, and / or otherwise preparing to sleep to ensure the user will be able to fall asleep by a certain time.

[0036] In one embodiment of the present disclosure, a computing device (e.g., wearable device 100) may implement (e.g., initiate, execute, operate) one or more sleep-promoting features that may be included with the computing device, such as, for example, a sleep-promoting audio feature (e.g., by playing music and / or sounds that promote sleep), a sleep-promoting lighting feature (e.g., by initiating a "sleep mode" and / or a "night mode" of the computing device and dimming one or more light sources of the computing device, such as a screen, display, or monitor), and / or other sleep-promoting features of the computing device. For example, in this or other embodiments, the computing device may cause an audio system of the computing device to play music and / or sounds that promote sleep, and / or cause a lighting system of the computing device to initiate a "sleep mode" and / or a "night mode" and dim one or more light sources of the computing device, such as a screen, display, or monitor.

[0037] In other embodiments of the present disclosure, a computing device (e.g., wearable device 100) may facilitate implementation of one or more sleep-promoting functions of other computing devices, such as, for example, a sleep-promoting audio function of a smart audio system (e.g., a home audio system included in, coupled to, and / or operated by other computing devices), a sleep-promoting lighting function of a smart lighting system (e.g., a home lighting system included in, coupled to, and / or operated by other computing devices), a sleep-promoting ambient temperature function of a smart heating, ventilation, and air conditioning (HVAC) system (e.g., a home HVAC system included in, coupled to, and / or operated by other computing devices), and / or other sleep-promoting functions of other computing devices. For example, in this or other embodiments, the computing device may transmit instructions (e.g., via one or more processors) to one or more of the above-mentioned smart systems that, when executed by such system(s), can cause the system(s) to implement one or more sleep-promoting functions of such system(s).

[0038] In one embodiment of the present disclosure, a computing device (e.g., wearable device 100) can transmit instructions to a smart audio system described above (e.g., via one or more processors) that, when executed by such a system, can cause the system to play music and / or sounds that promote sleep. In other embodiments, a computing device can transmit instructions to a smart lighting system described above (e.g., via one or more processors) that, when executed by such a system, can cause the system to initiate a "sleep mode" and / or a "night mode" and dim one or more light sources (e.g., light bulbs) of the smart lighting system. In other embodiments, a computing device can transmit instructions to a smart HAVC system described above (e.g., via one or more processors) that, when executed by such a system, can cause the system to output air at a particular sleep-promoting temperature (e.g., a particular temperature that can be user-definable).

[0039] In at least one embodiment described herein, a computing device (e.g., wearable device 100) can record the TBSS metric and / or one or more additional TBSS metrics corresponding to the user in a database (e.g., in a log that can be stored in a memory device). In this embodiment, the one or more additional TBSS metrics can be calculated (e.g., by the computing device described above) based at least in part on (e.g., using) at least one additional plurality of sleep stages associated with one or more additional sleep sessions of the user. In some embodiments, the computing device can obtain and / or store in such a database one or more other TBSS metrics, each corresponding to one or more other users. In these or other embodiments, the computing device can compare the user's TBSS metric and / or additional TBSS metric(s) with other TBSS metric(s), each corresponding to one or more other users. In these or other embodiments, the computing device may further classify the user into a defined sleep pattern category (e.g., an insomnia measurement pattern category) based at least in part on a comparison of the user's TBSS metric and / or additional TBSS metric(s) with other TBSS metric(s), respectively, corresponding to other user(s). In some embodiments, to perform the above-described comparison and / or classification operations, the computing system may use one or more of the classifiers described above and / or other classifiers that may compare one or more TBSS metrics of the user with one or more TBSS metrics of one or more other users and classify the user into a defined sleep pattern category based on such comparison.

[0040] In at least one embodiment of the present disclosure, a computing device (e.g., wearable device 100) can identify a user's defined sleep pattern based at least in part on the TBSS metric and / or additional TBSS metrics corresponding to the user. For example, in this or other embodiments, by comparing one or more TBSS metrics of the user to one or more TBSS metrics of one or more other users and classifying the user into a defined sleep pattern category based on such comparison (e.g., via one or more classifiers), the computing device can thereby determine that the user's sleep pattern corresponds to a particular sleep pattern, such as, for example, an insomnia sleep pattern. In some embodiments, based at least in part on (e.g., in response to) identifying such a defined sleep pattern of the user, the computing device can further determine a defined sleep condition diagnosis and / or a defined sleep condition prognosis that can be associated with the user's sleep quality. For example, in these or other embodiments, based at least in part on (e.g., in response to) determining that the user's sleep pattern corresponds to the above-mentioned insomnia sleep pattern, the computing device can further diagnose the user as an insomnia sufferer.

[0041] Exemplary aspects of the present disclosure provide several technical effects, advantages, and / or improvements in computing technology. For example, according to exemplary embodiments of the present disclosure, a computing device such as wearable device 100 can calculate and use one or more TBSS metrics of a user described herein to accurately and consistently determine when the user is in a relaxed, restful state (e.g., watching television in bed, reading) and when the user is in a true sleep state (e.g., when the user enters the deep sleep state defined above).

[0042] In some embodiments, by calculating and using one or more TBSS metrics of a user described herein to accurately and consistently determine when the user is in a relaxed, restful state (e.g., watching TV in bed, reading) and when the user is in a true sleep state (e.g., when the user enters the deep sleep state defined above), the computing device (e.g., wearable device 100) can thereby reduce the processing workload of one or more processors that perform the operations to make such determinations. For example, in these or other embodiments, the computing device can thereby reduce the processing workload of one or more processors that can be included in and / or coupled to the computing device and / or other computing devices that are external to the computing device, such as other computing devices and / or computing entities (e.g., modules, models, algorithms, agents) that can function as and / or be associated with, for example, medical professionals and / or sleep counseling professionals (e.g., processors of other computing devices that can be used to conduct sleep studies, diagnose patients with various sleep conditions, and / or treat patients with such sleep conditions). In these or other embodiments, by reducing the processing workload of such one or more processors, the computing device may thereby improve the processing efficiency and / or processing performance of the processor(s) and reduce the computational costs of the processor(s).

[0043] Exemplary Devices and Systems 1, 2, and 3 each illustrate a perspective view of an exemplary, non-limiting wearable device 100 according to one or more exemplary embodiments of the present disclosure. In the exemplary embodiments described herein, wearable device 100 may constitute and / or include a wearable computing device. For example, in these or other exemplary embodiments, wearable device 100 may constitute and / or include a wearable computing device, such as a wearable physiological monitoring device, that can be worn by a user (also referred to herein as a “wearer”) and / or that can capture one or more types of user physiological data (e.g., heart rate data, pulse-based data, movement data, temperature data).

[0044] A wearable device 100 according to an exemplary embodiment of the present disclosure may include a display 102, a mounting component 104, a stationary component 106, and a button 108 that may be located on a side of the wearable device 100. In at least one embodiment, two sides of the display 102 may be (e.g., mechanically, operably) coupled to the mounting component 104. In some embodiments, the stationary component 106 may be located on, (e.g., mechanically, operably) coupled to, and / or integral with the mounting component 104. In these or other embodiments, the stationary component 106 may be positioned opposite the display 102 at opposing ends of the mounting component 104. In some embodiments, the button 108 may be located on the side of the wearable device 100, directly below the display 102.

[0045] The display 102 according to the exemplary embodiments described herein may comprise and / or include any type of electronic display or screen known in the art. For example, in some embodiments, the display 102 may comprise and / or include a liquid crystal display (LCD) or an organic light-emitting diode (OLED) display, such as a transmissive LCD display or a transmissive OLED display. The display 102 according to the exemplary embodiments may be configured to provide brightness, contrast, and / or color saturation characteristics according to a display setting that may be maintained by control circuitry and / or other internal components and / or circuitry of the wearable device 100. In some embodiments, the display 102 may comprise and / or include a touchscreen, such as a capacitive touchscreen. For example, in these embodiments, the display 102 may comprise and / or include a surface capacitive touchscreen or a projected capacitive touchscreen that may be configured to respond to contact with a charge-retaining member or tool, such as a human finger.

[0046] In some embodiments, display 102 can be configured to provide (e.g., render) various information such as, for example, the time, the date, body signals (e.g., physiological data of a user wearing wearable device 100), readings, etc., based on user input and / or other information. In one embodiment, such body signals can include, but are not limited to, heart rate data (e.g., beats per minute), pulse rate data, movement data (e.g., ambulation data), blood pressure data, body temperature data, oxygen level data, and / or any other body signal that can be measured by a wearable device, such as wearable device 100, as would be understood by one skilled in the art. In some embodiments, readings based on user input can include, but are not limited to, the number of steps taken by the user, the distance traveled by the user, the user's sleep schedule, the user's route traveled, the height climbed by the user, and / or other metrics that can be input by a user into a wearable device, such as wearable device 100, as would be understood by one skilled in the art.

[0047] In at least one embodiment of the present disclosure, the above-described body signals and / or readings based on user input can be used to calculate further analysis to provide the user with data such as, for example, a fitness score, a sleep quality score, TBSS metrics described herein, the number of calories burned by the user, and / or other data. In some embodiments, wearable device 100 may incorporate (e.g., incorporate, collect, receive, measure) external data, regardless of the user, such as, for example, the ambient temperature of the environment surrounding and / or external to wearable device 100, the amount of sunlight exposure to which wearable device 100 is exposed, the atmospheric pressure of the environment surrounding and / or external to wearable device 100, the air quality of the environment surrounding and / or external to wearable device 100, the location of wearable device 100, e.g., based on a Global Positioning System (GPS), and / or other external factors that a person skilled in the art would understand that a wearable device, such as wearable device 100, may incorporate (e.g., incorporate, collect, receive, measure).

[0048] Attachment component 104 according to example embodiments described herein can be used to attach (e.g., affix, fasten) wearable device 100 to a user of wearable device 100. In some embodiments, attachment component 104 can take the form of, for example, a strap, an elastic band, a rope, and / or any other attachment that one skilled in the art would understand can be used to attach a wearable device, such as wearable device 100, to a user.

[0049] The securing component 106, according to exemplary embodiments of the present disclosure, can facilitate attachment of the mounting component 104 to a user of the wearable device 100. In some embodiments, the securing component 106 can include, but is not limited to, a pin-and-hole locking mechanism (e.g., a buckle), a magnetic system, a lock, a clip, and / or any other type of fastening that one skilled in the art would understand can be used to facilitate attachment of a wearable device, such as, for example, the wearable device 100, to a user. In one embodiment, the wearable device 100 does not include the securing component 106. For example, in this or other embodiments, the wearable device 100 can be secured to the user using a strap that can be tied around the user's wrist and / or other suitable appendage.

[0050] Button 108, according to exemplary embodiments described herein, can enable a user to interact with wearable device 100 and / or provide a form of input to wearable device 100. In the exemplary embodiments shown in FIGS. 1, 2, and 3, one button 108 is shown on wearable device 100. However, it should be understood that wearable device 100 is not so limited. For example, in some embodiments, wearable device 100 can include any number of buttons that enable a user to further interact with wearable device 100 and / or provide alternative input. In at least one embodiment, wearable device 100 does not include button 108. For example, as described above, in exemplary embodiments, wearable device 100 can include a screen, such as a touchscreen, that can receive input through (e.g., via) a user's touch. In additional or alternative embodiments, wearable device 100 can include a microphone that can receive input through (e.g., via) a user's voice commands.

[0051] In some embodiments, wearable device 100 may constitute a portable computing device that can be designed to be insertable into a wearable case (e.g., as shown in the exemplary embodiments illustrated in FIGS. 1, 2, and 3). In some embodiments, wearable device 100 may constitute a portable computing device that can be designed to be insertable into one or more of a number of different wearable cases (e.g., a wristband case, a belt clip case, a pendant case, a case configured to attach to a piece of athletic equipment such as a bicycle). Wearable device 100 according to embodiments described herein can be formed in one or more shapes and / or sizes that allow for coupling (e.g., fastened, worn, carried) to a user's body or clothing. In some embodiments, wearable device 100 may constitute a portable computing device that can be designed to be worn in a limited manner, such as, for example, a computing device that can be integrated into a wristband in a non-removable manner and / or that can be specifically intended to be worn on a person's wrist (or perhaps ankle).

[0052] Regardless of the configuration, wearable device 100 according to exemplary embodiments of the present disclosure may include one or more physiological and / or environmental sensors (e.g., internal physiological sensor(s) 143, external physiological sensor(s) 145, and / or environmental sensor(s) 155, described below with reference to FIG. 4), which may be configured to collect physiological and / or environmental data in accordance with various embodiments disclosed herein. In some embodiments, wearable device 100 may be configured to analyze and / or interpret the collected physiological and / or environmental data to perform a sleep quality assessment of a user (e.g., a wearer) of wearable device 100 (e.g., by calculating the TBSS metrics described above in accordance with exemplary embodiments described herein) or to communicate with other computing devices or servers that may perform sleep quality management (e.g., by calculating the TBSS metrics described above in accordance with exemplary embodiments described herein).

[0053] A wearable device 100 according to one or more exemplary embodiments of the present disclosure may include one or more physiological and / or environmental components and / or modules that may be designed to determine one or more physiological and / or environmental metrics associated with a user (e.g., a wearer) of the wearable device 100. In at least one embodiment, such physiological and / or environmental component(s) and / or module(s) may comprise and / or include one or more physiological and / or environmental sensors. For example, although not shown in the exemplary embodiments illustrated in FIGS. 1, 2, and 3, in some embodiments, wearable device 100 may include one or more physiological and / or environmental sensors, such as, for example, an accelerometer, a heart rate sensor (e.g., a photoplethysmography (PPG) sensor), a body temperature sensor, an ambient temperature sensor, and / or other physiological and / or environmental sensors. In these or other embodiments, such physiological sensor(s) and / or environmental sensor(s) may be located on, coupled to, and / or otherwise associated with the underside or back side (e.g., back surface 134) of wearable device 100.

[0054] In some embodiments, the physiological sensor(s) and / or environmental sensor(s) described above may be positioned on, coupled to, and / or otherwise associated with wearable device 100 such that the sensor(s) may be in contact or substantially in contact with human skin when a user wears wearable device 100. For example, in embodiments in which wearable device 100 may be worn on a user's wrist, the physiological sensor(s) and / or environmental sensor(s) may be positioned on, coupled to, and / or otherwise associated with back surface 134, which may be substantially opposite display 102 and in contact with the user's arm. In one embodiment, the physiological sensor(s) and / or environmental sensor(s) described above may be positioned on, coupled to, and / or otherwise associated with the interior or skin-side of wearable device 100 (e.g., a side of wearable device 100 that contacts, touches, and / or faces the user's skin, such as back surface 134 and / or bottom surface 142). In other embodiments, physiological sensors and / or environmental sensors may be positioned on one or more sides of wearable device 100, including the skin side (e.g., back side 134, bottom side 142) and one or more sides of wearable device 100 (e.g., first side 136, second side 138, top side 140, display 102) that face and / or are exposed to the surrounding environment (e.g., the external environment surrounding wearable device 100).

[0055] 4 illustrates a block diagram of the above-described exemplary, non-limiting wearable device 100, according to one or more exemplary embodiments of the present disclosure. That is, for example, FIG. 4 illustrates a block diagram of one or more internal and / or external components of the above-described exemplary, non-limiting wearable device 100, according to one or more exemplary embodiments of the present disclosure.

[0056] As described above with reference to the exemplary embodiments shown in FIGS. 1, 2, and 3, wearable device 100 may constitute and / or include a wearable computing device, such as, for example, a wearable physiological monitoring device. For example, in the exemplary embodiment shown in FIG. 4, wearable device 100 may constitute and / or include a wearable physiological monitoring device that can be worn by user 10 (also referred to herein as “wearer” or “wearer 10”) and / or may be configured to collect data regarding activities performed by user 10 and / or regarding the physiological state of user 10. In this or other embodiments, such data may include data describing the ambient environment around user 10 or user 10's interaction with the environment. For example, in some embodiments, the data may constitute and / or include physiological data obtained by measuring user 10's movements, ambient light, ambient noise, air quality, and / or various physiological characteristics of user 10 (e.g., heart rate, pulse-based data, respiratory data, body temperature, blood oxygen level, sweat level).

[0057] While particular embodiments are disclosed herein in the context of wearable physiological monitoring devices, it should be understood that the present disclosure is not so limited. For example, it should be understood that the physiological monitoring and sleep quality assessment principles and features disclosed herein may be applicable with respect to and / or may be implemented using any suitable or desired type of computing device or combination of computing devices, whether wearable or not. For example, the calculation and / or application of TBSS metrics described herein according to one or more embodiments may be performed and / or implemented using any suitable or desired type of computing device or combination of computing devices, such as, for example, a client computing device, a laptop, a tablet, a server, a wearable computing device, and / or other computing device, whether wearable or not.

[0058] 4 , a wearable device 100 according to an exemplary embodiment of the present disclosure may include one or more audio and / or visual feedback components 130, such as, for example, an electronic touchscreen display unit, a light-emitting diode (LED) display unit, an audio speaker, a light-emitting diode (LED) light, a buzzer, and / or other types of audio and / or visual feedback modules. In particular embodiments, one or more audio and / or visual feedback modules 130 may be located on the front side of and / or otherwise associated with wearable device 100 and / or display 102. For example, in a wearable embodiment of wearable device 100, an electronic display, such as, for example, display 102, may be configured to be presented externally to user 10 viewing wearable device 100.

[0059] Wearable device 100 according to exemplary embodiments of the present disclosure may include control circuitry 110. While certain modules and / or components are shown in the diagram of FIG. 4 as part of control circuitry 110, it should be understood that control circuitry 110 associated with wearable device 100 according to exemplary embodiments of the present disclosure and / or other components or devices may include additional components and / or circuits, such as, for example, one or more components in addition to the illustrated components shown in FIG. 4. Furthermore, in certain embodiments, one or more of the illustrated components of control circuitry 110 may be omitted and / or may differ from those shown in and described in connection with FIG. 4.

[0060] The term "control circuitry" is used herein in accordance with its broad and / or ordinary meaning and may include any combination of software and / or hardware elements, devices, and / or features that may be implemented in connection with the operation of wearable device 100. Furthermore, the term "control circuitry" may be used herein, in certain contexts, substantially interchangeably with one or more of the terms "controller," "integrated circuit," "IC," "application-specific integrated circuit," "ASIC," "controller chip," etc.

[0061] Control circuitry 110 according to exemplary embodiments of the present disclosure may comprise and / or include one or more processors, data storage devices, and / or electrical connections. In one embodiment, control circuitry 110 may be implemented on a system-on-chip (SoC), although one skilled in the art will recognize that other hardware and / or firmware implementations are possible.

[0062] In one or more embodiments of the present disclosure, control circuitry 110 may comprise and / or include one or more processors 181 that can be configured to execute computer-readable instructions that, when executed, cause wearable device 100 to perform one or more operations. In at least one embodiment, control circuitry 110 may comprise and / or include processor(s) 181 that can be configured to execute operational code (e.g., instructions, processing threads, software) for wearable device 100, such as firmware. Each of processor(s) 181 according to example embodiments described herein may be a processing device. For example, in the example embodiment shown in FIG. 4, each of processor(s) 181 may be a central processing unit (CPU), microprocessor, microcontroller, integrated circuit (e.g., application-specific integrated circuit (ASIC)), and / or other type of processing device. In this or other exemplary embodiments, processor(s) 181 may be coupled (e.g., electrically, communicatively, physically, operatively) to control circuitry 110 and / or one or more components of wearable device 100, thereby enabling processor(s) 181 to facilitate one or more operations in accordance with one or more exemplary embodiments described herein.

[0063] In at least one embodiment of the present disclosure, the above-mentioned computer-readable instructions and / or operational code executable by processor(s) 181 may be stored in one or more data storage devices of wearable device 100. In the exemplary embodiment shown in FIG. 4, such computer-readable instructions and / or operational code may be stored in memory 183 of wearable device 100. In this or other exemplary embodiments, memory 183 may be coupled (e.g., electrically, communicatively, physically, operatively) to control circuitry 110 and / or one or more components of wearable device 100, such that memory 183 can facilitate one or more operations in accordance with one or more exemplary embodiments described herein.

[0064] Memory 183 according to example embodiments described herein can store computer-readable and / or computer-executable entities (e.g., data, information, applications, models, algorithms) that can be created, modified, accessed, read, retrieved, and / or executed by each of processor(s) 181. In some embodiments, memory 183 can constitute, include, be (e.g., operatively) coupled to, and / or otherwise associated with a computing system and / or media, such as, for example, one or more computer-readable media, volatile memory, non-volatile memory, random access memory (RAM), read-only memory (ROM), hard drives, flash drives, and / or other memory devices. In these or other embodiments, such one or more computer-readable media can comprise, constitute, be (e.g., operatively) coupled to, and / or otherwise associated with one or more non-transitory computer-readable media. Although not shown in the exemplary embodiment illustrated in FIG. 4 , in some embodiments, memory 183 may include (e.g., store) sleep assessment module 111, TBSS metric module 113, physiological metric module 141, physiological metric calculation module 142, and / or other modules that may be used for sleep and / or data that may be used to facilitate one or more operations described herein.

[0065] Control circuitry 110 according to exemplary embodiments of the present disclosure may configure and / or include a sleep assessment module 111. Sleep assessment module 111 according to exemplary embodiments of the present disclosure may configure and / or include one or more hardware and / or software components and / or features that may be configured to perform an assessment of the quality of sleep of user 10, optionally using input from one or more environmental sensors 155 (e.g., ambient light sensors) and / or information from physiological metrics module 141. In particular embodiments, sleep assessment module 111 may include a time before deep sleep metric module 113 (also referred to herein as “TBSS metric module 113”) that may be configured to calculate the TBSS metrics described above. For example, in these or other embodiments, wearable device 100 may implement TBSS metric module 113 to calculate TBSS metrics of user 10 as described herein in accordance with one or more embodiments of the present disclosure.

[0066] In one embodiment, wearable device 100 may implement TBSS metric module 113 to calculate a TBSS metric for user 10 by obtaining the above-mentioned multiple sleep stages and calculating a TBSS metric using such sleep stages, in accordance with one or more embodiments described herein. In other embodiments, wearable device 100 may implement TBSS metric module 113 to calculate a TBSS metric for user 10 by generating the above-mentioned multiple sleep stages and calculating a TBSS metric using such sleep stages, in accordance with one or more embodiments described herein. For example, in this embodiment, wearable device 100 may generate the multiple sleep stages using the above-mentioned classifier(s) and calculate a TBSS metric using such sleep stages, in accordance with one or more embodiments described herein. In this embodiment, the wearable device 100 may implement classifier(s) to generate multiple sleep stages using physiological data of the user 10 that may be accumulated by the sleep assessment module 111, such as values ​​of one or more physiological metrics (e.g., the user's 10 heart rate, movement, body temperature, respiration), which may be determined by the physiological metric calculation module 142 of the physiological metric module 141.

[0067] In certain embodiments, physiological metric module 141 and / or physiological metric calculation module 142 can be communicatively coupled to one or more internal physiological sensors 143 that can be built into and / or integrated with wearable device 100. In certain embodiments, physiological metric module 141 and / or physiological metric calculation module 142 can optionally communicate with one or more external physiological sensors 145 (e.g., electrodes or sensors integrated into other electronic devices) that are not built into and / or integrated with wearable device 100. In some embodiments, examples of internal physiological sensors 143 and / or external physiological sensors 145 can comprise and / or include, but are not limited to, one or more sensors that can measure (e.g., capture, collect, receive) physiological data of user 10, such as, for example, body temperature, heart rate, blood oxygen level, movement, respiration, and / or other physiological data of user 10.

[0068] In the exemplary embodiment shown in FIG. 4, wearable device 100 may include one or more data storage components 151 (shown in FIG. 4 as “Data Storage 151”). Data storage component(s) 151 according to exemplary embodiments may constitute and / or include any suitable or desirable type of data storage, such as, for example, solid-state memory, which may be volatile or non-volatile. In some embodiments, such solid-state memory of wearable device 100 may constitute and / or include any of a wide variety of technologies, such as, for example, flash integrated circuits, phase-change (PC) memory, phase-change (PC) random-access memory (RAM), programmable metallized cell RAM (PMC-RAM or PMCm), Ovonic Integrated Memory (OUM), resistive RAM (RRAM®), NAND memory, NOR memory, EEPROM, ferroelectric memory (FeRAM), MRAM, or other discrete NVM (non-volatile solid-state memory) chips. In some embodiments, data storage component(s) 151 may be used to store system data, such as operating system data and / or system configurations or parameters. In some embodiments, the wearable device 100 may include data storage utilized as a buffer and / or cache memory for operational use by the control circuitry 110.

[0069] The data storage component(s) 151 according to an exemplary embodiment may include various sub-modules, such as, for example, a sleep detection module (e.g., sleep assessment module 111, TBSS metric module 113) capable of detecting an attempt or onset of sleep by user 10, an information collection module (e.g., physiological metric module 141, physiological metric calculation module 142) capable of managing the collection of physiological and / or environmental data related to sleep quality assessment, a sleep quality metric calculation module (e.g., sleep assessment module 111, TBSS metric module 113) capable of determining the value of one or more sleep quality metrics described in this disclosure, a unified score detection module capable of determining a representation of a unified sleep quality score described in this disclosure, a presentation module capable of managing the presentation of sleep quality measurement information to user 10, a heart rate determination module capable of determining one or more types of heart rate pairs and / or patterns of user 10, a feedback management module for collecting and interpreting sleep quality feedback from user 10, and / or one or more other sub-modules.

[0070] Wearable device 100 according to example embodiments may further include a power storage module 153 (denoted as "power storage 153"), which may comprise and / or include a rechargeable battery, one or more capacitors, or other charge-retaining device(s). In some embodiments, power stored by power storage module 153 may be utilized by control circuitry 110 for operation of wearable device 100, such as powering display 102. In some embodiments, power storage module 153 may receive power via a host interface of wearable device 100 (e.g., via one or more host interface circuits and / or components 176 (denoted in FIG. 4 as "host interface 176") and / or other means.

[0071] Wearable device 100 according to example embodiments may further include one or more environmental sensors 155. In at least one embodiment, examples of such environmental sensors 155 may include, but are not limited to, sensors capable of determining and / or measuring, for example, ambient light, external temperature (but not body temperature), altitude, device location (e.g., Global Positioning System (GPS)), and / or other environmental data.

[0072] Wearable device 100 according to example embodiments may further include one or more connectivity components 170, which may include, for example, a wireless transceiver 172. Wireless transceiver 172 according to example embodiments may be communicatively coupled to one or more antenna devices 195, which may be configured to wirelessly transmit and / or receive data and / or power signals to and / or from wearable device 100 using, but not limited to, peer-to-peer, WLAN, and / or other cellular communications. For example, wireless transceiver 172 may be utilized to transfer data and / or power between wearable device 100 and an external computing device (not shown in FIG. 4 ) and / or an external host system (e.g., a server) that may be configured to interface with wearable device 100, such as, for example, an external client computing device (e.g., a smartphone, tablet, computer). In certain embodiments, wearable device 100 may include one or more host interface circuits and / or components 176 (shown in FIG. 4 as “host interface 176”), e.g., wired interface components that can communicatively couple wearable device 100 to receive data and / or power from and / or transmit data to the external computing devices (e.g., smartphones, tablets, computers, servers) described above.

[0073] Connectivity component(s) 170 according to example embodiments may further include one or more user interface components 174 (shown in FIG. 4 as “user interface 174”) that can be used by wearable device 100 to receive input data from user 10 and / or provide output data to user 10. In some embodiments, user interface component(s) 174 may be (e.g., operatively, communicatively) coupled to and / or otherwise associated with audio and / or visual feedback component(s) 130. For example, in these embodiments, display 102 of wearable device 100 may constitute and / or include a touchscreen display that may be configured to provide (e.g., render) output data to user 10 and / or receive user input from user contact with the touchscreen display using audio and / or visual feedback component(s) 130. In some embodiments, user interface component(s) 174 may further constitute and / or include one or more buttons or other input components or features.

[0074] Connectivity component(s) 170 according to example embodiments may further include host interface circuitry and / or component(s) 176, which may be, for example, an interface that wearable device 100 can use to communicate with the aforementioned external computing devices (e.g., smartphones, tablets, computers, servers, etc.) via wired or wireless connections. Host interface circuitry and / or component(s) 176 according to example embodiments may utilize and / or be otherwise associated with any suitable or desirable communication protocol and / or physical connector, such as, for example, Universal Serial Bus (USB), micro-interface, Wi-Fi, Bluetooth, FireWire, PCIe, etc. For wireless connections, host interface circuitry and / or component(s) 176 according to example embodiments may be incorporated with a wireless transceiver 172.

[0075] While specific functional modules and components are shown and described herein, it should be understood that authentication management functionality according to the present disclosure can be implemented using a number of different approaches. For example, in some embodiments, control circuitry 110 can comprise and / or include one or more processors (e.g., processor(s) 181) that can be controlled by computer-executable instructions that can be stored in memory (e.g., memory 183, data storage component(s) 151) to provide functionality as described herein. In other embodiments, such functionality can be provided in the form of one or more specially designed electrical circuits. In some embodiments, such functionality can be provided by one or more processors (e.g., processor(s) 181) that can be controlled by computer-executable instructions that can be stored in memory (e.g., memory 183, data storage component(s) 151) that can be coupled (e.g., communicatively, operably, electrically) to one or more specially designed electrical circuits. Various examples of hardware that can be used to implement the concepts outlined herein may include, but are not limited to, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), and general purpose microprocessors that may be coupled with memory that stores executable instructions for controlling the general purpose microprocessor.

[0076] 5 illustrates a diagram of an exemplary, non-limiting sleep quality management system 500 according to one or more exemplary embodiments of the present disclosure. The sleep quality management system 500 illustrated in FIG. 5 illustrates an exemplary, non-limiting networked relationship between a wearable device 100, an external computing device 504, and / or one or more smart systems 512 according to one or more embodiments.

[0077] As described above, wearable device 100 according to exemplary embodiments of the present disclosure can assess the sleep quality of user 10 and / or facilitate altering (e.g., improving) the sleep quality of user 10 based on such assessment. More specifically, wearable device 100 according to exemplary embodiments described herein can calculate a TBSS metric associated with user 10 and further use the TBSS metric to perform one or more operations that can facilitate altering (e.g., improving) the sleep quality of user 10. Thus, in certain embodiments described in the present disclosure, wearable device 100 can be capable of and / or configured to collect physiological sensor readings of user 10 and / or use such readings to calculate a TBSS metric.

[0078] However, in additional and / or alternative embodiments, wearable device 100, or other electronic and / or computing devices that can be used to detect physiological information of user 10, can be in communication with external computing device 504. In these or other embodiments, external computing device 504 can be configured to use such physiological information of user 10 to calculate a TBSS metric for user 10 (e.g., by obtaining or generating multiple sleep stages as described above and using such sleep stages to calculate a TBSS metric in accordance with one or more embodiments described herein). In these or other embodiments, external computing device 504 can further use the TBSS metric of user 10 to perform one or more actions that can facilitate altering (e.g., improving) the quality of sleep of user 10.

[0079] Wearable device 100 according to example embodiments can be configured to collect one or more types of physiological and / or environmental data using built-in sensors and / or external devices, as described throughout this disclosure, and to communicate or relay such information via one or more networks 506 to other devices. This includes, in some embodiments, relaying the information to devices capable of functioning as internet-accessible data sources, thus allowing the collected data to be viewed, for example, using a web browser or network-based application on external computing device 504. For example, while user 10 is attempting to fall asleep and / or is asleep and wearing wearable device 100, wearable device 100 can use one or more physiological sensors to calculate and optionally store user 10's heart rate, movement data, body temperature, and / or respiration. The wearable device 100 according to an exemplary embodiment can then transmit data representing the user's 10 heart rate, movement data, body temperature, and / or respiration via network(s) 506 to a web service, computer, mobile phone, and / or health station account, where the data can be stored, processed, and visualized by the user 10 and / or other entities (e.g., medical professionals).

[0080] While wearable device 100 is shown having a display in exemplary embodiments of the present disclosure, it should be understood that in some embodiments, wearable device 100 does not have any type of display unit. In some embodiments, wearable device 100 can have audio and / or visual feedback components, such as, for example, light-emitting diodes (LEDs), buzzers, speakers, and / or limited-function displays. Wearable device 100 according to exemplary embodiments can be configured to be attached to the body or clothing of user 10. For example, in these or other embodiments, wearable device 100 can be configured as a wrist bracelet, a watch, a ring, electrodes, a finger clip, a toe clip, a chest strap, an ankle strap, and / or a pocketable device. In additional or alternative embodiments, wearable device 100 can be incorporated into something that comes into contact with user 10, such as, for example, clothing, a mat that can be placed under user 10, a blanket, a pillow, and / or other accessories involved in sleep-engaging activities.

[0081] In one or more embodiments of the present disclosure, communication between the wearable device 100 and the external computing device 504 may be facilitated by network(s) 506. In some embodiments, the network(s) 506 may constitute and / or include, for example, one or more of an ad-hoc network, a peer-to-peer communication link, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the public switched telephone network (PSTN), a cellular telephone network, and / or any other type of network. In some embodiments, communication between the wearable device 100 and the external computing device 504 may also be performed by a direct wired connection. In these or other embodiments, this direct wired connection may be associated with any suitable or desirable communication protocol and / or physical connector, such as, for example, universal serial bus (USB), micro-interface, Wi-Fi, Bluetooth, FireWire, PCIe, etc.

[0082] In exemplary embodiments of the present disclosure, various computing devices can communicate with wearable device 100 to facilitate assessment and / or alteration (e.g., improvement) of sleep quality. While external computing device 504 is shown as a smartphone in the exemplary embodiment depicted in FIG. 5, it should be understood that the present disclosure is not so limited. For example, external computing device 504 according to exemplary embodiments can comprise and / or include, for example, a smartphone having a display 508 as depicted in FIG. 5, a personal digital assistant (PDA), a mobile phone, a tablet, a personal computer, a laptop computer, a smart television, a video game console, a server, and / or other computing device that may be external to wearable device 100.

[0083] The networked relationships illustrated in the exemplary embodiment shown in FIG. 5 demonstrate how, in some embodiments, an external computing device 504 can be implemented to calculate TBSS metrics associated with a user 10 and / or further use the TBSS metrics to perform one or more actions that can facilitate altering (e.g., improving) the quality of the user's 10 sleep. For example, in one embodiment, the user 10 can wear a wearable device 100 that can be equipped as a bracelet having one or more physiological sensors but no display. In this or other embodiments, throughout the user's 10 sleep session (e.g., over the course of a night), as the user 10 attempts to fall asleep and then enters a deep sleep state as defined herein, the wearable device 100 can record, for example, the user's 10 heart rate, movements, body temperature, respiration, and / or blood oxygen level, as well as room temperature and / or ambient lighting levels. In this or other embodiments, the wearable device 100 can periodically transmit such information to the external computing device 504 via the network(s) 506.

[0084] In additional and / or alternative embodiments, the wearable device 100 may store the collected physiological and / or environmental data described above and transmit this data to the external computing device 504 in response to a trigger, such as detecting that the user 10 is awake after a period of sleep. In some embodiments, the user 10 may be awake for a threshold period of time to set this trigger (e.g., awake for at least 10 minutes) and / or may be awake for a threshold period of time after experiencing a threshold period of sleep (e.g., awake for at least 5 minutes after having slept for at least 6 hours). In some embodiments, the trigger for calculating the TBSS metric for the user 10 may be the detection of a command executed by the external computing device 504, such as manual or automatic execution of instructions to synchronize the collected physiological and / or environmental data and calculate the TBSS metric (e.g., by obtaining or generating the multiple sleep stages described above and using such sleep stages to calculate the TBSS metric in accordance with one or more embodiments described herein).

[0085] In some embodiments, the external computing device 504 can present (e.g., provide, render) the TBSS metric (e.g., a time value rendered in minutes). For example, in these or other embodiments, the external computing device 504 can generate an intelligent notification 510 that can include the TBSS metric and / or one or more sleep quality recommendations (e.g., suggested relaxation times), which, if and / or when implemented by the user 10, can facilitate altering (e.g., improving) the quality of the user 10's sleep. In the exemplary embodiment shown in FIG. 5 , the external computing device 504 can render the intelligent notification 510 with the TBSS metric and sleep quality recommendation(s) on the display 508 so that the user 10 and / or other entities (e.g., medical professionals, sleep therapy providers, physicians, caregivers) can view such information.

[0086] In some embodiments, the external computing device 504 can calculate a TBSS metric for the user 10, determine one or more sleep quality recommendations based on (e.g., in response to) the TBSS metric, generate an intelligent notification 510 such that the intelligent notification 510 includes the TBSS metric and the sleep quality recommendation(s), and send this information back to the wearable device 100 over the network(s) 506 for presentation of such information to the user 10 and / or other entities (e.g., medical professionals, sleep therapy providers, physicians, caregivers) (e.g., via the display 102). Although not shown in the exemplary embodiment shown in FIG. 5, in some embodiments, the wearable device 100 can calculate a TBSS metric for the user 10, determine one or more sleep quality recommendations based on (e.g., in response to) the TBSS metric, generate an intelligent notification 510 such that the intelligent notification 510 includes the TBSS metric and the sleep quality recommendation(s), and render this information on the display 102 of the wearable device 100.

[0087] In at least one embodiment described herein, based at least in part on (e.g., in response to) calculating the TBSS metric of user 10, wearable device 100 and / or external computing device 504 may implement and / or facilitate the implementation of one or more sleep promotion features of wearable device 100 and / or external computing device 504, e.g., based at least in part on the TBSS metric of user 10. For example, in this or other embodiments, wearable device 100 and / or external computing device 504 may implement and / or facilitate the implementation of one or more sleep promotion features of wearable device 100 and / or external computing device 504, such as, for example, based at least in part on the TBSS metric of user 10. For example, in this or other embodiments, wearable device 100 and / or external computing device 504 may suggest recommended wind-down times (e.g., recommended times when user 10 should begin winding down, relaxing, and / or otherwise preparing to sleep to ensure that user 10 will be able to fall asleep by a particular time).

[0088] In one embodiment of the present disclosure, wearable device 100 and / or external computing device 504 may implement (e.g., initiate, execute, operate) one or more sleep promotion functions that may be included with wearable device 100 and / or external computing device 504, such as, for example, a sleep promotion audio function (e.g., by playing music and / or sounds that promote sleep), a sleep promotion lighting function (e.g., by initiating a "sleep mode" and / or a "night mode" of wearable device 100 and / or external computing device 504 and dimming one or more light sources of wearable device 100 and / or external computing device 504, such as a screen, display, or monitor), and / or other sleep promotion functions of wearable device 100 and / or external computing device 504. For example, in this or other embodiments, wearable device 100 and / or external computing device 504 may cause an audio system of wearable device 100 and / or external computing device 504 to play music and / or sounds that promote sleep, and / or cause a lighting system of wearable device 100 and / or external computing device 504 to initiate a "sleep mode" and / or a "night mode" to dim one or more light sources of wearable device 100 and / or external computing device 504, such as a screen, display, or monitor.

[0089] In other embodiments of the present disclosure, wearable device 100 and / or external computing device 504 may facilitate implementation of one or more sleep-promoting features in other computing devices, such as, for example, the computing devices of one or more smart systems 512. In this or other embodiments, smart system(s) 512 may constitute and / or include, but are not limited to, an audio system (e.g., a home audio system), a lighting system (e.g., a home lighting system), an HVAC system (e.g., a home HVAC system), and / or other systems that may be included in, coupled to, and / or operate by, computing devices other than wearable device 100 and / or external computing device 504. For example, in some embodiments, smart system(s) 512 may constitute and / or include a smart audio system, a smart lighting system, and / or a smart HVAC system. In these or other embodiments, the wearable device 100 and / or the external computing device 504 may facilitate implementation of one or more sleep-promoting features of the smart system(s) 512, such as, for example, a sleep-promoting audio feature of a smart audio system, a sleep-promoting lighting feature of a smart lighting system, a sleep-promoting ambient temperature feature of a smart HVAC system, and / or other sleep-promoting features of the smart system(s) 512.

[0090] In some embodiments described herein, wearable device 100 and / or external computing device 504 can transmit instructions to smart system(s) 512 (e.g., via one or more processors) that, when executed by such system(s), can cause the system(s) to implement one or more sleep-promoting features of such system(s). In one embodiment, wearable device 100 and / or external computing device 504 can transmit instructions to a smart audio system (e.g., via one or more processors) that, when executed by such system(s), can cause the system to play music and / or sounds that promote sleep. In other embodiments, wearable device 100 and / or external computing device 504 can transmit instructions to a smart lighting system (e.g., via one or more processors) that, when executed by such system, can cause the system to enter a "sleep mode" and / or a "night mode" and dim one or more light sources (e.g., light bulbs) of the smart lighting system. In other embodiments, the wearable device 100 and / or the computing device 504 may send instructions to a smart HAVC system that, when executed by such system (e.g., via one or more processors), may cause the system to output air at a particular sleep-promoting temperature (e.g., a particular temperature that may be defined by the user 10).

[0091] 6 illustrates a diagram of an exemplary, non-limiting sleep quality management system 600, according to one or more exemplary embodiments of the present disclosure. The sleep quality management system 600 illustrated in FIG. 6 illustrates an exemplary, non-limiting networked relationship between one or more wearable devices 100a, 100b, 100c, one or more external computing devices 504a, 504b, 504c, and / or a server system 604, according to one or more embodiments.

[0092] 6, wearable devices 100a, 100b, and 100c may each include the same features, structure, components, attributes, and / or functionality as wearable device 100. In this embodiment, each wearable device 100a, 100b, and 100c may be coupled to (e.g., worn by) a respective user 10a, 10b, and 10c. In this embodiment, external computing devices 504a (e.g., a laptop computer), 504b (e.g., a smartphone), and 504c (e.g., a personal computer) may each include the same features, structure, components, attributes, and / or functionality as external computing device 504.

[0093] In some embodiments of the present disclosure, network(s) 506 can couple (e.g., communicatively) one or more of wearable devices 100a, 100b, 100c to server system 604 and / or one or more of external computing devices 504a, 504b, 504c. In some embodiments, one or more of external computing devices 504a, 504b, 504c and / or one or more of wearable devices 100a, 100b, 100c can be interconnected in a local area network (LAN) 602 or other type of communication interconnection that can connect (e.g., communicatively) in network(s) 506. LAN 602 according to an example embodiment can interconnect one or more of external computing devices 504a, 504b, 504c and one or more of wearable devices 100a, 100b, 100c. In some embodiments, one or more of wearable devices 100a, 100b, 100c and / or one or more of external computing devices 504a, 504b, 504c can indirectly connect to (e.g., communicatively couple to) network(s) 506 and / or server system 604 via LAN 602. In some embodiments, one or more of wearable devices 100a, 100b, 100c can directly connect to (e.g., communicatively couple to) network(s) 506 and / or connect to indirect network(s) 506 via LAN 602. For example, in the exemplary embodiment shown in FIG. 6 , wearable device 100b can connect to (e.g., communicatively couple to) external computing device 504b (e.g., a smartphone) through, for example, a Bluetooth connection.In this embodiment, the external computing device 504b can be connected to (e.g., communicatively coupled to) the server system 604 via the network(s) 506, and the wearable device 100b can also be connected to (e.g., communicatively coupled to) the server system 604 via the network 506.

[0094] 6, the server system 604 may collect physiological and / or environmental sensor readings detected from one or more of the wearable devices 100a, 100b, 100c. In some embodiments, the server system 604 may also collect TBSS metrics of one or more users 10a, 10b, 10c from one or more of the wearable devices 100a, 100b, 100c and / or from one or more of the external computing devices 504a, 504b, 504c.

[0095] 6, because the wearable device 100a is not associated with an external computing device, the wearable device 100a can transmit physiological data collected during the user's 10a sleep session to the server system 604. In this embodiment, the server system 604 can analyze the received data to calculate a TBSS metric for the user 10a (e.g., by obtaining or generating the above-mentioned multiple sleep stages in accordance with one or more embodiments described herein and using such sleep stages to calculate a TBSS metric for the user 10a). In this embodiment, the server system 604 can send an intelligent notification, the user's 10a TBSS metric, and / or one or more sleep quality recommendations back to the wearable device 100a.

[0096] 6, wearable device 100b can transmit physiological data collected during a sleep session of user 10b to server system 604 and external computing device 504a. In this embodiment, external computing device 504a can analyze the received data to calculate TBSS metrics for user 10b (e.g., by obtaining or generating the above-mentioned multiple sleep stages in accordance with one or more embodiments described herein and using such sleep stages to calculate TBSS metrics for user 10b). In this embodiment, server system 604 can use the received physiological data of user 10b to update a user profile for user 10b, which can be stored in profile database 612 (e.g., a log), which can be stored in memory 608, which can be included in, coupled to, and / or otherwise associated with server system 604.

[0097] In some embodiments, server system 604 may be implemented on one or more standalone data processing devices or a distributed network of computers. In some embodiments, server system 604 may also use various virtual devices and / or services from third-party service providers (e.g., third-party cloud service providers) to provide the underlying computing and / or infrastructure resources for server system 604. In some embodiments, server system 604 may include, but is not limited to, a handheld computer, a tablet computer, a laptop computer, a desktop computer, or a combination of any two or more of these or other data processing devices.

[0098] Server system 604 according to an example embodiment may include one or more processors or processing units 606 (denoted in FIG. 6 as “processor(s) 606”), such as, for example, one or more CPUs. In these or other embodiments, server system 604 may include one or more network interfaces 614, which may include, for example, input / output (I / O) interfaces to, for example, external computing devices 504a, 504b, and / or 504c and / or wearable devices 100a, 100b, and / or 100c. In some embodiments, server system 604 may include memory 608 and one or more communication buses for interconnecting these components.

[0099] Memory 608 according to example embodiments may include high-speed random-access memory such as, for example, DRAM, SRAM, DDR RAM, or other random-access solid-state memory devices, and may optionally include non-volatile memory such as, for example, one or more magnetic disk storage devices, one or more optical disk storage devices, one or more flash memory devices, or one or more other non-volatile solid-state storage devices. Memory 608 according to example embodiments may optionally include one or more storage devices that may be located remotely from the processor(s) or processing unit(s) 606. Memory 608 according to example embodiments, or alternatively, the non-volatile memory within memory 608, may include a non-transitory computer-readable storage medium. In some embodiments, memory 608, or the non-transitory computer-readable storage medium of memory 608, may store one or more programs, modules, and data structures. In these embodiments, such programs, modules, and data structures may include, but are not limited to, one or more of: an operating system, which may include procedures for handling various basic system services and for performing hardware-dependent tasks; a network communication module for connecting the server system 604 to other computing devices (e.g., wearable devices 100a, 100b, and / or 100c, and / or external computing devices 504a, 504b, and 504c) connected to the network(s) 506 via network interface(s) 614 (e.g., wired or wireless).

[0100] 4, which can use collected physiological and / or environmental data (e.g., received from one or more wearable devices 100a, 100b, 100c or one or more external computing devices 504a, 504b, 504c) of one or more users 10a, 10b, 10c to calculate TBSS metrics corresponding to each user 10a, 10b, 10c. In one embodiment, the server system 604 can implement the TBSS metric module 113 to calculate TBSS metrics for each user 10a, 10b, 10c by obtaining the above-mentioned sleep stages of each user 10a, 10b, 10c and calculating each TBSS metric using such sleep stages, in accordance with one or more embodiments described herein. In other embodiments, the server system 604 may implement the TBSS metric module 113 to calculate the TBSS metric for each of the users 10a, 10b, 10c by generating the above-mentioned multiple sleep stages for each of the users 10a, 10b, 10c and using such sleep stages to calculate each TBSS metric, in accordance with one or more embodiments described herein. For example, in this embodiment, the server system 604 may use the above-mentioned classifier(s) to generate the above-mentioned multiple sleep stages for each of the users 10a, 10b, 10c and using such sleep stages to calculate each TBSS metric for each of the users 10a, 10b, 10c, in accordance with one or more embodiments described herein. In this embodiment, the server system 604 may implement classifier(s) to generate multiple sleep stages for each user 10a, 10b, 10c using physiological data (e.g., heart rate, movement, body temperature, respiration) of each user 10a, 10b, 10c that may be captured, collected, and / or measured by the wearable device 100a, 100b, 100c, respectively.

[0101] The memory 608, according to an example embodiment, may also include a profile database 612 that may store user profiles for the users 10a, 10b, 10c. In some embodiments, the user profiles for each of the users may include, for example, a user identifier (e.g., an account name or handle), login credentials (e.g., login credentials to the sleep quality management system 600), an email address or preferred contact information, wearable device information (e.g., a model number), demographic parameters of the user (e.g., age, gender, occupation), the user's past sleep quality information, the user's past TBSS metrics, and / or the user's identified sleep quality tendencies (e.g., being a particularly restless sleeper).

[0102] In some embodiments, the collected physiological information of multiple users, e.g., users 10a, 10b, and 10c, can provide more robust population-normalized sleep metrics. For example, user 10a may be a 35-year-old female veterinarian, and user 10b may be a 34-year-old female veterinarian, and their respective past sleep quality physiological data and / or metrics can be used in determining one or more population-normalized sleep quality metrics due to their closely matched demographic characteristics. In some embodiments, users can opt in or out of contributing their sleep quality assessment information to other users' population normalization determinations. In some embodiments, a user's sleep quality information can be incorporated into the population-normalized sleep quality metric information used to determine that user's own values ​​for one or more sleep quality metrics.

[0103] In at least one embodiment described herein, the server system 604 can record TBSS metrics corresponding to the users 10a, 10b, and 10c in the profile database 612. In this embodiment, the server system 604 can compare the TBSS metrics of a particular user 10a, 10b, or 10c with the TBSS metrics of other users and further classify such particular user into a defined sleep pattern category (e.g., an insomnia sleep pattern category) based at least in part on such comparison of the users' TBSS metrics. In some embodiments, to perform the above-described comparison and / or classification operations, the server system 604 can use one or more of the classifiers described above and / or other classifiers that can compare one or more TBSS metrics of a particular user with one or more TBSS metrics of one or more other users and classify such particular user into a defined sleep pattern category based on such comparison.

[0104] In at least one embodiment of the present disclosure, the server system 604 can identify a defined sleep pattern for a particular user 10a, 10b, or 10c based at least in part on TBSS metrics and / or historical TBSS metrics corresponding to such particular user. For example, in this or other embodiments, by comparing one or more TBSS metrics of such particular user with one or more TBSS metrics of one or more other users and classifying such particular user into a defined sleep pattern category based on such comparison (e.g., via one or more classifiers), the server system 604 can thereby determine that such particular user's sleep pattern corresponds to a particular sleep pattern, such as, for example, an insomnia sleep pattern. In some embodiments, based at least in part on (e.g., in response to) identifying such a defined sleep pattern for such particular user, the server system 604 can further determine a defined sleep condition diagnosis and / or a defined sleep condition prognosis that can be associated with the sleep quality of such particular user. For example, in these or other embodiments, based at least in part on (e.g., in response to) determining that such particular user's sleep patterns correspond to the above-described insomnia sleep patterns, server system 604 may further diagnose such particular user as an insomnia sufferer.

[0105] 7A-7C illustrate diagrams of exemplary, non-limiting sleep stages 700a, 700b, and 700c, respectively, according to one or more exemplary embodiments of the present disclosure. The sleep stages 700a, 700b, and 700c illustrated in the exemplary embodiments shown in Figures 7A, 7B, and 7C can each constitute and / or include multiple sleep stages described herein that can be associated with a user's sleep session (e.g., a sleep session of user 10 that may last, for example, one hour or more).

[0106] 7A, 7B, and 7C, sleep stages 700a, 700b, and 700c can each include, for example, a wake stage (represented by a black box in FIGS. 7A, 7B, and 7C), a light sleep stage (represented by a light gray box in FIGS. 7A, 7B, and 7C), a deep sleep stage (represented by a dark gray box in FIGS. 7A, 7B, and 7C), and a REM sleep stage (represented by a white box in FIGS. 7A, 7B, and 7C). In these embodiments, sleep stages 700a, 700b, and 700c can each include, for example, a wake stage in which the user is awake, a light sleep stage in which the user is in a quiet pre-sleep state and / or a relaxed, still state, a deep sleep stage in which the user is in a true, continuous, and / or high-quality sleep state, and / or a REM sleep stage in which the user is experiencing REM and therefore in a true, continuous, and / or high-quality sleep state.

[0107] In at least one embodiment of the present disclosure, one or more defined sleep stages of sleep stages 700a, 700b, 700c can indicate a defined sleep state of a user (e.g., a deep sleep state as defined above). In this or other embodiments, such defined sleep stage(s) can comprise and / or include a defined amount of one or more of the sleep stages of sleep stages 700a, 700b, 700c. For example, in one embodiment, the one or more defined sleep stages can comprise and / or include a defined amount (e.g., 10, 20, 30) of light sleep stages, a defined amount (e.g., 1, 2) of deep sleep stages, and / or a defined amount (e.g., 1, 2) of REM sleep stages.

[0108] In some embodiments described herein, each sleep stage of sleep stages 700a, 700b, 700c, and each defined sleep stage of the one or more defined sleep stages, can be defined by a specific time interval (e.g., 30 seconds, 1 minute, 2 minutes) such that each sleep stage of sleep stages 700a, 700b, 700c is defined by the same duration (e.g., 30 seconds, 1 minute, 2 minutes). In some embodiments described herein, each sleep stage of sleep stages 700a, 700b, 700c, and each defined sleep stage of the one or more defined sleep stages can be defined by a specific time interval (e.g., 30 seconds, 1 minute, 2 minutes) that corresponds to a discrete time interval (e.g., 30 seconds, 1 minute, 2 minutes) in the user's sleep session.

[0109] 7A, 7B, and 7C, each sleep stage 700a, 700b, and 700c may be one minute in duration. That is, for example, in these exemplary embodiments, each wake stage, each light sleep stage, each deep sleep stage, and each REM sleep stage shown in Figures 7A, 7B, and 7C may be one minute in duration. Thus, in these exemplary embodiments, each rectangle representing a sleep stage in sleep stages 700a, 700b, and 700c may represent one minute in duration.

[0110] In one or more embodiments of the present disclosure, the time at which a particular light sleep stage (e.g., a first light sleep stage) begins in a user's sleep session can correspond to the user's estimated bedtime as defined above. In some embodiments, the time at which one or more defined sleep stages begin can correspond to the time at which the user enters a defined sleep state (e.g., a deep sleep state as defined above).

[0111] In the exemplary embodiment shown in FIG. 7A , the one or more defined sleep stages described above may constitute and / or include a specified amount of light sleep stages, such as, for example, 20 light sleep stages (e.g., 20 consecutive light sleep stages). For example, in the exemplary embodiment shown in FIG. 7A , the one or more defined sleep stages described above may constitute and / or include 20 consecutive uninterrupted light sleep stages in the user's sleep session. Thus, in this exemplary embodiment or other embodiments, the one or more defined sleep stages may constitute and / or include a 20-minute uninterrupted period of light sleep. In this embodiment or other embodiments, the time at which the first light sleep stage begins in the user's sleep session (e.g., T0 in FIG. 7A ) may correspond to the user's estimated bedtime as defined above. In this embodiment or other embodiments, the time at which the first light sleep stage begins within the period of 20 consecutive uninterrupted light sleep stages (e.g., T1 in FIG. 7A ) may correspond to the time at which the user enters the deep sleep state as defined above. To calculate the TBSS metric 702a shown in the exemplary embodiment shown in FIG. 7A, a computing device described herein (e.g., wearable device 100, 100a, 100b, and / or 100c, external computing device 504, 504a, 504b, and / or 504c, and / or server system 604) may subtract the time at which the first sleep stage begins in the user's sleep session (e.g., T0 in FIG. 7A) from the time at which the first light sleep stage begins in the period of 20 consecutive uninterrupted light sleep stages in the user's sleep session (e.g., T1 in FIG. 7A).

[0112] In the exemplary embodiment shown in FIG. 7B , the one or more defined sleep stages described above can constitute and / or include a defined amount of deep sleep stages, such as, for example, one deep sleep stage. In this or other embodiments, the time at which a first light sleep stage begins in the user's sleep session (e.g., T0 in FIG. 7B ) can correspond to the user's estimated bedtime as defined above. In this or other embodiments, the time at which a deep sleep stage (e.g., a first deep sleep stage) begins in the user's sleep session (e.g., T1 in FIG. 7B ) can correspond to the time at which the user enters the deep sleep state as defined above. To calculate the TBSS metric 702b shown in the exemplary embodiment illustrated in FIG. 7B, a computing device described herein (e.g., wearable device 100, 100a, 100b, and / or 100c, external computing device 504, 504a, 504b, and / or 504c, and / or server system 604) may subtract the time at which a first light sleep stage begins in a user's sleep session (e.g., T0 in FIG. 7B) from the time at which a deep sleep stage (e.g., the first deep sleep stage) begins in a user's sleep session (e.g., T1 in FIG. 7B).

[0113] In the exemplary embodiment shown in FIG. 7C , the one or more defined sleep stages described above may constitute and / or include a defined amount of REM sleep stages, such as, for example, one REM sleep stage. In this or other embodiments, the time at which a first light sleep stage begins in a user's sleep session (e.g., T0 in FIG. 7C ) may correspond to the user's estimated bedtime as defined above. In this or other embodiments, the time at which a REM sleep stage (e.g., a first REM sleep stage) begins in a user's sleep session (e.g., T1 in FIG. 7C ) may correspond to the time at which the user enters deep sleep as defined above. To calculate the TBSS metric 702c shown in the exemplary embodiment illustrated in FIG. 7C, a computing device described herein (e.g., wearable device 100, 100a, 100b, and / or 100c, external computing device 504, 504a, 504b, and / or 504c, and / or server system 604) may subtract the time at which the first light sleep stage begins in the user's sleep session (e.g., T0 in FIG. 7C) from the time at which the REM sleep stage (e.g., the first REM sleep stage) begins in the user's sleep session (e.g., T1 in FIG. 7C).

[0114] Exemplary Methods 8 illustrates a flow diagram of an exemplary, non-limiting computer-implemented method 800 according to one or more exemplary embodiments of the present disclosure. The computer-implemented method 800 can be implemented using, for example, wearable device 100, 100a, 100b, 100c, 504, 504a, 504b, 504c, or 604 described above with reference to the exemplary embodiments illustrated in FIGS. 1, 2, 3, 4, 5, and 6.

[0115] 8 shows operations performed in a particular order for purposes of illustration and description. Those skilled in the art, using the disclosure provided herein, will understand that the various operations or steps of computer-implemented method 800, or any of the other methods disclosed herein, can be adapted, modified, rearranged, performed concurrently, may include operations not shown, and / or can be modified in various ways without departing from the scope of the present disclosure.

[0116] At 802, the computer-implemented method 800 may include acquiring, by a computing device (e.g., wearable device 100, 100a, 100b, and / or 100c, external computing device 504, 504a, 504b, and / or 504c, and / or server system 604) operatively coupled to one or more processors (e.g., processor(s) 181, processor(s) 606), (e.g., via network(s) 506, LAN 602) a plurality of sleep stages (e.g., sleep stages 700a, 700b, or 700c) associated with a sleep session of a user (e.g., user 10), wherein the sleep session is defined at least in part by the user's estimated bedtime (e.g., T0 of FIG. 7A, 7B, or 7C).

[0117] At 804, the computer-implemented method 800 may include identifying, by the computing device, one or more defined sleep stages (e.g., a defined amount (e.g., 10, 20, 30) of light sleep stages, a defined amount (e.g., 1, 2) of deep sleep stages, and / or a defined amount (e.g., 1, 2) of REM sleep stages) among the plurality of sleep stages that indicate the user's defined sleep state (e.g., a deep sleep state as defined above).

[0118] At 806, the computer-implemented method 800 may include calculating, by the computing device, a time before deep sleep metric (e.g., a TBSS metric defined herein) based at least in part on the user's estimated bedtime (e.g., T0 in FIG. 7A, 7B, or 7C) and the start time of one or more defined sleep stages (e.g., T1 in FIG. 7A, 7B, or 7C).

[0119] At 810, the computer-implemented method 800 may include performing, by the computing device, one or more operations (e.g., generating and / or providing intelligent notifications, TBSS metrics, and / or one or more sleep quality recommendations to the user and / or other computing devices, implementing one or more sleep promotion features of the computing device, other computing devices, and / or smart systems defined above, and / or identifying a defined sleep pattern of the user and determining a defined sleep condition diagnosis or a defined sleep condition prognosis based at least in part on the time before deep sleep metrics).

[0120] Additional Disclosures The technology described herein refers to servers, databases, software applications, and other computer-based systems, as well as actions performed by and information sent to and from such systems. The inherent flexibility of computer-based systems allows for a wide variety of possible configurations, combinations, and divisions of tasks and functionality among components. For example, the processes discussed herein can be implemented using a single device or component, or multiple devices or components working in combination. Databases and applications can be implemented on a single system or distributed across multiple systems. Distributed components can operate sequentially or in parallel.

[0121] While the subject matter of the present disclosure has been described in detail with respect to various specific exemplary embodiments thereof, each example is provided for purposes of illustration and not limitation of the present disclosure. Those skilled in the art, once they arrive at the foregoing understanding, will be able to readily create modifications, variations, and equivalents to such embodiments. Accordingly, the disclosure of the subject matter does not exclude the inclusion of such modifications, variations, and / or additions to the subject matter as would be readily apparent to one of ordinary skill in the art. For example, features illustrated and / or described as part of one embodiment can be used with other embodiments to yield still other embodiments. Accordingly, the present disclosure is intended to cover such modifications, variations, and equivalents.

Claims

1. 1. A computing device comprising: one or more processors; one or more computer-readable media storing instructions that, when executed by the one or more processors, cause the computing device to perform operations; and the operation comprises: obtaining a plurality of sleep stages associated with a sleep session of the user defined at least in part by the user's estimated bedtime; identifying one or more defined sleep stages in the plurality of sleep stages that indicate a defined sleep state of the user; calculating a time before deep sleep metric based at least in part on the estimated bedtime of the user and a start time of the one or more defined sleep stages; and performing one or more actions based at least in part on the time before deep sleep metric.

2. The computing device of claim 1 , wherein the time before deep sleep metric indicates the period between the user's estimated bedtime and the time the user enters the defined sleep state.

3. the plurality of sleep stages include at least one of a wake stage, a light sleep stage, a deep sleep stage, or a rapid eye movement sleep stage; 2. The computing device of claim 1, wherein the one or more defined sleep stages include a defined amount of at least one of the light sleep stages, the deep sleep stages, or the rapid eye movement sleep stages.

4. Calculating the time before deep sleep metric based at least in part on the estimated bedtime of the user and the start times of the one or more defined sleep stages includes:

10. The computing device of claim 1, further comprising: calculating a time difference between the estimated bedtime of the user and the start time of the one or more defined sleep stages.

5. Performing the one or more actions based at least in part on the time before deep sleep metric includes: generating an intelligent notification including the time before deep sleep metric; and providing the intelligent notification to at least one of the user or a second computing device.

6. Performing the one or more actions based at least in part on the time before deep sleep metric includes: generating one or more sleep quality recommendations based at least in part on the time before deep sleep metric; and providing an intelligent notification to at least one of the user or a second computing device, the notification including at least one of the time before deep sleep metric or the one or more sleep quality recommendations.

7. Performing the one or more actions based at least in part on the time before deep sleep metric includes:

10. The computing device of claim 1, further comprising: implementing one or more sleep promoting features of at least one of the computing device or a second computing device based at least in part on the time before deep sleep metric.

8. Performing the one or more actions based at least in part on the time before deep sleep metric includes: recording in a database at least one of the time before deep sleep metric corresponding to the user or one or more additional time before deep sleep metrics calculated based at least in part on at least one additional plurality of sleep stages associated with one or more additional sleep sessions of the user; The computing device of claim 1 , comprising:

9. Performing the one or more actions based at least in part on the time before deep sleep metric includes: comparing the time before deep sleep metric or at least one of the one or more additional time before deep sleep metrics with one or more second time before deep sleep metrics corresponding to one or more second users, respectively; and classifying the user into a defined sleep pattern category based at least in part on a comparison of the time before deep sleep metric or at least one of the one or more additional time before deep sleep metrics to the one or more second time before deep sleep metrics.

10. Performing the one or more actions based at least in part on the time before deep sleep metric includes: identifying a defined sleep pattern for the user based at least in part on at least one of the time before deep sleep metric or one or more additional time before deep sleep metrics corresponding to the user; and determining at least one of a defined sleep condition diagnosis or a defined sleep condition prognosis associated with the user's sleep quality based at least in part on the defined sleep pattern.

11. 1. A computer-implemented method for assessing sleep quality and facilitating changes in sleep quality, comprising: obtaining, by a computing device operatively coupled to one or more processors, a plurality of sleep stages associated with a sleep session of the user defined at least in part by an estimated bedtime of the user; identifying, by the computing device, one or more defined sleep stages within the plurality of sleep stages that indicate a defined sleep state of the user; calculating, by the computing device, a time before deep sleep metric based at least in part on the estimated bedtime of the user and a start time of the one or more defined sleep stages; and performing, by the computing device, one or more actions based at least in part on the time before deep sleep metric.

12. The computer-implemented method of claim 11 , wherein the time before deep sleep metric indicates the period between the user's estimated bedtime and the time the user enters the defined sleep state.

13. the plurality of sleep stages include at least one of a wake stage, a light sleep stage, a deep sleep stage, or a rapid eye movement sleep stage; 12. The computer-implemented method of claim 11, wherein the one or more defined sleep stages include a defined amount of at least one of the light sleep stages, the deep sleep stages, or the rapid eye movement sleep stages.

14. Calculating, by the computing device, the time before deep sleep metric based at least in part on the estimated bedtime of the user and the start times of the one or more defined sleep stages, includes:

12. The computer-implemented method of claim 11, comprising calculating, by the computing device, a time difference between the estimated bedtime of the user and the start time of the one or more defined sleep stages.

15. Performing, by the computing device, the one or more actions based at least in part on the time before deep sleep metric includes: generating, by the computing device, an intelligent notification including the time before deep sleep metric; and providing, by the computing device, the intelligent notification to at least one of the user or a second computing device.

16. Performing, by the computing device, the one or more actions based at least in part on the time before deep sleep metric includes: generating, by the computing device, one or more sleep quality recommendations based at least in part on the time before deep sleep metric; and providing, by the computing device, to at least one of the user or a second computing device, an intelligent notification including at least one of the time before deep sleep metric or the one or more sleep quality recommendations.

17. Performing, by the computing device, the one or more actions based at least in part on the time before deep sleep metric includes:

12. The computer-implemented method of claim 11, comprising: implementing, by the computing device, one or more sleep promotion features of at least one of the computing device or a second computing device based at least in part on the time before deep sleep metric.

18. Performing, by the computing device, the one or more actions based at least in part on the time before deep sleep metric includes: recording, by the computing device, in a database, at least one of the time before deep sleep metric corresponding to the user or one or more additional time before deep sleep metrics calculated based at least in part on at least one additional plurality of sleep stages associated with one or more additional sleep sessions of the user; comparing, by the computing device, the time before deep sleep metric or at least one of the one or more additional time before deep sleep metrics with one or more second time before deep sleep metrics corresponding to one or more second users, respectively; and classifying, by the computing device, the user into a defined sleep pattern category based at least in part on a comparison of the time before deep sleep metric or at least one of the one or more additional time before deep sleep metrics to the one or more second time before deep sleep metrics.

19. Performing, by the computing device, the one or more actions based at least in part on the time before deep sleep metric includes: identifying, by the computing device, a defined sleep pattern for the user based at least in part on the time before deep sleep metric; and determining, by the computing device, at least one of a defined sleep condition diagnosis or a defined sleep condition prognosis associated with the user's sleep quality based at least in part on the defined sleep pattern.

20. One or more computer-readable media storing instructions that, when executed by one or more processors of a computing device, cause the computing device to perform operations, the operations including: obtaining a plurality of sleep stages associated with a sleep session of the user defined at least in part by the user's estimated bedtime; identifying one or more defined sleep stages in the plurality of sleep stages that indicate a defined sleep state of the user; calculating a time before deep sleep metric based at least in part on the estimated bedtime of the user and a start time of the one or more defined sleep stages; and performing one or more actions based at least in part on the time before deep sleep metric.

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