Techniques for determining circadian time pattern

Abstract

Methods, systems, and devices are described for determining a circadian time pattern. The system may be configured to receive a first set of physiological data collected over a period of time and to receive a second set of physiological data collected during a previous sleep day. Further, the system may be configured to classify the first set of physiological data as circadian timeforms using a machine learning model. The system may then compare the determined circadian time pattern with the received second set of physiological data. The system may cause a graphical user interface of the user device to display messages related to the comparison, the determined circadian time pattern, the received second set of physiological data, or a combination thereof.

Description

[0001] Cross-references

[0002] This patent application claims the benefit of U.S. non-provisional patent application No. 18 / 320,943, filed by KARSIKAS et al. on May 19, 2023, entitled “TECHNIQUES FOR DETERMINIG ACIRCADIAN RHYTHM CHRONOTYPE,” which claims the benefit of U.S. provisional patent application No. 63 / 344,800, filed by KARSIKAS et al. on May 23, 2022, entitled “TECHNIQUES FOR DETERMINIG ACIRCADIAN RHYTHM CHRONOTYPE,” which has been assigned to its assignee and is expressly incorporated herein by reference. Technical Field

[0003] The following content relates to wearable devices and data processing, including techniques for determining circadian chronotype. Background Art

[0004] Some wearable devices may be configured to collect data related to body temperature and heart rate from a user. For example, some wearable devices may be configured to determine a user's chronotype associated with one or more physiological parameters or characteristics. However, conventional chronotype techniques implemented by wearable devices may be limited in practicality because they may only consider a limited number of inputs or variables, resulting in inaccurate chronotype classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1 An example of a system supporting techniques for determining circadian chronotype in accordance with aspects of the present disclosure is shown.

[0006] Figure 2 An example of a system supporting techniques for determining circadian chronotype in accordance with aspects of the present disclosure is shown.

[0007] Figure 3 An example of a system supporting techniques for determining circadian chronotype in accordance with aspects of the present disclosure is shown.

[0008] Figure 4 An example of a timing diagram supporting techniques for determining circadian chronotype in accordance with aspects of the present disclosure is shown.

[0009] Figure 5 An example of a graphical representation of techniques supporting determination of circadian chronotype in accordance with aspects of the present disclosure is shown.

[0010] Figure 6 An example of a system supporting techniques for determining circadian chronotype in accordance with aspects of the present disclosure is shown.

[0011] Figure 7 An example of a graphical representation of techniques supporting determination of circadian chronotype in accordance with aspects of the present disclosure is shown.

[0012] Figure 8 An example of a graphical user interface (GUI) supporting techniques for determining circadian chronotype in accordance with aspects of the present disclosure is shown.

[0013] Fig. 9 A block diagram of an apparatus supporting techniques for determining circadian chronotype in accordance with aspects of the present disclosure is shown.

[0014] Fig.10 A block diagram of a wearable application supporting techniques for determining circadian chronotype in accordance with aspects of the present disclosure is shown.

[0015] Fig.11 A diagram of a system including devices supporting techniques for determining circadian chronotype in accordance with aspects of the present disclosure is shown.

[0016] Fig.12 and Fig.13 A flow chart is shown illustrating a method of supporting techniques for determining circadian chronotype in accordance with aspects of the present disclosure. DETAILED DESCRIPTION

[0017] Some wearable devices can be configured to collect physiological data from users, including temperature data, heart rate data, heart rate variability (HRV) data, sleep data, breathing data, etc. The acquired physiological data can be used to analyze behavioral and physiological characteristics related to the user, such as movement, sleep patterns, activity patterns, etc. Many users want to know more about their physical health, including their sleep patterns, activities, and overall physical health. Specifically, many users may want to know more about their circadian rhythm, chronotype, and inconsistency with circadian rhythm. However, for a variety of reasons, typical tracking or health devices and applications lack the ability to provide robust determination and insights.

[0018] First, the process of determining and understanding chronotypes may rely on self-report scales or questionnaires, which can introduce many biases into the calculations. A questionnaire may consist of a set of questions about an individual’s sleep preferences, activity time start preferences, activity time offset preferences, sleep regularity preferences, etc. However, self-report assessments can be subjective. Second, even for wearables or devices that measure user biomarkers, typical devices and applications lack the ability to collect other physiological, behavioral, or contextual inputs from the user that can be combined with measured physiological parameters (e.g., temperature data, sleep data, etc.) to more fully understand the full set of physiological contributors to the user’s circadian rhythm and associated chronotype.

[0019] Aspects of the present disclosure are directed to techniques for determining circadian chronotypes. Specifically, a computing device of the present disclosure may receive physiological data collected over a period of time from a wearable device associated with a user. The physiological data may include at least nighttime temperature data, activity data, sleep pattern data, or some combination or subset of these measurements. Aspects of the present disclosure may use a machine learning model to classify physiological data from a wearable device into a circadian chronotype based on the nighttime temperature data, activity data, sleep pattern data, or a combination thereof. As described in more detail below, circadian chronotype classification may additionally or alternatively involve different physiological inputs and / or different chronotype classifications as inputs.

[0020] For purposes of this disclosure, terms such as "circadian chronotype," "circadian chronotype," or "circadian profile" may be used to refer to an individual's circadian rhythmicity related to sleep, diet, physical activity patterns, and the like. Circadian rhythms are biological internal processes that operate in the context of daily functions and coordinate a twenty-four hour cycle (or approximately a twenty-four hour cycle) in a user. Circadian rhythms regulate biological functions and processes, including but not limited to sleep-wake cycles, alertness, digestion, body temperature, hormone release, and the like. In some cases, a user's circadian rhythm (e.g., biological clock) may be sensitive to the external environment and affected by lifestyle choices and other factors. For example, exposure to light at different times of the day, traveling across multiple time zones, and working shifts with variable rotations may all affect the internal biological clock.

[0021] In some cases, the determined circadian chronotype may be compared to received physiological data from a previous calendar day (e.g., including the previous night's sleep the night before the current calendar day). For example, the determined circadian chronotype may be compared to sleep data from last night's sleep. In this case, the system may determine whether the user's most recent sleep data is aligned with the user's determined circadian chronotype. During circadian misalignment, body systems cease to function optimally, and many users may suffer from severe sleep disruptions due to, for example, circadian misalignment, as well as decreased alertness, academic performance, athletic performance, and other symptoms that reduce quality of life and also signal an increased risk of insomnia and chronic health conditions (e.g., sleep disorders, irritability, anxiety, obesity, diabetes, depression, and seasonal affective disorder).

[0022] In some cases, determining circadian chronotype and detecting misalignment at an early stage may reduce health risks in later life, particularly the risk of cardiovascular disease and cognitive impairment. In such cases, technologies that determine circadian chronotype may be needed to improve quality of life, sleep, and mood, and reduce future health risks. For example, methods and technologies that help users understand their circadian chronotype in a personalized way and how to optimize lifestyle changes to reduce misalignment may be needed.

[0023] Some aspects of the present disclosure are directed to measuring and / or receiving physiological data or signals that are regulated and affected by circadian rhythms. For example, signals may include, but are not limited to, sleep-wake cycles, physical activity, temperature, heart rate, recovery time, etc. In some embodiments, a computing device may be able to cause a graphical user interface (GUI) of a user device to display a graphical representation of an average of one or more measured or calculated physiological parameters or characteristics (e.g., sleep pattern data) over a period of time. For example, a graphical representation may include an average of one or more measured or calculated physiological parameters over a period of time and a second set of physiological data, the second set of physiological data including at least sleep data for the previous night's sleep.

[0024] In this case, the computing device can generate a behavioral and physiological picture of the user's twenty-four-hour clock based on the user's physiological data. For example, the system can create a prototype report based on circadian rhythm related data, which may include wake-up time and bedtime (e.g., sleep duration), the user's sleep regularity during the time range in which the report is processed, physical activity (e.g., metabolic equivalent of task (MET)) data distribution that can illustrate the user's energy expenditure at different times of the day, the user's overall sleep temperature changes, or a combination thereof.

[0025] The technology described herein can notify users of their determined circadian rhythm time type in a variety of ways, including a graphical representation of the average over a period of time. For example, the system can cause the GUI of the user's device to display a message or other notification to notify the user of the determined circadian rhythm time type and make suggestions to the user. In one example, the GUI can display the recommended time of the day for user activities, the recommended wake-up time for the user to wake up, the recommended bedtime for the user to fall asleep, the recommended sleep duration, the recommended time of the day for the user to rest, or a combination thereof. The GUI can also include a graphic / text indicating the misalignment between the received additional physiological data and the determined circadian rhythm time type. In this case, a message or notification can be generated based on the misalignment.

[0026] In some cases, understanding a user's circadian chronotype may allow a user to schedule sleep and daily activities so that the body can operate according to the user's own personalized circadian rhythm. For example, by taking into account the different alert timelines of morning and evening individuals throughout the day and recommending bedtimes and wake-up times that are appropriate for the user's determined chronotype, determining and understanding a circadian chronotype may allow a user to enhance mental, emotional, and physical performance, thereby improving the user's overall health.

[0027] Aspects of the disclosure are initially described in the context of a system that supports collecting physiological data from a user via a wearable device. Additional aspects of the disclosure are described in the context of example timing diagrams and example GUIs. Aspects of the disclosure are further illustrated and described with reference to apparatus diagrams, system diagrams, and flow charts related to techniques for determining circadian chronotype.

[0028] Figure 1 An example of a system 100 that supports determining a circadian chronotype according to various aspects of the present disclosure is shown, the system 100 including a plurality of electronic devices (e.g., wearable device 104, user device 106) that can be worn and / or operated by one or more users 102. The system 100 further includes a network 108 and one or more servers 110.

[0029] The electronic device may include any electronic device known in the art, including a wearable device 104 (e.g., a ring wearable device, a watch wearable device, etc.), a user device 106 (e.g., a smart phone, a laptop computer, a tablet computer). The electronic device associated with the corresponding user 102 may include one or more of the following functions: 1) measuring physiological data; 2) storing the measured data; 3) processing the data; 4) providing output (e.g., via a GUI) to the user 102 based on the processed data; and 5) communicating data with each other and / or other computing devices. Different electronic devices may perform one or more of these functions.

[0030] Example wearable devices 104 may include wearable computing devices, such as a ring computing device (hereinafter referred to as a "ring") configured to be worn on a finger of user 102, a wrist computing device (e.g., a smart watch, a fitness band or a bracelet) configured to be worn on the wrist of user 102, and / or a head-mounted computing device (e.g., glasses / goggles). Wearable device 104 may also include cords, bands (e.g., flexible or non-flexible cords or bands), Velcro sensors, etc. that may be positioned in other locations, such as bands around the head (e.g., forehead headbands), arms (e.g., forearm bands and / or double headbands), and / or legs (e.g., thigh or calf bands), behind the ears, under the armpits, etc. Wearable device 104 may also be attached to or included in an item of clothing. For example, wearable device 104 may be included in a pocket and / or pouch on clothing. As another example, wearable device 104 may be clipped and / or pinned to clothing, or may be otherwise kept near user 102. Exemplary clothing items may include, but are not limited to, hats, shirts, gloves, pants, socks, outerwear (e.g., jackets), and underwear. In some implementations, the wearable device 104 may be included in other types of equipment, such as training / sports equipment used during physical activity. For example, the wearable device 104 may be attached to or included in a bicycle, skis, tennis rackets, golf clubs, and / or training weights.

[0031] Many aspects of the present disclosure may be described in the context of a finger-ring wearable device 104. Therefore, unless otherwise noted herein, the terms "finger-ring 104," "wearable device 104," and similar terms may be used interchangeably. However, the use of the term "finger-ring 104" should not be considered limiting, as it is contemplated herein that various aspects of the present disclosure may be performed using other wearable devices (e.g., watch wearables, necklace wearables, bracelet wearables, earring wearables, ankle wearables, etc.).

[0032] In some aspects, user device 106 may include a handheld mobile computing device, such as a smartphone and a tablet computing device. User device 106 may also include a personal computer, such as a laptop and a desktop computing device. Other example user devices 106 may include a server computing device that can communicate with other electronic devices (e.g., via the Internet). In some implementations, the computing device may include a medical device, such as an external wearable computing device (e.g., a Holter monitor). The medical device may also include an implantable medical device, such as a pacemaker and a cardioverter defibrillator. Other example user devices 106 may include home computing devices, such as Internet of Things (IoT) devices (e.g., IoT devices), smart TVs, smart speakers, smart displays (e.g., video call displays), hubs (e.g., wireless communication hubs), security systems, smart appliances (e.g., thermostats and refrigerators), and fitness equipment.

[0033] Some electronic devices (e.g., wearable device 104, user device 106) can measure physiological parameters of the corresponding user 102, such as photoplethysmography waveform, continuous skin temperature, pulse waveform, respiration rate, heart rate, heart rate variability (HRV), body movement monitoring, galvanic skin response, pulse oximetry, blood oxygen saturation (SpO2), blood sugar level (e.g., glucose index), and / or other physiological parameters. Some electronic devices that measure physiological parameters can also perform some / all calculations described herein. Some electronic devices may not measure physiological parameters, but may perform some / all calculations described herein. For example, a ring (e.g., wearable device 104), a mobile device application, or a server computing device can process the received physiological data measured by other devices.

[0034] In some implementations, user 102 may operate or be associated with multiple electronic devices, some of which may measure physiological parameters, and some of which may process the measured physiological parameters. In some implementations, user 102 may have a ring (e.g., wearable device 104) that measures physiological parameters. User 102 may also have a user device 106 (e.g., a mobile device, a smart phone) or be associated with user device 106, wherein wearable device 104 and user device 106 are communicatively coupled to each other. In some cases, user device 106 may receive data from wearable device 104 and perform some / all calculations described herein. In some implementations, user device 106 may also measure physiological parameters described herein, such as motion / activity parameters.

[0035] For example, Figure 1As shown, a first user 102-a (user 1) can operate or be associated with a wearable device 104-a (e.g., a ring 104-a) and a user device 106-a that can operate as described herein. In this example, the user device 106-a associated with the user 102-a can process / store physiological parameters measured by the ring 104-a. In contrast, a second user 102-b (user 2) can be associated with a ring 104-b, a watch wearable device 104-c (e.g., a watch 104-c), and a user device 106-b, wherein the user device 106-b associated with the user 102-b can process / store physiological parameters measured by the ring 104-b and / or the watch 104-c. In addition, the nth user 102-n (user N) can be associated with the arrangement of electronic devices (e.g., ring 104-n, user device 106-n) described herein. In some aspects, wearable devices 104 (eg, rings 104, watches 104) and other electronic devices may be communicatively coupled to user devices 106 of respective users 102 via Bluetooth, Wi-Fi, and other wireless protocols.

[0036] In some implementations, the finger ring 104 (e.g., wearable device 104) of the system 100 may be configured to collect physiological data from the corresponding user 102 based on arterial blood flow in the user's finger. Specifically, the finger ring 104 may collect physiological data based on arterial blood flow in the user's finger using one or more light-emitting components (such as light-emitting diodes (LEDs) (e.g., red LEDs, green LEDs)) that emit light on the palm side of the user's finger. In general, the terms light-emitting component, light-emitting element, and similar terms may include, but are not limited to, LEDs, micro-LEDs, mini-LEDs, laser diodes (LDs) (e.g., vertical cavity surface emitting lasers (VCSELs)), and the like.

[0037] In some cases, the system 100 can be configured to collect physiological data from the corresponding user 102 based on blood flow diffused into the microvascular bed of the skin having capillaries and arterioles. For example, the system 100 can collect PPG data based on the measured amount of blood diffused into the microvascular system of capillaries and arterioles. In some embodiments, the ring 104 can use a combination of both green LEDs and red LEDs to acquire physiological data. The physiological data can include any physiological data known in the art, including but not limited to temperature data, accelerometer data (e.g., movement / motion data), heart rate data, HRV data, blood oxygen level data, or any combination thereof.

[0038] The use of both green and red LEDs can provide several advantages over other solutions, as it has been found that red and green LEDs have their own different advantages when acquiring physiological data under different conditions (e.g., bright / dark, active / inactive) and via different parts of the body, etc. For example, it has been found that green LEDs exhibit better performance during exercise. In addition, it has been found that wearable devices that utilize multiple LEDs (e.g., green and red LEDs) distributed around the ring 104 exhibit superior performance compared to LEDs positioned close to each other (such as within a watch wearable device). In addition, blood vessels (e.g., arteries, capillaries) in the fingers are more accessible via LEDs than blood vessels in the wrist. Specifically, the arteries in the wrist are located at the bottom of the wrist (e.g., the palm side of the wrist), which means that only capillaries are accessible at the top of the wrist (e.g., the back of the hand side of the wrist), and wearable watch devices and similar devices are typically worn on the top of the wrist. As such, it has been found that utilizing LEDs and other sensors within the finger ring 104 exhibits superior performance compared to wearable devices worn on the wrist, because the finger ring 104 has greater access to arteries (compared to capillaries), resulting in stronger signals and more valuable physiological data. In some cases, the system 100 may be configured to collect physiological data from various users 102 based on blood flow diffused into the microvascular bed of the skin having capillaries and arterioles. For example, the system 100 may collect PPG data based on the amount of blood measured diffused into the microvascular system of capillaries and arterioles.

[0039] The electronic devices of the system 100 (eg, user device 106, wearable device 104) may be communicatively coupled to one or more servers 110 via a wired or wireless communication protocol. Figure 1 As shown, electronic devices (e.g., user devices 106) can be communicatively coupled to one or more servers 110 via a network 108. The network 108 can implement the Transmission Control Protocol and Internet Protocol (TCP / IP) such as the Internet, or can implement other network 108 protocols. The network connection between the network 108 and the corresponding electronic devices can facilitate data transmission via email, web, text messaging, mail, or any other suitable form of interaction within the computer network 108. For example, in some implementations, a finger ring 104-a associated with a first user 102-a can be communicatively coupled to the user device 106-a, where the user device 106-a is communicatively coupled to the server 110 via the network 108. In addition or alternatively, the wearable device 104 (e.g., finger ring 104, watch 104) can be directly communicatively coupled to the network 108.

[0040] The system 100 can provide an on-demand database service between a user device 106 and one or more servers 110. In some cases, the server 110 can receive data from the user device 106 via the network 108, and can store and analyze the data. Similarly, the server 110 can provide data to the user device 106 via the network 108. In some cases, the server 110 can be located at one or more data centers. The server 110 can be used for data storage, management and processing. In some implementations, the server 110 can provide a web-based interface to the user device 106 via a web browser.

[0041] In some aspects, the system 100 can detect the time period when the user 102 is asleep and classify the time period when the user 102 is asleep into one or more sleep stages (e.g., sleep stage classification). Figure 1 As shown, user 102-a can be associated with wearable device 104-a (e.g., ring 104-a) and user device 106-a. In this example, ring 104-a can collect physiological data associated with user 102-a, including temperature, heart rate, HRV, breathing rate, etc. In some aspects, the data collected by ring 104-a can be input into a machine learning classifier, wherein the machine learning classifier is configured to determine the time period when user 102-a is asleep (or previously asleep). In addition, the machine learning classifier can be configured to classify the time period into different sleep stages, including awake sleep stage, rapid eye movement (REM) sleep stage, light sleep stage (non-REM (NREM)), and deep sleep stage (NREM). In some aspects, the classified sleep stage can be displayed to user 102-a via the GUI of user device 106-a. The sleep stage classification can be used to provide feedback to user 102-a about the user's sleep pattern, such as recommended bedtime, recommended wake-up time, etc. Furthermore, in some implementations, the sleep stage classification techniques described herein may be used to calculate scores for corresponding users, such as sleep scores, readiness scores, and the like.

[0042] In some aspects, the system 100 can further improve physiological data collection, data processing procedures, and other technologies described herein using features derived from circadian rhythms. The term circadian rhythm can refer to a natural internal process that regulates the sleep-wake cycle of an individual that repeats approximately every 24 hours. In this regard, the technology described herein can utilize a circadian rhythm regulation model to improve physiological data collection, analysis, and data processing. For example, the circadian rhythm regulation model and the physiological data collected from the user 102-a via the wearable device 104-a can be input into a machine learning classifier. In this example, the circadian rhythm regulation model can be configured to "weight" or regulate the physiological data collected in the natural, approximately 24-hour circadian rhythm of the entire user. In some implementations, the system can initially start with a "baseline" circadian rhythm regulation model, and the baseline model can be modified using the physiological data collected from each user 102 to generate a customized, personalized circadian rhythm regulation model specific to each respective user 102.

[0043] In some aspects, the system 100 can utilize other biological rhythms to further improve physiological data collection, analysis, and processing through the phases of these other rhythms. For example, if a weekly rhythm is detected within the baseline data of an individual, the model can be configured to adjust the "weight" of the data by the day of the week. Biological rhythms that may need to be adjusted by this method include: 1) ultradian (faster than daily rhythms, including sleep cycles in the sleep state, and oscillations from less than an hour period to several hours in physiological variables measured during the waking state; 2) circadian rhythms; 3) daily rhythms that are not endogenous and are shown to be imposed on circadian rhythms, such as in work schedules; 4) weekly rhythms, or other artificial time periods imposed by exogenous sources (for example, in a hypothetical culture with a "week" of 12 days, a 12-day rhythm can be used); 5) multi-day ovarian rhythms in women and spermatogenesis rhythms in men; 6) lunar rhythms (associated with individuals living under low or no artificial light); and 7) seasonal rhythms.

[0044] Biorhythms are not always static rhythms. For example, many women experience variability in the length of the ovarian cycle between cycles, and even within a user, ultradian rhythms are not expected to occur at exactly the same time or cycle over several days. Thus, signal processing techniques sufficient to quantify the frequency composition while maintaining the temporal resolution of these rhythms in the physiological data can be used to improve the detection of these rhythms, assign the phase of each rhythm to each moment measured, and thereby modify the comparison of regulation models and time intervals. Biorhythm regulation models and parameters can be added in linear or nonlinear combinations where appropriate to more accurately capture the dynamic physiological baseline of an individual or group of individuals.

[0045] In certain aspects, various devices of system 100 may support techniques for determining circadian rhythm chronotype based on data collected by wearable device 104. Specifically, Figure 1 The system 100 shown in the figure can support technology for determining the circadian rhythm chronotype of the user 102 and causing the user device 106 corresponding to the user 102 to display a graphical representation of the average of sleep pattern data over a period of time (e.g., the last 30 or 60 days) relative to the sleep pattern data of the previous night's sleep.

[0046] For example, Figure 1 As shown, user 1 (user 102-a) can be associated with a wearable device 104-a (e.g., finger ring 104-a) and a user device 106-a. In this example, the finger ring 104-a can collect data associated with the user 102-a, including continuous nighttime temperature data, activity data, sleep pattern data, heart rate, etc. As used herein, "continuous" nighttime temperature can refer to the ability of the system 100 to continuously sample the temperature of the user 102-a during the day and / or night at a sufficient rate (e.g., one sample per minute) to provide sufficient temperature data for the analysis described herein.

[0047] In certain aspects, the physiological data from the wearable device 104-a may be classified into a circadian chronotype of the user 1 using the data collected by the ring 104-a using a machine learning model. Determining the circadian chronotype may be performed by any component of the system 100, including the ring 104-a, a user device 106-a associated with the user 1, one or more servers 110, or any combination thereof. After determining the circadian chronotype, the system 100 may selectively cause the GUI of the user device 106-a to display a graphical representation indicating the determined chronotype, one or more physiological parameters used to classify the chronotype, or some combination of such information.

[0048] For example, the system 100 may cause the GUI of the user device 106-a to display at least an average of sleep pattern data over a period of time. In other examples, the system 100 may cause the GUI of the user device 106-a to display sleep pattern data for the previous night's sleep of the user 1. In some examples, this information may be displayed simultaneously, allowing the user to easily see multiple types of information superimposed on a time scale (e.g., a 24-hour clock face, etc.), thereby clearly understanding multiple insights or relationships between different physiological parameters or chronotypes (e.g., average bedtime or wake-up time compared to last night's bedtime or wake-up time).

[0049] In some embodiments, upon receiving physiological data (e.g., including at least continuous nighttime temperature data, activity data, and sleep pattern data), the system 100 may classify the physiological data into a circadian rhythm chronotype (e.g., determining whether you are an active person, whether you have a regular sleep schedule, etc.) using a machine learning model. In some examples, the system 100 may overlay a graphical representation of at least the sleep pattern data and an average of the sleep pattern data from a previous night's sleep over a period of time against a circular representation of a twenty-four hour time span. In this case, the system 100 may cause the GUI of the user device 106-a to display a first segment including an average of the sleep pattern data over a period of time and a second segment including the sleep pattern data from a previous night's sleep.

[0050] In some cases, the system 100 may display the first segment and the second segment to the user 1 (e.g., via a GUI of the user device 106). In some embodiments, the system 100 may generate an alert, message, or suggestion for the user 1 based on the determined circadian chronotype (e.g., via the ring 104-a, the user device 106-a, or both), wherein the alert may provide insights into inconsistencies between the received physiological data and the determined circadian chronotype. In some cases, the message may provide insights into recommended times of day for exercise, waking, sleeping, resting, or a combination thereof.

[0051] Those skilled in the art will appreciate that one or more aspects of the present disclosure may be implemented in the system 100 to additionally or alternatively address other issues beyond those described above. In addition, various aspects of the present disclosure may provide technical improvements to the "conventional" systems or processes described herein. However, the description and drawings include only example technical improvements resulting from implementing various aspects of the present disclosure, and therefore do not represent all technical improvements provided within the scope of the claims.

[0052] Figure 2 An example of a system 200 supporting techniques for determining a circadian rhythm chronotype according to various aspects of the present disclosure is shown. The system 200 may implement or be implemented by the system 100. Specifically, the system 200 shows an example of a ring 104 (e.g., a wearable device 104), a user device 106, and a server 110, as shown in FIG. Figure 1 as described.

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

[0054] The system 200 further includes a user device 106 (e.g., a smartphone) in communication with the ring 104. For example, the ring 104 can communicate with the user device 106 wirelessly and / or by wire. In some implementations, the ring 104 can send measured and processed data (e.g., temperature data, photoplethysmogram (PPG) data, motion / accelerometer data, ring input data, etc.) to the user device 106. The user device 106 can also send data to the ring 104, such as ring 104 firmware / configuration updates. The user device 106 can process the data. In some implementations, the user device 106 can transmit the data to the server 110 for processing and / or storage.

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

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

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

[0058] The housing 205 may include one or more housing 205 components. The housing 205 may include an outer housing 205-b component (eg, a housing) and an inner housing 205-a component (eg, a molded part). The housing 205 may include Figure 2 205-b). For example, in some implementations, the finger ring 104 may include one or more insulating layers that electrically insulate the device electronics and other conductive materials (e.g., electrical traces) from the outer shell 205-b (e.g., a metal outer shell 205-b). The housing 205 may provide structural support for the device electronics, the battery 210, one or more substrates, and other components. For example, the housing 205 may protect the device electronics, the battery 210, and one or more substrates from mechanical forces, such as pressure and shock. The housing 205 may also protect the device electronics, the battery 210, and one or more substrates from damage by water and / or other chemicals.

[0059] The outer shell 205-b can be made of one or more materials. In some implementations, the outer shell 205-b can include a metal, such as titanium, which can provide strength and wear resistance at a relatively light weight. The outer shell 205-b can also be made of other materials (such as polymers). In some implementations, the outer shell 205-b can be protective and decorative.

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

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

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

[0063] The various components / modules of the finger ring 104 represent functions (e.g., circuits and other components) that may be included in the finger ring 104. A module may include any discrete and / or integrated electronic circuit components that implement analog and / or digital circuits capable of producing the functions attributed to the modules herein. For example, a module may include analog circuits (e.g., amplification circuits, filtering circuits, analog / digital conversion circuits, and / or other signal conditioning circuits). A module may also include digital circuits (e.g., combinational or sequential logic circuits, memory circuits, etc.).

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

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

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

[0067] The processing module 230-a can be in communication with the memory 215. The memory 215 can include computer-readable instructions that, when executed by the processing module 230-a, cause the processing module 230-a to perform various functions attributed to the processing module 230-a herein. In some implementations, the processing module 230-a (e.g., a microcontroller) can include additional features associated with other modules, such as communication functions provided by the communication module 220-a (e.g., an integrated Bluetooth low energy transceiver) and / or additional onboard memory 215.

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

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

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

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

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

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

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

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

[0076] The ring 104 (e.g., the communication module) can transmit the sampled temperature data and / or the average temperature data to the user device 106 for storage and / or further processing. The user device 106 can transmit the sampled temperature data and / or the average temperature data to the server 110 for storage and / or further processing.

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

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

[0079] The temperature sensor 240 on the finger ring 104 can obtain the remote temperature at the user's finger (e.g., any finger). For example, one or more temperature sensors 240 on the finger ring 104 can obtain the user's temperature from the underside of the finger or different locations on the finger. In some implementations, the finger ring 104 can continuously obtain the remote temperature (e.g., at a sampling rate). Although the remote temperature measured by the finger ring 104 at the finger is described herein, other devices can measure the temperature at the same / different location. In some cases, the remote temperature measured at the user's finger may be different from the temperature measured at the user's wrist or other external body location. In addition, the remote temperature measured at the user's finger (e.g., "shell" temperature) may be different from the user's core temperature. In this way, the finger ring 104 can provide a useful temperature signal that may not be obtained at other internal / external locations of the body. In some cases, continuous temperature measurements at the finger can capture temperature fluctuations (e.g., small fluctuations or large fluctuations) that may not be obvious in the core temperature. For example, continuous temperature measurements at a finger can capture minute-to-minute or hour-to-hour temperature fluctuations, which provides additional insight that other temperature measurements elsewhere in the body may not provide.

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

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

[0082] The number and ratio of emitters and receivers included in the PPG system 235 can vary. Example light emitters can include light emitting diodes (LEDs). Light emitters can emit light in the infrared spectrum and / or other spectrums. Example light receivers can include, but are not limited to, photosensors, phototransistors, and photodiodes. Light receivers can be configured to generate a PPG signal in response to wavelengths received from light emitters. The locations of emitters and receivers can vary. In addition, a single device can include a reflective and / or transmissive PPG system 235.

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

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

[0085] Sampling the PPG signal generated by the PPG system 235 can produce a pulse waveform, which can be referred to as a "PPG". The pulse waveform can indicate blood pressure versus time for multiple cardiac cycles. The pulse waveform can include peaks indicating cardiac cycles. In addition, the pulse waveform can include respiration-induced changes that can be used to determine respiratory rate. In some implementations, the processing module 230-a can store the pulse waveform in the memory 215. The processing module 230-a can process the pulse waveform as it is generated and / or from the memory 215 to determine the user's physiological parameters described herein.

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

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

[0088] The finger ring 104 may include one or more motion sensors 245, such as one or more accelerometers (e.g., 6-D accelerometers) and / or one or more gyroscopes (gyros). The motion sensor 245 may generate a motion signal indicating the motion of the sensor. For example, the finger ring 104 may include one or more accelerometers that generate an acceleration signal indicating the acceleration of the accelerometer. As another example, the finger ring 104 may include one or more gyro sensors that generate a gyro signal indicating angular motion (e.g., angular velocity) and / or orientation change. The motion sensor 245 may be included in one or more sensor packages. An example accelerometer / gyro sensor is a Bosch BM1160 inertial micro-electromechanical system (MEMS) sensor, which can measure angular rate and acceleration on three perpendicular axes.

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

[0090] The finger ring 104 may store various data described herein. For example, the finger ring 104 may store temperature data, such as raw sampled temperature data and calculated temperature data (e.g., average temperature). As another example, the finger ring 104 may store PPG signal data, such as a pulse waveform and data calculated based on the pulse waveform (e.g., heart rate value, IBI value, HRV value, and respiratory rate value). The finger ring 104 may also store motion data, such as motion data indicating samples of linear motion and angular motion.

[0091] The ring 104 or other computing device can calculate and store additional values ​​based on the sampled / calculated physiological data. For example, the processing module 230 can calculate and store various metrics, such as sleep metrics (e.g., sleep scores), activity metrics, and readiness metrics. In some implementations, the additional values / metrics can be referred to as "derived values". The ring 104 or other computing / wearable device can calculate various values / metrics about motion. Example derived values ​​of motion data may include, but are not limited to, motion count values, regularity values, intensity values, metabolic equivalence of task values ​​(METs), and orientation values. Motion counts, regularity values, intensity values, and METs can indicate the amount of user motion (e.g., speed / acceleration) over time. The orientation value can indicate how the ring 104 is oriented on the user's finger and whether the ring 104 is worn on the left or right hand.

[0092] In some implementations, the motion count and regularity value can be determined by counting the number of acceleration peaks within one or more time periods (e.g., one or more 30 seconds to 1 minute time periods). The intensity value can indicate the number of movements and the associated intensity of the movements (e.g., acceleration value). The intensity value can be classified as low, medium, and high depending on the associated threshold acceleration value. METs can be determined based on the intensity of the movements during a time period (e.g., 30 seconds), the regularity / irregularity of the movements, and the number of movements associated with different intensities.

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

[0094] Although the physiological parameters of the user can be measured by the sensor included on the finger ring 104, other devices can also measure the physiological parameters of the user. For example, although the temperature of the user can be measured by the temperature sensor 240 included in the finger ring 104, other devices can also measure the temperature of the user. In some examples, other wearable devices (e.g., wrist devices) can include sensors that measure the physiological parameters of the user. In addition, medical devices such as external medical devices (e.g., wearable medical devices) and / or implantable medical devices can measure the physiological parameters of the user. The techniques described herein can be implemented using one or more sensors on any type of computing device.

[0095] Physiological measurements may be acquired continuously throughout the day and / or night. In some implementations, physiological measurements may be acquired during various parts of the day and / or various parts of the night. In some implementations, physiological measurements may be acquired in response to determining that the user is in a particular state (e.g., an active state, a resting state, and / or a sleeping state). For example, the ring 104 may perform physiological measurements in a resting / sleeping state to obtain a cleaner physiological signal. In one example, the ring 104 or other device / system may detect when the user is resting and / or sleeping and obtain physiological parameters (e.g., temperature) of the detected state. When the user is in other states, the device / system may use resting / sleeping physiological data and / or other data to implement the technology of the present disclosure.

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

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

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

[0099] In some cases, a "sleep day" can be aligned with a traditional calendar day so that a given sleep day extends from midnight to midnight on the corresponding calendar day. In other cases, a sleep day can be offset relative to a calendar day. For example, a sleep day can extend from 6:00 p.m. (18:00) on a calendar day to 6:00 p.m. (18:00) on a subsequent calendar day. In this example, 6:00 p.m. can serve as a "cutoff time," where data collected from the user before 6:00 p.m. is counted for the current sleep day, and data collected from the user after 6:00 p.m. is counted for subsequent sleep days. Due to the fact that most individuals sleep the most at night, offsetting the sleep day relative to a calendar day can enable the system 200 to evaluate the user's sleep pattern in a manner consistent with their sleep schedule. In some cases, a user may be able to selectively adjust (e.g., via a GUI) the timing of the sleep day relative to a calendar day so that the sleep day is aligned with the duration of the corresponding user's usual sleep.

[0100] In some implementations, each total score (e.g., sleep score, readiness score) for a user on each respective day may be determined / calculated based on one or more "contributors," "factors," or "contributing factors." For example, a total sleep score for a user may be calculated based on a set of contributors, including: total sleep, efficiency, restfulness, REM sleep, deep sleep, latency, timing, or any combination thereof. A sleep score may include any number of contributors. A "total sleep" contributor may refer to the sum of all sleep sessions for a sleep day. An "efficiency" contributor may reflect the percentage of time spent asleep compared to the time spent awake while sleeping, and may be calculated using the efficiency average of the long sleep sessions (e.g., main sleep sessions) of a sleep day weighted by the duration of each sleep session. A "restfulness" contributor may indicate how restful a user's sleep is, and may be calculated using the average of all sleep sessions for a sleep day weighted by the duration of each session. Serenity contributors may be based on “wake count” (e.g., the sum of all wakeups detected during different sleep sessions (when the user wakes up)), excessive movement, and “getup count” (e.g., the sum of all getups detected during different sleep sessions (when the user gets out of bed)).

[0101] A "REM sleep" contributor may refer to the sum of the duration of REM sleep over all sleep periods of a sleep day that includes REM sleep. Similarly, a "deep sleep" contributor may refer to the sum of the duration of deep sleep over all sleep periods of a sleep day that includes deep sleep. A "wait time" contributor may represent how long it takes a user to fall asleep (e.g., average, median, longest), and may be calculated using an average of long sleep periods during a sleep day, weighted by the duration of each period and the number of such periods (e.g., a given one or more sleep stages combined may be a contributor in themselves or may be weighted against other contributors). Finally, a "timing" contributor may refer to the relative timing of sleep periods within a sleep day and / or calendar day, and may be calculated using an average of all sleep periods of a sleep day weighted by the duration of each period.

[0102] As another example, a user's overall readiness score may be calculated based on a set of contributors, including: sleep, sleep balance, heart rate, HRV balance, recovery index, temperature, activity, activity balance, or any combination thereof. The readiness score may include any number of contributors. A "sleep" contributor may refer to a combined sleep score for all sleep periods within a sleep day. A "sleep balance" contributor may refer to the cumulative duration of all sleep periods within a sleep day. Specifically, sleep balance may indicate to a user whether the sleep that the user has taken over a certain duration (e.g., the past two weeks) is balanced with the user's needs. Typically, adults need 7-9 hours of sleep each night to stay healthy, alert, and perform at their best mentally and physically. However, occasional nights with poor sleep are the norm, so sleep balance contributors consider long-term sleep patterns to determine whether each user's sleep needs are met. A "resting heart rate" contributor may indicate the lowest heart rate from the longest sleep period (e.g., the main sleep period) of a sleep day and / or the lowest heart rate from a nap that occurs after the main sleep period.

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

[0105] In some aspects, the system 200 can support techniques for determining circadian rhythm chronotypes. Specifically, the various components of the system 200 can be used to classify physiological data from the wearable device 104 into circadian rhythm chronotypes based on the received physiological data (e.g., including continuous night temperature data, activity data, sleep pattern data, or additional or alternative physiological parameters) using a machine learning model. The user's circadian rhythm chronotype can be predicted by utilizing a temperature sensor, a heart rate sensor, etc. on the ring 104 of the system 200.

[0106] The system 200 may compare the determined circadian chronotype to the sleep pattern data for the night before the current calendar day. In this case, the system 200 may display to the user 102 a graphical representation of at least an average of the sleep pattern data over a period of time (e.g., the past 90 days or other configurable time period) relative to the sleep pattern data for the night before the current calendar day.

[0107] For example, as described earlier herein, the finger ring 104 of the system 200 may be worn by the user 102 to collect physiological data from the user 102, including continuous nighttime temperature data, activity data, sleep pattern data, etc. The finger ring 104 of the system 200 may collect physiological data from the user 102 based on arterial blood flow, which may provide a more accurate measurement signal than measuring venous blood flow. However, the concepts described herein may also be applicable to measurements made from venous blood flow or some combination of arterial and venous blood flow.

[0108] Physiological data can be collected continuously. In some embodiments, processing module 230-a can continuously sample the user's body temperature throughout the day and night. Sampling at a sufficient rate (e.g., one sample per minute) throughout the day can provide sufficient temperature data for analysis as described herein. In some embodiments, ring 104 can continuously acquire temperature data, activity data, sleep pattern data, heart rate data, etc. (e.g., at the sampling rate). The data collected by ring 104 can be used to determine circadian rhythm time type. Reference Figure 3 and Figure 6 Examples of circadian chronotype determination are further shown and described.

[0109] refer to Figure 2 In the system 200 shown in FIG. 1 , the ring 104 can be worn by the user 102 and can collect data associated with the user 102 throughout the day and night (e.g., continuously). The ring 104 can collect data (e.g., temperature, sleep, MET, heart rate) and transmit the collected data to the user device 106. In some cases, the user device 106 can forward (e.g., relay, transmit) the data received from the ring 104 to the server 110 for processing. In addition, or alternatively, the user device 106 and / or the ring 104 can perform processing on the collected data.

[0110] Continuing with the same example, the ring 104, the user device 106, the server 110, or any combination thereof may determine a circadian chronotype based on the collected data. After determining the circadian chronotype, the server 110 may transmit an indication of the circadian chronotype to the user device 106. Alternatively, where the user device 106 performs data processing, the user device 106 may generate an indication of the determined circadian chronotype. In this example, the next time the user opens the wearable application 250, the indication of the determined circadian chronotype may be presented to the user via the GUI 275 of the user device 106. Reference Figure 8 Some illustrative but non-limiting examples of the process and user interface are further described.

[0111] For purposes of this disclosure, the terms "circadian chronotype", "circadian chronotype", "circadian profile", etc., may be used interchangeably. In some cases, the system 100 (e.g., the user device 106, the server 110) may be configured to receive data collected from the user 102 via the ring 104 and determine the circadian chronotype.

[0112] Figure 3 An example of a system 300 supporting techniques for determining a circadian chronotype according to aspects of the present disclosure is shown. The system 300 may implement or be implemented by the system 100, the system 200, or both. Specifically, the system 300 shows an example of a ring 104 (e.g., a wearable device 104), a user device 106, and a server 110, as shown in reference Figure 1 described.

[0113] The system 300 may include an algorithm for chronotype characterization. In this case, the system 300 may determine the circadian chronotype from one or more data sources. As further described herein, if the system 300 receives an amount of data that meets a threshold, the system 300 may determine the circadian chronotype. If the system 300 determines that the amount of data does not meet the threshold, the system 300 may avoid determining the circadian chronotype. The system 300 may include one or more processing pipelines and a collective estimate of each processing pipeline. For example, the system 300 may classify each set of physiological data into different chronotypes (e.g., determining whether the user is an "active person", or has a "regular sleep schedule", etc.). The system 300 may then determine the circadian chronotype based on the classification of each set of physiological data.

[0114] At 305, the system 300 may receive input parameters. The input parameters may include a user identification (e.g., the identity of the user), a history length (e.g., the number of days the system 300 receives data), a timeline (e.g., a start date for receiving physiological data and an end date for receiving physiological data), a configuration parameter, a data threshold, or a combination thereof.

[0115] At 310, the system 300 may receive sleep data. For example, the system 300 may receive physiological data related to the user from the wearable device over a period of time. The physiological data may include at least sleep pattern data. In some cases, the sleep pattern data may include sleep regularity data. In this case, the system 300 may load sleep summary data (e.g., sleep data, sleep pattern data, or both) within a time range (e.g., a time period) and process the sleep summary data. In some examples, the sleep pattern data may include at least the time the user falls asleep (or goes to bed but has not fallen asleep) each night and the time the user wakes up in the morning.

[0116] At 315, the system 300 may check a data threshold. For example, the system 300 may determine whether the amount of sleep data received meets a threshold. The system 300 may determine that the amount of sleep data received does not meet a threshold. In this case, the system 300 may avoid extracting sleep data. For example, the system 300 may determine that the system 300 does not contain a sufficient amount of data to estimate a sleep chronotype. In other examples, the system 300 may determine that the amount of sleep data received meets (e.g., equals or exceeds) a threshold.

[0117] The threshold may be an example of a history length that indicates the number of days that the system 300 received sleep data. In this case, the threshold may be predetermined because the system 300 receives the threshold at 305 and then receives sleep data at 310. The system 300 may identify the history length to determine whether the amount of sleep data received meets the threshold. In some examples, the threshold may be 90 consecutive nights in the same time zone. However, the threshold may be configurable and / or changed over time by the user or the system 300.

[0118] At 320, the system 300 can extract the sleep data. For example, the system 300 can discard sleep data that may be affected by jet lag (e.g., spanning two or more time zones). The system 300 can extract the sleep data based on determining that the received sleep data contains a minimum gap between measurements (e.g., receiving sleep data for 90 consecutive nights). In some cases, the system 300 can extract the sleep data in response to checking a data threshold and determining that the data meets the threshold.

[0119] The system 300 may extract the sleep data of the last length "n" (e.g., the history length). In this case, the system 300 may narrow down the subset of sleep data to be processed further (e.g., including the measurement date). For example, the system 300 may extract sleep data measured within the history length (e.g., the time period). Extracting the sleep data at 320 may trigger a data processing pipeline for temperature data, MET data, and / or heart rate data, as described herein.

[0120] At 325, the system 300 may derive a sleep index. For example, the system 300 may determine that the sleep data (e.g., sleep pattern data) includes wake time, bedtime, sleep duration, or a combination thereof. In this case, the system 300 may identify the average wake time, average bedtime, average sleep duration, or a combination thereof for the user during the time period. In some cases, the system 300 may determine a sleep regularity index based on receiving physiological data (e.g., sleep data).

[0121] At 330, the system 300 can estimate a sleep chronotype. For example, the system 300 can classify the physiological data from the wearable device into a chronotype associated with the sleep pattern data. In some cases, the system 300 can determine a sleep chronotype (e.g., a third chronotype referred to elsewhere herein) based on determining a sleep regularity index. The system 300 can input the physiological data into a machine learning classifier. In this case, the system 300 can use machine learning to classify sleep chronotypes, determine circadian chronotypes, or both.

[0122] At 335, the system 300 may receive temperature data. For example, the system 300 may receive physiological data associated with the user from the wearable device over a period of time. The physiological data may include at least continuous nighttime temperature data, continuous daytime temperature data, or both. In this case, the system 300 may load the continuous nighttime temperature data within a time range (e.g., the time period) and process the continuous nighttime temperature data. The continuous nighttime temperature data may be loaded and processed based on the extracted sleep data. For example, the system 300 filters the data based on the reduced (e.g., extracted) sleep time. In this case, the system 300 may discard the daytime temperature data and store the nighttime temperature data.

[0123] At 340, the system 300 may aggregate the temperature data. For example, the system 300 may determine an aggregated time series by calculating the 75th percentile of data points measured at the same time of day on different days. In this case, the system 300 may determine an aggregated sleep temperature. For example, the system 300 may generate a pool of measurements (e.g., including temperature values ​​and timestamps of the temperature values) over the course of weeks or months of sleep data. In some cases, the system 300 may discard temperature values ​​with nighttime peaks and / or baseline changes that may indicate outliers compared to the rest of the temperature data. In some examples, the system 300 may record continuous nighttime temperature data for the past 90 nights of sleep data.

[0124] The temperature time series can be filtered to include data that may have been measured during bedtime. The system 300 can quantize the nighttime temperature data into minute-by-minute bins and collect all temperature values ​​recorded in each minute-long bin. In this case, the system 300 can summarize the distribution of temperature values ​​for each minute and extract the 75th percentile of the distribution for each minute-long bin. The 75th percentile of each bin can create a time series that can represent the aggregate temperature signal and overall temperature change for 90 consecutive nights, which can be referenced Figure 4 Further description.

[0125] If the temperature value is measured on at least 20% of the bottom nights, the system 300 can assign the 75th percentile of each bin (e.g., a one-minute long interval) as the representative temperature in that bin. The number of values ​​in each bin can be stored as the weight of the representative value. In some cases, the first half hour of the aggregated temperature signal can be omitted from calculations where the temperature is stable and therefore constitutes an upward trend for many users. In this case, discarding the first half hour portion of the temperature values ​​can prevent problems when fitting a function to the data, such as spline fit distortion.

[0126] At 345, the system 300 can fit a spline curve (or other mathematical function) on the aggregated sleep temperature. For example, the system 300 can fit a 5th order univariate spline curve on the aggregated temperature signal using the derived weights (e.g., the number of values ​​in each minute-long interval and / or bin). In this case, the system 300 can fit the spline curve to the time series. The system 300 can fit the spline curve in response to the aggregated temperature data.

[0127] At 350, a temperature minimum may be determined. The temperature minimum may be determined based on fitting a spline curve to the aggregated sleep temperature. In this case, the temperature minimum may include a time of the temperature minimum and a value of the temperature minimum. For example, the system 300 may identify a nighttime time associated with a nighttime temperature minimum based on receiving the physiological data. To determine the minimum temperature value, the system 300 may determine whether 90% of the spline fit values ​​are greater than a local minimum. In some cases, the system 300 may determine a maximum and / or minimum temperature value. In some cases, the system 300 may identify more than one minimum temperature value. In this case, the system 300 may select the lowest minimum temperature value.

[0128] If the system 300 cannot determine the temperature minimum, the system 300 can refrain from deriving the temperature indicator. If the temperature minimum is not determined, the system 300 may be unable to estimate the temperature profile. In some cases, the system 300 can determine whether the difference between the 95th percentile and the 5th percentile of the aggregated temperature signal meets a threshold. In the event that the difference meets the threshold, the system 300 can derive the temperature indicator. If the system 300 determines that the difference fails to meet the threshold, the system 300 can refrain from deriving the temperature indicator.

[0129] At 355, the system 300 may derive a temperature indicator. For example, the system 300 may derive the temperature indicator in response to determining a temperature minimum. At 360, the system 300 may estimate a temperature chronotype. For example, the system 300 may classify the physiological data from the wearable device as a first chronotype associated with continuous nighttime temperature data. In some cases, classifying the physiological data as a first chronotype associated with continuous nighttime temperature data may be in response to identifying a nighttime time associated with a nighttime temperature minimum. The system 300 may input the temperature data into a machine learning classifier. In this case, the system 300 may use machine learning to classify the temperature chronotype, determine the circadian rhythm chronotype, or both. In some cases, the system 300 may classify the physiological data from the wearable device as a first chronotype associated with continuous nighttime temperature data, sleep pattern data, activity data, or a combination thereof.

[0130] At 365, the system 300 may receive MET data. For example, the system 300 may receive physiological data associated with the user from the wearable device over a period of time. The physiological data may include at least activity data. In this case, the system 300 may load the MET data and process the MET data. The MET data may be loaded and processed based on extracting the sleep data.

[0131] At 370, the system 300 may aggregate the MET data. At 375, the system 300 may check a data threshold. For example, the system 300 may determine whether the amount of MET data received meets a threshold. The system 300 may determine that the amount of MET data received does not meet a threshold. In this case, the system 300 may avoid deriving a MET metric. For example, the system 300 may determine that the system 300 does not include a threshold amount of data for estimating a MET chronotype. In other examples, the system 300 may determine that the amount of MET data received meets (e.g., equals or exceeds) a threshold.

[0132] The threshold may be an example of a history length that indicates the number of days that the system 300 receives MET data. In this case, the threshold may be predetermined such that the system 300 receives the threshold at 305 before receiving the MET data at 365. The system 300 may identify the history length to determine whether the number of METs received meets the threshold. In some examples, the threshold may be 90 consecutive days in the same time zone. At 380, the system 300 may derive a MET indicator. For example, the system 300 may derive a MET indicator in response to determining that the MET data meets the threshold.

[0133] At 385, the system 300 may estimate a MET chronotype. For example, the system 300 may classify the physiological data from the wearable device into a second chronotype associated with the activity data. The system 300 may input the MET data into a machine learning classifier. In this case, the system 300 may use machine learning to classify the MET chronotype, determine the circadian rhythm chronotype, or both.

[0134] At 390, the system 300 may receive heart rate data. For example, the system 300 may receive physiological data associated with the user from the wearable device over a period of time. The physiological data may include at least heart rate data. In this case, the system 300 may load the heart rate data and process the heart rate data. The heart rate data may be loaded and processed based on the extracted sleep data.

[0135] At 392, the system 300 can estimate the heart rate chronotype. For example, the system 300 can classify the physiological data from the wearable device as a fourth chronotype associated with the heart rate data in response to receiving the physiological data. In this case, the system 300 can receive the heart rate data and use the heart rate data to determine the heart rate chronotype.

[0136] At 395, the system 300 may determine a circadian chronotype based on the continuous nighttime temperature data and the first chronotype, the second chronotype, and the third chronotype. In some examples, the system 300 may determine a circadian chronotype based on the continuous nighttime temperature data, the first chronotype, the second chronotype, the third chronotype, or a combination thereof. For example, the system 300 may determine a circadian chronotype based on the continuous nighttime temperature data and in response to determining a sleep chronotype, a temperature chronotype, a MET chronotype, a heart rate chronotype, or a combination thereof. In some cases, the circadian chronotype may be determined in response to inputting the physiological data into a machine learning classifier.

[0137] The system 300 can determine a circadian chronotype based on the amount of measured sleep data that meets a threshold over a period of time. In this case, the system 300 can determine the circadian chronotype based on the sleep summary data and at least an estimate of the sleep, temperature, and / or MET chronotype. The system 300 can fuse the estimates obtained from different sources into a collective estimated chronotype of the circadian chronotype. Therefore, by enabling more complete and accurate circadian chronotype determination, the techniques described herein can enable the system 300 to provide improved insights and guidance to the user that better correlates with the user's overall health.

[0138] Figure 4 An example of a timing diagram 400 supporting techniques for determining a circadian chronotype according to aspects of the present disclosure is shown. The timing diagram 400 may implement aspects of the system 100, the system 200, the system 300, or a combination thereof, or may be implemented by aspects of the system 100, the system 200, the system 300, or a combination thereof. For example, in some embodiments, the timing diagram 400 may be displayed to the user 102 via the GUI 275 of the user device 106, such as Figure 2 shown.

[0139] As described in further detail herein, the system can be configured to determine a circadian rhythm chronotype. In some cases, a user's core body temperature pattern throughout the night can be an indicator that can characterize the user's chronotype. For example, nighttime skin temperature and sleep timelines can determine a temperature chronotype. Thus, timing graph 400-a illustrates a relationship between a user's temperature data and a duration (e.g., number of minutes since midnight) starting at midnight. In this regard, the multiple dashed vertical lines shown in timing graph 400-a can be understood to refer to "aggregate temperature data 405," as shown in reference to FIG. Figure 3 In this regard, the solid line curve shown in the timing diagram 400-a can be understood to refer to the "fitted spline curve 410", as shown in reference Figure 3 In this regard, the single dashed vertical line in the timing diagram 400-a may be referred to as a "temperature minimum 415," as shown in FIG. Figure 3 As described in block 350.

[0140] In some cases, the system may determine or estimate the user's temperature minimum 415 based on the user's continuous nighttime temperature data collected by the ring. In some embodiments, the system may determine a temperature chronotype, a circadian rhythm chronotype, or both in response to receiving continuous nighttime skin temperature data. The skin temperature data may be collected in one-minute increments (e.g., frequency).

[0141] In some cases, the system (e.g., a ring, a user device, a server) may receive physiological data associated with the user from a wearable device. The physiological data may include at least temperature data, and may also include heart rate data and other physiological measurements or derived values. The temperature data may be collected continuously by the wearable device. Physiological measurements may be performed continuously throughout the day and / or night. For example, in some embodiments, the ring may be configured to continuously acquire physiological data (e.g., temperature data, sleep data, heart rate, MET data, etc.) according to one or more measurement cycles throughout the day / sleep day. In other words, the ring can continuously acquire physiological data from the user without considering the "trigger conditions" for performing such measurements. In some cases, continuous temperature measurements at the finger can capture temperature fluctuations (e.g., small fluctuations or large fluctuations) of the core temperature that may not be obvious. For example, continuous temperature measurements at the finger can capture temperature fluctuations every minute or hour, which can provide additional insights that other temperature measurements in other parts of the body may not provide, or if the user manually measures the body temperature once a day, these insights cannot be provided.

[0142] Figure 4 The timing diagram 400-a shown in shows the relative timing of the nighttime temperature minimum 415 associated with the number of minutes before midnight. For example, the nighttime temperature minimum may be a value of approximately 35.9 degrees Celsius at 150 minutes after midnight (e.g., 2:30 a.m.). In some cases, the temperature chronotype may be determined based on the nighttime time associated with the nighttime temperature minimum 415. For example, a temperature chronotype that characterizes a user as a "morning type" user may include a nighttime temperature minimum 415 at the midpoint of sleep, while a temperature chronotype that characterizes the user as an "evening type" user may include a nighttime temperature minimum 415 in the second half of the user's sleep (e.g., after the midpoint of sleep). In some cases, the difference between the nighttime times of the temperature minimums 415 for a "morning type" user and a "evening type" user may be two hours.

[0143] The system can use the aggregate temperature data 405 time series to fit a spline curve 410 to the aggregate temperature data 405. In this case, the system can use the aggregate temperature data 405 to characterize the fitted spline curve 410 into different categories. For example, Figure 4The timing diagram 400-a shown in FIG. 4 shows a u-shaped fitted spline curve 410. The u-shaped fitted spline curve 410 can show that the aggregate temperature data 405 decreases, reaches a minimum value (eg, temperature minimum value 415), and increases before the wake-up time.

[0144] In other examples, the fitted spline curve 410 may include a constant downward slope. In this case, the aggregate temperature data 405 may continue to follow a downward trend throughout the night. In other examples, the fitted spline curve 410 may include a constant upward slope throughout the night.

[0145] In some examples, the system may determine that the received temperature data does not meet a threshold. In this case, the system may identify the user as a "rare synchronizer," where the system may not have received enough temperature data to determine the aggregated temperature data 405, the fitted spline 410, the temperature minimum 415, or a combination thereof. In some cases, clusters of the user's aggregated temperature data 405 may appear within a tight (e.g., narrow) range, such that the ups and downs of the time series (e.g., aggregated temperature data 405) may include lower accuracy and reliability. A tight range may be an example of a minimum temperature change along the y-axis. In this case, the system may discard the temperature data and avoid using the temperature data to determine the circadian chronotype.

[0146] Figure 4 The timing diagram 400-b shown in the diagram shows the relative timing of sleep patterns with respect to weekends, weekdays, and both. For example, the timing diagram 400-b shows the relationship between the user's sleep and a certain day of the week. In this regard, the dotted line shown in the timing diagram 400-b can be understood to refer to "sleep duration 420". Sleep duration 420 can include wake-up time, bedtime, sleep duration, or a combination thereof. In this regard, the black line shown in the timing diagram 400-b can be understood to refer to "interquartile range 425". In some cases, the system can determine or estimate the sleep chronotype, circadian rhythm chronotype, or both based on the sleep pattern data.

[0147] The system can use sleep pattern data to characterize users into different categories (e.g., sleep chronotypes). Figure 4The timing diagram 400-b shown in shows a regular sleeper. In this case, the sleep duration 420 for weekends and weekdays can be consistent between weekends and weekdays to indicate that the user wakes up at the same (e.g., consistent) time every day and goes to bed at the same (e.g., consistent) time every day. For example, the bottom of the sleep duration 420 on weekends can be aligned with the bottom of the sleep duration 420 on weekdays. The top of the sleep duration 420 on weekends can be aligned with the top of the sleep duration 420 on weekdays. The wake-up time can be shown as the top of the sleep duration 420, and the bedtime can be shown as the bottom of the sleep duration 420. The interquartile range 425 can represent the variability of sleep data over a period of time. In this case, a shorter interquartile range 425 may indicate that the change in bedtime and wake-up time is small, while a longer interquartile range 425 may indicate that the change in bedtime and wake-up time is large.

[0148] In some cases, the timing diagram may illustrate a person with irregular sleep. In this case, the weekend and weekday sleep durations 420 may not be consistent between weekends and weekdays to indicate that the user wakes up at different times on weekends and / or weekdays and goes to bed at different times on weekends and / or weekdays. For example, the bottom of the weekend sleep duration 420 may not align with the bottom of the weekday sleep duration 420 (e.g., the bottom of the weekend sleep duration 420 is longer or shorter than the bottom of the weekday sleep duration 420). The top of the weekend sleep duration 420 may not align with the top of the weekday sleep duration 420 (e.g., the top of the weekend sleep duration 420 is longer or shorter than the top of the weekday sleep duration 420). In this case, the interquartile range 425 may be longer than the interquartile range 425 shown in the timing diagram 400-b, so that the interquartile range 425 indicates a higher variability in the sleep pattern data.

[0149] In some cases, the time series graph may illustrate a person with irregular sleep patterns. In this case, the sleep duration 420 on weekdays may be shorter than the sleep duration on weekends. For example, the time series graph may indicate that the user accumulates sleep deprivation during weekdays and sleeps longer on weekends. For example, the bottom and top of the sleep duration 420 on weekdays may not align with the bottom and top of the sleep duration 420 on weekends, respectively (e.g., be shorter than the bottom and top of the sleep duration 420 on weekends). In this case, the interquartile range 425 may be shorter, so that the interquartile range 425 indicates that there is less variability in the sleep pattern data between weekends and weekdays.

[0150] In some cases, the system may determine a sleep regularity index. The sleep regularity index may indicate how evenly a user sleeps. In some cases, the sleep regularity index may take into account naps recorded by the user or received by the system. In some examples, irregular bedtime and wake-up times may be associated with an increased risk of different diseases. The sleep regularity index may measure the consistency of a user's sleep timeline, so users who frequently change their sleep times and light / dark exposure patterns may experience misalignment between the circadian system and the sleep / wake cycle. Irregular sleep may reduce a user's daily performance and cognitive function and be associated with risk factors that threaten health. In this case, having a regular sleep pattern may be beneficial to the user's overall health. In some cases, adhering to a user's established sleep pattern may be a factor that affects the user's sleep quality, and therefore a factor that affects the user's sleep score and readiness score.

[0151] Figure 4 The timing diagram 400-c shown in the diagram shows the relative timing of activity patterns related to the time of day for a single calendar day. For example, the timing diagram 400-c shows the relationship between the average MET of a user and the time of day. In this regard, the solid line shown in the timing diagram 400-c can be understood to refer to "activity data 430". In some cases, the system can determine or estimate a MET chronotype, a circadian rhythm chronotype, or both based on the activity data 430.

[0152] The system can use the number of average METs (e.g., activity data 430) and the time of day associated with the average METs to characterize the user into different categories (e.g., activity chronotypes). Figure 4 The time series diagram 400-c shown in FIG. 4 shows a user who is active in the morning. The aggregated MET time series (e.g., activity data 430) can illustrate how active the user is over the entire day. The peak of activity data 430 is before 10:00 am, indicating that the user is an active user in the morning.

[0153] In other examples, activity data 430 may illustrate a situation where the user is an active user at night. In this case, activity data 430 may include lower MET values ​​in the morning, and as the day continues, the MET values ​​increase. For example, the peak of activity data 430 may be during the night. In some cases, activity data 430 may illustrate a situation where the user is an inactive user. In this case, activity data 430 may remain constant (e.g., stable) throughout the day. For example, the average MET value may be low and the same throughout the day (e.g., a horizontal line along the x-axis).

[0154] In some examples, activity data 430 may illustrate situations in which a user may be active at specific times of the day. For example, activity data 430 may include one or more distinguishable peaks throughout the day. In some cases, a single distinguishable peak may indicate that a user is regularly active at a specific time (e.g., at the same time during more than one day, week, month, etc.). In other examples, a peak with a wider bandwidth and lower amplitude may indicate that a user is regularly active within a certain time range for a certain duration.

[0155] In some examples, the system may determine that the received activity data 430 does not meet a threshold. In this case, the system may identify the user as a "non-permanent wearer" or a "night wearer," where the system may not receive enough activity data 430 to determine an activity chronotype. For example, during the day, activity data 430 may be unavailable or partially unavailable. In this case, the system may discard activity data 430 and avoid using activity data 430 to determine a circadian chronotype.

[0156] Figure 5 An example of a graphical representation 500 supporting techniques for determining a circadian chronotype according to aspects of the present disclosure is shown. The graphical representation 500 may implement aspects of or be implemented by the system 100, the system 200, the system 300, the timing diagram 400, or any combination thereof. For example, the graphical representation 500 may be displayed on a GUI 275 of a user device 106 (e.g., user devices 106-a, 106-b, 106-c) corresponding to the user 102.

[0157] The graphical representation 500 may include a circular representation 505 of a twenty-four hour time span. For example, the system may overlay one or more physiological parameters or an aggregation or representation of one or more physiological parameters onto the twenty-four hour time span. In one non-limiting example, the system may overlay an average of at least continuous nighttime temperature data, activity data, sleep pattern data, or some combination or subset of these data over a period of time (e.g., the past 60 or 90 days) against the circular representation 505 of the twenty-four hour time span. In some cases, the circular representation 505 may include other shapes, such as rectangles, ellipses, triangles, etc.

[0158] In some cases, the graphical representation 500 may include a first segment 510, a second segment 515, and a third segment 520. The first segment 510 may include an average of continuous nighttime temperature data over a period of time. In this case, the system 200 may cause the GUI 275 of the user device to display the first segment 510 of the circular representation 505 of the twenty-four hour time span, the first segment including the average of continuous nighttime temperature data over a period of time. In some examples, the first segment 510 may include an arched segment that represents the average of continuous nighttime temperature data over a period of time as a color gradient that indicates changes in nighttime temperature throughout the duration of sleep.

[0159] For example, the continuous nighttime temperature may be contained in an outermost circular line with a color gradient. The color gradient may be an example of a red-blue gradient, where the red segments represent higher nighttime temperatures than the blue segments, and the blue segments represent lower nighttime temperatures. For example, among users who experience higher nighttime temperatures at midnight, the red segments may be displayed toward the center of the first segment 510, and the blue segments may be displayed toward the end of the first segment 510. In other examples, the color gradient may be an example of a blue gradient, where the dark blue segments represent lower nighttime temperatures than the light blue segments, and the light blue segments represent higher nighttime temperatures. For example, the dark blue segments may be displayed toward the center of the first segment 510, and the light blue segments may be displayed toward the end of the first segment 510. In this case, the color gradient of the first segment 510 may allow the user to quickly and efficiently identify the night time when the lowest nighttime temperature occurs. The time and value of the lowest nighttime temperature may indicate whether the user is a morning person or a night person. The first segment 510 may include an average of the continuous nighttime temperature data for the past 30 days, 60 days, or 90 days, or some other configurable time period (including weekends and weekdays).

[0160] In some cases, the second segment 515 may include an average of the activity data over a period of time. In this case, the system 200 may cause the GUI 275 of the user device to display the second segment 515 of the circular representation 505 of the twenty-four hour time span including the average of the activity data over the period of time. In some examples, the second segment 515 may include a curved segment that represents the average of the activity data over the period of time as a graphical plot that indicates relative changes in activity levels over the twenty-four hour time span.

[0161] For example, the graphical representation 500 may include activity tracking. For users who are active at regular times of the day, the second segment 515 may include a certain shape. For example, during the period when the user is sleeping, the second segment 515 may not be visible to the user, indicating that the user is not active during the night time period. Figure 5As shown, the second segment 515 may indicate that the user is active in the morning (e.g., between 8:00-9:00 a.m.), so that the second segment 515 is most noticeable during the morning hours. The second segment 515 may extend from the periphery of the circular representation 505 inwardly toward the center of the circular representation 505. In some cases, the second segment 515 may be displayed during the evening hours, indicating that the user is active during the evening hours. The shape volume of the second segment 515 may be associated with the amount of activity. For example, a larger volume of the second segment 515 may indicate that the user is more active than other time periods of the day. The second segment 515 may include an average of activity data for the past 30 days, 60 days, or 90 days, or some other configurable time period (including weekends and weekdays).

[0162] In some cases, the third segment 520 may include an average of the sleep pattern data over a period of time. In this case, the system 200 may cause the GUI 275 of the user device to display a third segment 520 of the circular representation 505 of the twenty-four hour time span, which includes the average of the sleep pattern data over a period of time. In some examples, the third segment 520 may include a wedge-shaped segment that represents the average of the sleep pattern data over a period of time. The third segment 520 may include a first side 525 indicating the time when the user fell asleep, a second side 530 indicating the time when the user woke up, and a third curved side 535 adjacent to the circular representation 505 of the twenty-four hour time span. In this case, the third segment 520 may indicate a bedtime, a wake-up time, a sleep duration, or a combination thereof.

[0163] For example, the graphical representation 500 may indicate that the user falls asleep at 10:00 p.m. and wakes up at 8:00 a.m. The clarity of the first side 525 and the second side 530 may indicate the regularity of the sleep data. For example, if the first side 525 and / or the second side 530 include blurred or less distinct lines, this may be a visual indication that the user has irregular bedtimes, wake-up times, or both. In other examples, if the first side 525 and / or the second side 530 include clear or visible or distinct lines, this may be a visual indication that the user has regular bedtimes, wake-up times, or both. The third segment 520 may include an average of the sleep pattern data for the past 30, 60, or 90 days, or some other configurable time period (including weekends and weekdays).

[0164] In some examples, the graphical representation 500 may include one or more parameters 540. For example, the one or more parameters 540 may be an example of an indication of the current time of day. In other examples, the one or more parameters 540 may be an example of a message or alarm indicating heart rate data, menstrual cycle indication, respiratory data, or a combination thereof. In this case, the system may cause the GUI 275 of the user device to display the one or more parameters 540 for a circular representation 505 for a twenty-four hour time span, the circular representation 505 including the heart rate data, menstrual cycle indication, respiratory data, or a combination thereof averaged over a period of time. In some cases, the one or more parameters 540 may overlay the graphical representation 500 for the circular representation 505 for a twenty-four hour time span.

[0165] Graphical representation 500 may allow a user to visualize their long-term habits on a twenty-four hour user interface component. Graphical representation 500 may indicate the user's bedtime and wake-up times, nighttime temperature, and activity data. In this case, the system may provide the user with insights into key variables that influence the determination of a circadian rhythm profile (e.g., circadian chronotype). Graphical representation 500 may be an example of a report generated to show a picture of a user's body clock changes, seasonal changes, travel effects, lifestyle habits, or a combination thereof.

[0166] In some cases, the graphical representation 500 may include a static rendering. By applying a smoothing function to the activity data (e.g., the second segment 515) and blurring the exact boundaries of the user's bedtime and wake-up times (e.g., the first side 525 and the second side 530 of the third segment 520, respectively), the system can display an interactive and useful tool to the user to gain insight into the user's lifestyle. The patterns determined based on the received bio-signals (e.g., physiological data) can classify the users into multiple categories, including but not limited to morning people, evening people, highly active people, inactive people, or combinations thereof. In this case, the graphical representation 500 can classify the users into users who are very aligned with their circadian chronotypes and users who are not aligned with their circadian chronotypes.

[0167] Figure 6 An example of a system 600 supporting techniques for determining a circadian rhythm chronotype according to aspects of the present disclosure is shown. The system 600 may implement or be implemented by the system 100, the system 200, the system 300, or a combination thereof. Specifically, the system 600 shows an example of a ring 104 (e.g., a wearable device 104), a user device 106, and a server 110, as shown in FIG. Figure 1 described.

[0168] System 600 may include an algorithm for chronotype characterization. In this case, system 600 may determine a circadian chronotype from one or more data sources. As further described herein, if system 600 receives an amount of data that satisfies a threshold, system 600 may determine a circadian chronotype. If system 600 determines that the amount of data does not satisfy the threshold, system 600 may refrain from determining a circadian chronotype. System 600 may include one or more processing pipelines and a collective estimate for each processing pipeline.

[0169] At 605, the system 600 may receive input parameters. The input parameters may include a user identification (e.g., an identity of a user), a history length (e.g., a number of days that the system 600 receives data), a timeline (e.g., a start date for receiving physiological data and an end date for receiving physiological data), a configuration parameter, a data threshold, or a combination thereof.

[0170] At 610, the system 600 may receive sleep data. For example, the system 600 may receive physiological data associated with the user from the wearable device over a period of time. The time period may be an example of 90 consecutive calendar days. The physiological data may include at least sleep pattern data. In some cases, the sleep pattern data may include sleep regularity data. In this case, the system 600 may load sleep summary data (e.g., sleep data, sleep pattern data, or both) within a time range (e.g., a time period) and process the sleep summary data. In some examples, the sleep pattern data may include at least the time the user falls asleep each night (or the time when he or she goes to bed but has not fallen asleep yet), the time the user wakes up in the morning, the duration of sleep, or a combination thereof.

[0171] For example, the system 600 may receive from the wearable device a first set of physiological data measured by the wearable device from the user and collected over a period of time. The first set of physiological data may include at least sleep data. In some examples, the system 600 may receive from the wearable device a second set of physiological data measured by the wearable device from the user during a sleep day of the previous day. For example, the second set of physiological data may include sleep pattern data from the previous night. In this case, the previous night may be an example of the night immediately before the current calendar day. In some examples, the sleep data in the first set of physiological data and the second set of physiological data may include sleep data obtained from naps taken in one or more calendar days. For example, the first set of physiological data may include sleep data from a nap measured over a period of time (e.g., 90 consecutive calendar days). The second set of physiological data may include sleep data from a nap measured from the previous calendar day immediately before the current calendar day.

[0172] At 615, the system 600 may check a data threshold. For example, the system 600 may determine whether the amount of sleep data received meets a threshold. The system 600 may determine that the amount of sleep data received meets (e.g., equals or exceeds) a threshold. In other examples, the system 600 may determine that the amount of sleep data received does not meet a threshold. In this case, the system 600 may avoid extracting sleep data. The system 600 may determine that the system 600 does not contain a sufficient amount of data to estimate the circadian rhythm time type. For example, the system 600 may determine that the wearable device may not be worn frequently during the time period, the wearable device received less than 30 sleep measurement instances during the time period, the wearable device was partially worn during sleep, the wearable device cannot be synchronized, high-frequency data is overwritten, the user travels across different time zones, so that the system 600 may not be able to collect enough data in the same time zone, or a combination of the above.

[0173] The threshold may be an example of a history length that indicates the number of days that the system 600 received sleep data. In this case, the threshold may be predetermined because the system 600 receives the threshold at 605 before receiving the sleep data at 610. The system 600 may identify the history length to determine whether the amount of sleep data received meets the threshold. In some examples, the threshold may be 90 consecutive nights in the same time zone, more than 30 sleep measurement instances within 90 consecutive nights, or both. However, the threshold may be configurable and / or changed over time by the user or the system 600.

[0174] At 620, the system 600 can extract sleep data. For example, the system 600 can discard sleep data that may be affected by jet lag (e.g., spanning two or more time zones). In this case, the system 600 can extract and discard sleep data measured in time zones that are more than one hour away from the most commonly occurring time zone. The system 600 can extract sleep data based on determining that the received sleep data includes a minimum gap between measurements (e.g., 90 consecutive nights of receiving sleep data). In some cases, the system 600 can extract sleep data in response to checking a data threshold and determining that the data meets the threshold.

[0175] The system 600 can extract the last sleep data of length "n" (e.g., a historical length). In this case, the system 600 can narrow the subset of sleep data to continue processing (e.g., including the measurement date). For example, the system 600 can extract sleep data measured within a historical length (e.g., a time period). In some cases, the system 600 can extract and discard sleep data to avoid outliers in the sleep data from affecting circadian rhythm time type estimation. In other examples, the system 600 can extract sleep data associated with sleep sessions longer than 3 hours and / or extract a single sleep session per calendar day. Extracting sleep data at 620 can trigger a data processing pipeline for temperature data and / or MET data, as described herein.

[0176] At 625, the system 600 can derive sleep metrics. For example, the system 600 can determine that the sleep data (e.g., sleep pattern data) includes wake-up time, bedtime, sleep duration, or a combination thereof. In this case, the system 600 can identify the user's average wake-up time, average bedtime, average sleep duration, or a combination thereof over a period of time.

[0177] In some examples, the system 600 may identify a median bedtime, a median wakeup time, a standard deviation of sleep midpoints, or a combination thereof. In this case, the system 600 may process the sleep pattern data in the first set of physiological data to extract at least a standard deviation of sleep midpoints, a median wakeup time at which a user wakes up, a median bedtime at which a user falls asleep, or a combination thereof. For example, the system 600 may extract a median bedtime, a median wakeup time, a standard deviation of sleep midpoints, an average wakeup time, an average bedtime, an average sleep duration, or a combination thereof from the sleep data.

[0178] In some cases, the system 600 can input the first set of physiological data into a machine learning model. For example, the system 600 can input sleep data, sleep metrics, extracted sleep data, or a combination thereof into the machine learning model. The system 600 can classify the first set of physiological data into a circadian chronotype using the machine learning model in response to inputting the first set of physiological data into the machine learning model. For example, the system 600 can use the derived sleep metrics and a linear regression model to estimate the circadian chronotype, as described herein.

[0179] At 630, the system 600 may receive temperature data. For example, the system 600 may receive physiological data associated with the user from the wearable device over a period of time. The physiological data may include at least continuous nighttime temperature data, continuous daytime temperature data, or both. In this case, the system 600 may load the continuous nighttime temperature data within a time range (e.g., the time period) and process the continuous nighttime temperature data. The continuous nighttime temperature data may be loaded and processed based on the extracted sleep data. For example, the system 600 filters the data based on the reduced (e.g., extracted) sleep time. In this case, the system 600 may discard the daytime temperature data and store the nighttime temperature data.

[0180] At 635, the system 600 may aggregate the temperature data. For example, the system 600 may generate a pool of measurements (e.g., including temperature values ​​and timestamps of the temperature values) over the course of weeks or months of sleep data. In some cases, the system 600 may discard temperature values ​​with nighttime peaks and / or baseline variations that may indicate outliers compared to the rest of the temperature data. In some examples, the system 600 may record continuous nighttime temperature data for the past 90 nights of sleep data. In this case, the system 600 may record temperature data corresponding to the same calendar day that the sleep data was recorded (e.g., 90 nights of sleep data). The temperature time series may be filtered to include data that can be measured at bedtime.

[0181] At 640, the system 600 may check a data threshold. For example, the system 600 may determine whether the amount of temperature data received meets a threshold. In some examples, the system 600 may determine that the amount of temperature data received meets (e.g., equals or exceeds) a threshold. The system 600 may determine that the amount of temperature data received does not meet the threshold. In this case, the system 600 may avoid deriving a temperature indicator. For example, the system 600 may determine that the system 600 does not contain a sufficient amount of data to estimate a circadian chronotype.

[0182] In this case, the system 600 may determine that the wearable device may not be frequently worn during the time period, the wearable device received less than 30 sleep measurement instances during the time period, the wearable device was partially worn during sleep, the wearable device failed to synchronize, high-frequency data was overwritten, the user moved across different time zones such that the system 600 may not be able to collect sufficient data in the same time zone, or a combination of these situations. For example, if the high-frequency data is overwritten, skin temperature data may be missing from the received physiological parameters.

[0183] The threshold may be an example of a history length that indicates the number of days that the system 600 receives temperature data. In this case, the threshold may be predetermined because the system 600 receives the threshold at 605 before receiving the temperature data at 630. The system 600 may identify the history length to determine whether the amount of temperature data received meets the threshold. In some examples, the threshold may be 90 consecutive nights in the same time zone. However, the threshold may be configurable and / or changed over time by the user or the system 600.

[0184] At 645, the system 600 may derive a temperature indicator. For example, the system 600 may derive a temperature indicator in response to checking a data threshold. The system 600 may process the continuous nighttime temperature data by the application to extract at least one average skin temperature, an average skin temperature of five highest temperature values ​​for a continuous twenty-four hour time span, an average skin temperature of five lowest temperature values ​​for a continuous twenty-four hour time span, or a combination thereof. For example, the system 600 may generate a daily temperature rhythm for the user to derive the temperature indicator. In this case, the system 600 may derive the time of day of the average skin temperature of the five highest temperature values ​​for a continuous period of a twenty-four hour time span, the time of day of the average skin temperature of the five lowest temperature values ​​for a continuous period of a twenty-four hour time span, the average skin temperature, or a combination thereof.

[0185] In some cases, the system 600 may input the temperature data, the temperature index, the extracted temperature data, or a combination thereof into a machine learning model. The system 600 may classify the first set of physiological data into a circadian chronotype using the machine learning model in response to inputting the first set of physiological data into the machine learning model. For example, the system 600 may estimate the circadian chronotype using the derived temperature index and a linear regression model, as described herein.

[0186] At 650, the system 600 may receive MET data. For example, the system 600 may receive physiological data associated with the user from the wearable device over a period of time. The physiological data may include at least activity data. In this case, the system 600 may load the MET data and process the MET data. The MET data may be loaded and processed based on the extracted sleep data.

[0187] At 655, the system 600 may aggregate the MET data. At 660, the system 600 may check a data threshold. For example, the system 600 may determine whether the amount of MET data received meets a threshold. The system 600 may determine that the amount of MET data received meets (e.g., equals or exceeds) a threshold. In other examples, the system 600 may determine that the amount of MET data received does not meet a threshold. In this case, the system 600 may avoid deriving a MET indicator. For example, the system 600 may determine that the system 600 does not include a threshold amount of data for estimating a circadian chronotype.

[0188] In this case, the system 600 can determine that the system 600 does not include a sufficient amount of data to estimate the circadian chronotype. For example, the system 600 can determine that the wearable device may not be worn frequently during the time period, the wearable device received less than 30 sleep measurement instances during the time period, the wearable device was partially worn during sleep, the wearable device failed to synchronize, high-frequency data was overwritten, the user moved across different time zones such that the system 600 may not be able to collect sufficient data in the same time zone, or a combination of the above. Partially wearing the wearable device during sleep may result in missing MET data during the day. In this case, the system 600 can avoid extracting features from the user's MET data and / or physical activity data during the day. If the wearable device is not synchronized frequently enough, MET data may be missing from the received physiological data.

[0189] The threshold value may be an example of a history length that indicates the number of days that the system 600 receives MET data. In this case, the threshold value may be predetermined such that the system 600 receives the threshold value at 605 before receiving the MET data at 650. The system 600 may identify the history length to determine whether the number of METs received meets the threshold value. In some examples, the threshold value may be 90 consecutive days of the same time. The system 600 may record MET data including the last 90 nights of sleep data. In this case, the system 600 may record MET data corresponding to the same calendar day that the sleep data was recorded (e.g., the last 90 nights of sleep data).

[0190] At 665, the system 600 may derive a MET indicator. For example, the system 600 may derive a MET indicator in response to determining that the MET data satisfies a threshold. The system 600 may process the activity (e.g., MET) data of the first set of physiological data by the application to extract at least an average MET value, a time of user activity, or both. For example, the system 600 may calculate a rest-activity rhythm and extract (e.g., derive) a MET indicator. In this case, the system 600 may extract from the rest-activity rhythm an average MET value for the ten most active consecutive hours within a twenty-four hour time span, an average MET value for the five least active consecutive hours within a twenty-four hour time span, a midpoint MET value for the ten most active consecutive hours within a twenty-four hour time span, a midpoint MET value for the five least active consecutive hours within a twenty-four hour time span, a time of measuring maximum physical activity, or a combination thereof.

[0191] In some cases, the system 600 may input the MET data, the MET index, the extracted MET data, or a combination thereof into a machine learning model. The system 600 may classify the first set of physiological data into a circadian chronotype using the machine learning model in response to inputting the first set of physiological data into the machine learning model. For example, the system 600 may use the derived MET index and a linear regression model to estimate the circadian chronotype, as described herein.

[0192] At 670, the system 600 may determine a circadian chronotype based on the sleep data, the temperature data, the MET data, or a combination thereof. In some examples, the system 600 may determine a circadian chronotype based on the derived metrics of sleep, temperature, METs, or a combination thereof. In some cases, the circadian chronotype may be determined in response to inputting the physiological data (e.g., the first set of physiological data) into a machine learning classifier.

[0193] In some cases, the system 600 may output a number between 16 and 86 corresponding to a Morningness-Eveningness Questionnaire (MEQ) score, where 16 represents the most extreme evening user and 86 represents the most extreme morning user. The estimated MEQ score may be mapped to a sleep midpoint. For example, in response to determining an estimated circadian chronotype, the system 600 may determine the user's sleep midpoint. The sleep midpoint may be measured from midnight. The sleep midpoint (e.g., including the sleep-wake cycle) may be affected by the circadian chronotype. In this case, the system 600 may determine the sleep midpoint based on the user's circadian chronotype. In some cases, the sleep midpoint of a morning user may have an earlier sleep midpoint than the sleep midpoints of an intermediate user and an evening user.

[0194] The system 600 can determine a relationship between the MEQ score and the sleep midpoint. For example, the system 600 can estimate the circadian chronotype and determine the relationship between the MEQ score and the sleep midpoint in response to determining the circadian chronotype. In some cases, the relationship between the MEQ score and the sleep midpoint can be linear. In this case, the linear relationship can create a mapping to associate the optimal sleep midpoint with each MEQ score.

[0195] System 600 can determine a circadian chronotype based on an amount of measured sleep data meeting a threshold over a period of time. System 600 can fuse estimates from different sources into a collective estimated chronotype of circadian chronotype. Thus, by enabling more complete and accurate circadian chronotype determinations, the techniques described herein can enable system 600 to provide better insights and guidance to a user that better correlates with the user's overall health.

[0196] As referenced in this article Figure 7As described above, the system 600 can compare the determined circadian rhythm chronotype with the received second set of physiological data (e.g., sleep data from the previous night). The system 600 can cause the GUI of the user device to display a message related to the comparison, the determined circadian rhythm chronotype, the received second set of physiological data, or a combination thereof, such as a reference to Figure 8 described.

[0197] Figure 7 An example of a graphical representation 700 supporting techniques for determining a circadian chronotype according to aspects of the present disclosure is shown. The graphical representation 700 may implement or be implemented by aspects of the system 100, the system 200, the system 300, the timing diagram 400, the system 600, or any combination thereof. For example, the graphical representation 700 may be displayed on a GUI 275 of a user device 106 (e.g., user devices 106-a, 106-b, 106-c) corresponding to the user 102.

[0198] The graphical representation 700 may include a circular representation 705 of a twenty-four hour time span. For example, the system may overlay one or more physiological parameters or an aggregation or representation of one or more physiological parameters onto the twenty-four hour time span. In one non-limiting example, the system may overlay the determined circadian chronotype and sleep data from the previous night's sleep onto the circular representation 705 of the twenty-four hour time span, as shown in FIG. Figure 6 described.

[0199] In some cases, the graphical representation 700 may include a first segment 710 representing sleep pattern data for a previous night's sleep and a second segment 715 representing a determined circadian rhythm chronotype. The first segment 710 may include a wake-up time when the user woke up that day, a bedtime when the user fell asleep the previous night, a midpoint 720-a of the user's previous night's sleep, a sleep duration of the previous night, or a combination thereof. For example, the graphical representation 700 may indicate that the user fell asleep at 10:00 p.m. the previous night and woke up at 6:00 a.m. The midpoint 720-a may indicate that the midpoint of the user's sleep was 2:15 a.m. The first side may indicate the time when the user fell asleep, and the second side may indicate the time when the user woke up.

[0200] In this case, the system can cause the GUI 275 of the user device to display a first segment 710 of a circular representation 705 of a twenty-four hour time span containing the sleep pattern data for the previous night. In some examples, the first segment 710 can include an arched segment representing the sleep pattern data for the previous night. For example, the sleep pattern data for the previous night can be included in the outermost circular line having a first color. The first segment 710 can include a midpoint 720-a representing the midpoint of the user's sleep for the previous night. In this case, the midpoint 720-a of the first segment 710 can allow the user to quickly and efficiently identify the nighttime time at which the user's sleep midpoint occurs. The time of the midpoint 720-a can indicate whether the user is a morning person or a night person.

[0201] In some cases, the graphical representation 700 may include a second segment 715 that represents an average of sleep pattern data in the first set of physiological data over a period of time. The second segment 715 may include an average awake time for a user to wake up, an average bedtime for a user to fall asleep, an average sleep midpoint 720-b time, an average sleep duration, or a combination thereof. For example, the graphical representation 700 may indicate that the user falls asleep at 11:00 p.m. on average and wakes up at 7:00 a.m. The midpoint 720-b may indicate that the average midpoint of a user's sleep is 2:45 a.m. The first side may indicate the average time a user falls asleep, and the second side may indicate the average time a user wakes up.

[0202] In this case, the system may cause the GUI 275 of the user device to display a second segment 715 of the circular representation 705 of the twenty-four hour time span, which includes an average of the sleep pattern data in the first set of physiological data over the time period. In some examples, the second segment 715 may include an arched segment that represents the average of the sleep pattern data in the first set of physiological data over the time period. For example, the average sleep pattern data may be included in the innermost circular line having a second color different from the first color. The second segment 715 may include a midpoint 720-b that represents the user's average sleep midpoint. In this case, the second segment 715 may allow the user to quickly and efficiently compare the sleep pattern data of the previous night with the circadian rhythm time type determined for the user. In this case, the first segment 710 can be easily compared with the second segment 715 to determine whether the user's sleep from the previous night is aligned with the determined circadian rhythm time type.

[0203] For example, the system can compare the determined circadian chronotype (e.g., second segment 715) to the received second set of physiological data (e.g., first segment 710). The system can compare one or more features of the determined circadian chronotype to the received sleep data from a previous night's sleep. For example, the system can compare sleep data associated with the determined circadian chronotype to the received sleep data from a previous night's sleep.

[0204] In this case, the average of the sleep pattern data in the first set of physiological data over a period of time can be compared with the sleep pattern data collected on the previous sleep day. For example, the system can compare the average wake-up time of the user to the wake-up time of the previous night, the average bedtime of the user to the bedtime of the previous night, the average sleep duration to the sleep duration of the previous night, the average sleep midpoint to the sleep midpoint of the previous night, or a combination thereof.

[0205] In some examples, the graphical representation 700 may include one or more parameters. For example, one or more parameters may be an example of an indication of the current time of day. In other examples, one or more parameters may be an example of a message or alarm indicating heart rate data, menstrual cycle indication, breathing data, activity data, temperature data, or a combination thereof. In this case, the system may cause the GUI 275 of the user device to display one or more parameters for a circular representation 705 of a twenty-four hour time span. In some cases, the one or more parameters may overlay the graphical representation 700 with respect to the circular representation 705 of a twenty-four hour time span. In some cases, the graphical representation 700 may be an example of a generated report for displaying a picture of a user's circadian clock changes, seasonal changes, the effects of travel, lifestyle habits, or a combination thereof.

[0206] Graphical representation 700 can allow a user to visualize long-term habits relative to the user's previous night's sleep on a twenty-four hour clock user interface component. Graphical representation 700 can indicate the user's bedtime and wake-up times relative to their determined circadian chronotype. In this case, the system can provide the user with insights into key variables that influence the determination of the circadian profile (e.g., circadian chronotype) and provide recommendations for the current day (e.g., activity, sleep, etc.) by comparing the first segment 710 (e.g., the previous night's sleep data) with the second segment 715 (e.g., the determined circadian chronotype).

[0207] In some cases, the graphical representation 700 may include a static rendering. In this case, the system may display interactive and useful tools to the user to gain insight into the user's lifestyle. The patterns determined from the received bio-signals (e.g., physiological data) may classify the user into multiple categories, including, for example, but not limited to, morning people, evening people, highly active people, inactive people, or combinations thereof. In this case, the graphical representation 700 may classify the users into users who are well aligned with their circadian chronotypes and users who are not aligned with their circadian chronotypes.

[0208] Figure 8An example of a GUI 800 supporting techniques for determining a circadian chronotype according to aspects of the present disclosure is shown. The GUI 800 may implement or be implemented by aspects of the system 100, the system 200, the system 300, the timing diagram 400, the system 600, or any combination thereof. For example, the GUI 800 may be an example of a GUI 275 corresponding to a user device 106 (e.g., user devices 106-a, 106-b, 106-c) of the user 102.

[0209] In some examples, GUI 800 shows a series of application pages 802 that can be accessed through GUI 800 (e.g., Figure 2 275) is displayed to the user 102. The system can generate a personalized tracking experience on the GUI 275 of the user device 106 to determine the circadian rhythm chronotype. Continuing with the above example, after determining the circadian rhythm chronotype, the user 102 can be presented with the application page 802-a through the GUI 800 when opening the wearable application 250. The GUI 800 can display an alert 805, a graphical representation 810, a message 815, or a combination thereof. The graphical representation 810 can be a reference Figure 5 Graphical representation of description 500, reference Figure 7 Examples of graphical representation 700, a portion of graphical representation 500, a portion of graphical representation 700, or a combination thereof are described.

[0210] In some embodiments, the user device and / or server may generate an alert 805 associated with the determined circadian chronotype and / or circadian chronotype misalignment, which may be displayed to the user via the GUI 800. In this case, the application page 802-a may display an indication of the determined circadian chronotype via the alert 805. For example, the application page 802-a may include the alert 805 on the home page.

[0211] In the event that the user's determined circadian chronotype is misaligned with the received physiological data, as described herein, the server may send an alert 805 to the user, wherein the alert 805 is associated with the misalignment. Specifically, the alert 805 generated and displayed to the user via the GUI 800 may be associated with the circadian chronotype misalignment and a suggestion to return to the user's baseline determined circadian chronotype. In some cases, the alert 805 may display a suggestion on how to adjust their lifestyle on the day of the determined misalignment and / or in the days following the determined misalignment.

[0212] For example, after determining the circadian chronotype, the system can receive additional physiological data associated with the user from the wearable device. In response to receiving the additional physiological data, the system can determine a misalignment between the received additional physiological data and the determined circadian chronotype. In this case, the system can determine a deviation from the determined circadian chronotype (e.g., a circadian misalignment).

[0213] In response to determining a misalignment between the received additional physiological data and the determined circadian rhythm, the user may receive an alert 805, which may indicate a message related to the misalignment. For example, the alert 805 may indicate to the user when the user's physiological data deviates from the determined circadian rhythm chronotype. In this case, the system may cause the GUI 800 of the user device to display the alert 805, the message 815, or both related to the misalignment. The alert 805 may be configurable / customizable so that the user may receive different alerts 805 based on the determined circadian rhythm chronotype, the misalignment, or both. In some cases, the alert 805 may indicate the impact of the user's menstrual cycle on the determined circadian rhythm chronotype.

[0214] In some cases, the user may take remedial action to resolve the misalignment before the system displays alert 805. In this case, the system may receive physiological data related to the remedial action, and the system may refrain from displaying alert 805 (e.g., covering alert 805). In some examples, the system may adjust alert 805 based on receiving physiological data related to the remedial action.

[0215] In addition, in some embodiments, the application page 802-a may display one or more scores (e.g., sleep score, readiness score, activity goal progress) of the user on the corresponding date. In addition, in some cases, the misalignment can be used to update (e.g., modify) one or more scores associated with the user (e.g., sleep score, readiness score). That is, data related to the circadian rhythm time type misalignment can be used to update the user's score on the next calendar day after the misalignment is detected. In some cases, the readiness score can be updated based on the misalignment. In some cases, the message 815-a displayed to the user through the GUI 800 of the user device can indicate how the misalignment affects the overall score (e.g., overall readiness score) and / or individual contributing factors. The system can be configured to dynamically update and compare the sleep regularity index. The GUI 800 can display the sleep regularity index for the viewed time period.

[0216] refer to Figure 5, the application page 802-a can display a graphical representation 810. For example, the system can cause the GUI 800 of the user device to display a graphical representation 810 of at least the average of continuous nighttime temperature data, activity data, and sleep pattern data over a period of time. Based on the detected patterns, the system can be able to provide additional context and insights about the graphical representation 810, thereby increasing value to the user by helping the user understand the graphical representation 810.

[0217] refer to Figure 7 , application pages 802-a and 802-b can display graphical representations 810-a and 810-b, respectively. For example, the system can cause the GUI 800 of the user device to display a graphical representation 810-a of the sleep pattern data of the user's previous night's sleep compared to the determined circadian chronotype. Graphical representation 810-a can also include text indicating how the user's sleep midpoints align with the determined circadian chronotype, as described herein. Graphical representation 810-a can be an example of a portion of graphical representation 810-b.

[0218] In some cases, the graphical representation 810 can be socially shared by using a user interface tool for social sharing. The graphical representation 810 can provide the user with a momentary cross-section of each individual component of the graphical representation 810. For example, the message 815-a can indicate "You are very active and awake" or "You are sleeping and the temperature is falling" with respect to the graphical representation 810. In this case, the user can compare the long-term pattern with the pattern of today, yesterday, or last week. In some cases, the graphical representation 810 can include dynamic components so that different segments can become animated and / or highlighted as the user moves from one segment to another.

[0219] The GUI 800 may also include a message 815 that includes insights, suggestions, etc. related to the determined circadian chronotype. The server of the system may cause the GUI 800 of the user device to display the message 815 related to the determined circadian chronotype. The user device 106 may display suggestions and / or information related to the determined circadian chronotype via the message 815. As previously described herein, an accurately determined circadian chronotype may benefit the user's overall health because it provides the user with indicators that enable the user to understand how behavioral changes (e.g., improvements in sleep, exercise, diet, and mood) may help improve the user's overall health and reduce the occurrence of circadian chronotype misalignment.

[0220] The system may cause the GUI 800 of the user device 106 to display a message 815-a related to a comparison of the determined circadian chronotype and the received second set of physiological data (e.g., sleep pattern data from the previous night), the determined circadian chronotype, the received second set of physiological data, or a combination thereof. For example, the graphical representation 810-a may indicate an average of the sleep pattern data in the first set of physiological data over a period of time (e.g., used to determine the circadian chronotype), the sleep pattern data from the previous night, the average wake time that the user wakes up, the average bedtime that the user falls asleep, the average sleep midpoint time, the average sleep duration, or a combination thereof.

[0221] Continuing with the above example, after selecting graphical representation 810, application page 802-b may be presented to user 102 via GUI 800. Figure 8 As shown, application page 802-b can display a message 815-c related to the determined circadian rhythm time type. In this case, the system can cause the GUI 800 of the user device to display a message 815, which can provide suggestions to the user based on the determined circadian rhythm time type.

[0222] Application page 802-b may display message 815-c, which may indicate “You are physically active in the mornings. You go to bed relatively early, and you wake up early. Your sleep temperature reaches its lowest point near 3 o'clock” or “You are a very active morning type!”. In some cases, message 815-c may indicate “You are a night wolf” or “You are an early bird.” In other examples, message 815-c may indicate “You are a late morning type. You are more of a morning person, but not as extreme.” In this case, message 815-c may provide the user with insights about morning type individuals. For example, message 815-c may indicate “Morning people with early bedtimes have a lower risk of cardiovascular disease, less obesity, and a lower risk of mental health disorders (including depression, anxiety, etc.).”

[0223] In some cases, message 815-b may indicate how well each night's sleep is aligned with the user's recommended bedtime and wake time (e.g., relative to sleep pattern data). For example, message 815-b may indicate the user's sleep alignment and whether the user's previous night's sleep was aligned with a determined circadian rhythm type. In this case, message 815-b may indicate that the user's current sleep pattern data (e.g., previous night's sleep data) is ahead of the determined circadian rhythm chronotype, behind the determined circadian rhythm chronotype, or aligned with the determined circadian rhythm chronotype. For example, message 815-b may indicate "Your sleep midpoint was 46 minutes ahead of your chronotype."

[0224] In some examples, message 815-b may indicate “You are within 85% of your recommended pattern. Keep up the good work!” In some cases, graphical representation 810 may be configured to focus on various aspects by filtering or dimming other portions of graphical representation 810 and receiving specific insights about the focused aspects. For example, a user may highlight (e.g., select) activity data for graphical representation 810, and message 815 may indicate “Activity patterns show that you have regular activity during the morning hours, which corresponds well to your recommended activity window.”

[0225] In situations where the system may detect a circadian chronotype misalignment, message 815-b may provide suggestions to the user to improve their overall health. For example, the message may indicate "If you're feeling really low on energy, why not try switching to rest mode today," or "Since you're sleeping later than usual, take a break today." In such situations, accurately determining circadian chronotype and detecting misalignment may improve the accuracy and efficiency of readiness scores and activity scores.

[0226] The message 815-b may include a timeline or calendar view to enable the user to adjust the time span and explore the body clock. For example, the message 815-b may include a toggle button to allow the user to select the duration of time to be averaged on the graphical representation 810-b. For example, the message may include a toggle button to select the number of months (e.g., March to June), the number of weekdays (e.g., only Saturday and Sunday, Monday to Sunday, etc.), the number of weeks (e.g., 2 weeks), or a combination thereof.

[0227] After selecting message 815-c, application page 802-c may be presented to user 102 via GUI 800. For example, message 815-d and message 815-e may include suggested times of day for the user to be active, suggested wake-up times for the user to wake up, suggested bedtimes for the user to go to sleep, suggested sleep durations, suggested times of day for the user to rest, or combinations thereof.

[0228] In some cases, message 815-e may indicate “Feeling sleepy? Your body is going through a low-energy slump in the afternoon. Don't worry if you're feeling lazy; the energy peak will come in an hour.”. In this case, message 815-e may also provide insights about recommended exercise times, focus times, etc. For example, application page 802-c may display message 815-e, which may indicate “The best time to exercise is 2:30 PM - 5:00 PM for maximum cardiovascular efficiency and muscle strength.”. In other examples, message 815-e may indicate “Take advantage of the early morning hours. Exercise in the late morning to start the day with a boost of energy.”. In some cases, message 815-3 may indicate “Take advantage of the afternoon to focus on the task at hand. Get work done in the afternoon to complete your tasks efficiently and effectively.”. Personalized insights may indicate aspects of the collected physiological data (e.g., contributing factors in the physiological data) used to determine the circadian rhythm chronotype. In some cases, message 815 may provide personalized insights about graphical representation 810.

[0229] Application page 802-c may display message 815-d indicating the user's optical sleep schedule. In some cases, message 815-d may include a recommended schedule for the user, including bedtime, wake-up time, exercise time, focus time, rest time, or a combination thereof. For example, message 815-d may indicate "6:00 AM – 6:30 AM: Blood pressure rises fastest, best time to get up. 6:00 AM – 12:00 AM: High alert, focus on deep or creative work. 1:30 PM – 2:00 PM: Afternoon slump. Period of low energy. Relax during this time.". Message 815-d may indicate a recommended bedtime, wake-up time, sleep midpoint, or a combination thereof. In this case, message 815 may recommend a bedtime and / or wake-up time based on the determined circadian rhythm time type.

[0230] In some cases, message 815 may indicate how to utilize information collected from the user's circadian rhythm profile in travel mode to help the user adjust their body clock to get rid of jet lag and return to the new time zone. For example, message 815 may indicate jet lag-related melatonin onset estimates, alertness timeline estimates, exercise timeline recommendations, more personalized bedtime recommendations, light therapy assistance, or a combination thereof.

[0231] For example, the determined circadian chronotype may be used to suggest a bedtime for the new time zone based on the user's determined circadian chronotype, the time of nighttime temperature minimum derived from historical data for the original time zone, the user's sleep history statistics, or a combination thereof. In this case, message 815 may provide suggestions to adjust / plan the user's sleep-wake schedule for the first few days in the new time zone.

[0232] In some cases, a user may record a symptom or moment via user input 820. For example, the system may receive user input (e.g., a tag) to record a symptom associated with a state of relaxation (e.g., a user experiencing a Moment). In some examples, the system may identify a recovery moment when the user is in a state of relaxation. In this case, the system may determine a circadian chronotype based on identifying the recovery moment. For example, the system may use the recovery time to determine a circadian chronotype.

[0233] In some embodiments, the system may be configured to receive user input 820 regarding the determined circadian chronotype in order to train a classifier (e.g., supervised learning of a machine learning classifier) ​​and improve the circadian chronotype determination technique. For example, a user device may display a determination of the circadian chronotype. Subsequently, the user may enter one or more user inputs, such as the onset of symptoms, confirmation of the determined circadian chronotype, etc. These user inputs 820 may then be input into the classifier to train the classifier. In other words, the user input 820 may be used to verify or confirm the determined circadian chronotype.

[0234] Fig. 9 A block diagram 900 of a device 905 supporting techniques for determining a circadian chronotype according to aspects of the present disclosure is shown. The device 905 may include an input module 910, an output module 915, and a wearable application 920. The device 905 may also include a processor. Each of these components may communicate with each other (e.g., via one or more buses).

[0235] The input module 910 may provide a means for receiving information (e.g., data packets, user data, control information, or any combination thereof) associated with various information channels (e.g., control channels, data channels, information channels associated with disease detection techniques). The information may be passed to other components of the device 905. The input module 910 may use a single antenna or a group of multiple antennas.

[0236] The output module 915 can provide a means for transmitting signals generated by other components of the device 905. For example, the output module 915 can transmit information related to various information channels (e.g., control channels, data channels, information channels related to disease detection technology), such as data packets, user data, control information, or any combination thereof. In some examples, the output module 915 can be co-located with the input module 910 in the transceiver module. The output module 915 can utilize a single antenna or a group of multiple antennas.

[0237] For example, the wearable application 920 may include a data acquisition component 925, a sleep component 930, a data classifier 935, a chronotype component 940, a user interface component 945, or any combination thereof. In some examples, the wearable application 920 or its various components may be configured to use or otherwise cooperate with the input module 910, the output module 915, or both to perform various operations (e.g., receive, monitor, transmit). For example, the wearable application 920 may receive information from the input module 910, send information to the output module 915, or be integrated with the input module 910, the output module 915, or both to receive information, send information, or perform various other operations described herein.

[0238] According to examples disclosed herein, the wearable application 920 may support determining a circadian chronotype on an application running on an operating system of a user device and associated with a wearable device. The data acquisition component 925 may be configured to or otherwise support a means for receiving a first set of physiological data from a wearable device, the first set of physiological data being measured by the wearable device from a user over a period of time, the first set of physiological data comprising at least nighttime temperature data, activity data, and sleep pattern data. The sleep component 930 may be configured to or otherwise support a means for receiving a second set of physiological data from a wearable device, the second set of physiological data being measured by the wearable device from a user in a previous sleep day, the second set of physiological data comprising at least sleep pattern data. The data classifier 935 may be configured to or otherwise support a means for classifying the first set of physiological data into a circadian chronotype using a machine learning model based at least in part on inputting the first set of physiological data into a machine learning model. The chronotype component 940 may be configured to or otherwise support a means for comparing the determined circadian chronotype and the received second set of physiological data by an application configured to process data received from a wearable device. The user interface component 945 may be configured as or otherwise support means for causing a graphical user interface of a user device to display a message related to the comparison, the determined circadian chronotype, the received second set of physiological data, or a combination thereof.

[0239] Fig.10A block diagram 1000 of a wearable application 1020 supporting techniques for determining circadian chronotypes according to aspects of the present disclosure is shown. The wearable application 1020 can be an example of aspects of the wearable application or the wearable application 920 described herein, or both. The wearable application 1020 or its various components can be examples of devices for performing various aspects of the techniques for determining circadian chronotypes described herein. For example, the wearable application 1020 can include a data acquisition component 1025, a sleep component 1030, a data classifier 1035, a chronotype component 1040, a user interface component 1045, or any combination thereof. Each of these components can communicate with each other directly or indirectly (e.g., via one or more buses).

[0240] Wearable application 1020 may support determining a circadian chronotype on an application running on an operating system of a user device and associated with a wearable device according to examples disclosed herein. Data acquisition component 1025 may be configured to or otherwise support means for receiving a first set of physiological data collected over a period of time and measured by the wearable device from a user, the first set of physiological data including at least nighttime temperature data, activity data, and sleep pattern data from the wearable device. Sleep component 1030 may be configured to or otherwise support means for receiving a second set of physiological data collected during a previous sleep day and measured by the wearable device from the user, the second set of physiological data including at least sleep pattern data from the wearable device. Data classifier 1035 may be configured to or otherwise support means for classifying the first set of physiological data into a circadian chronotype using a machine learning model based at least in part on inputting the first set of physiological data into a machine learning model. Chronotype component 1040 may be configured to or otherwise support means for comparing the determined circadian chronotype and the received second set of physiological data by an application configured to process data received from the wearable device. The user interface component 1045 can be configured as or otherwise support means for causing a graphical user interface of a user device to display a message related to the comparison, the determined circadian chronotype, the received second set of physiological data, or a combination thereof.

[0241] In some examples, user interface component 1045 can be configured to or otherwise support a means for causing a graphical user interface of a user device to display a graphical representation of an average of sleep pattern data in the first set of physiological data over a period of time.

[0242] In some examples, the average of the sleep pattern data includes an average wake-up time that the user wakes up, an average bedtime that the user falls asleep, an average sleep midpoint time, an average sleep duration, or a combination thereof.

[0243] In some examples, user interface component 1045 can be configured or otherwise support means for overlaying a graphical representation of an average of sleep pattern data in the first set of physiological data over a period of time relative to a representation of a twenty-four hour time span.

[0244] In some examples, user interface component 1045 can be configured as or otherwise support means for causing a graphical user interface of a user device to display a segment of a representation of a twenty-four hour time span that includes an average of sleep pattern data in the first set of physiological data over the time period.

[0245] In some examples, the segment represents an average of sleep pattern data in the first set of physiological data over the time period as a shaped portion having a first side, a second side, and a midpoint, wherein the first side indicates an average time for the user to fall asleep, the second side indicates an average time for the user to wake up, and the midpoint is between the first side and the second side and indicates an average time for the user's midpoint of sleep.

[0246] In some examples, data acquisition component 1025 can be configured as or otherwise support a device for identifying a night time associated with a night temperature minimum based at least in part on receiving a first set of physiological data, wherein classifying the first set of physiological data as a circadian rhythm time type is based at least in part on identifying the night time associated with the night temperature minimum.

[0247] In some examples, the data classifier 1035 can be configured as or otherwise support a device for processing the sleep pattern data in the first set of physiological data by the application to extract at least the standard deviation of the midpoint of sleep, the median wake-up time when the user wakes up, the median bedtime when the user falls asleep, or a combination thereof. In some examples, the data classifier 1035 can be configured as or otherwise support a device for processing the activity data in the first set of physiological data by the application to extract at least the average metabolic equivalent of task (MET) value, the user activity time, or both. In some examples, the data classifier 1035 can be configured as or otherwise support a device for processing the night temperature data by the application to extract at least the average skin temperature, the average skin temperature of multiple highest temperature values ​​for a continuous twenty-four hour time span, the average skin temperature of multiple lowest temperature values ​​for a continuous twenty-four hour time span, or a combination thereof. In some examples, the first set of physiological data is classified as a circadian rhythm time type based at least in part on the processing of the sleep pattern data, the activity data, and the night temperature data by the application.

[0248] In some examples, chronotype component 1040 can be configured to or otherwise support means for determining a misalignment between the received second set of physiological data and the determined circadian chronotype based at least in part on comparing the determined circadian chronotype and the received second set of physiological data.

[0249] In some examples, the message includes a recommended time of day for the user to be active, a recommended wake-up time for the user to wake up, a recommended bedtime for the user to fall asleep, a recommended sleep duration, a recommended time of day for the user to rest, a recommended time of day for the user to focus, a sleep alignment message, a sleep misalignment message, or a combination thereof.

[0250] In some examples, the nighttime temperature data includes continuous nighttime temperature data.

[0251] In some examples, the wearable device includes a wearable ring device.

[0252] In some examples, the wearable device collects a first set of physiological data and a second set of physiological data from the user based on arterial blood flow, capillary blood flow, arteriolar blood flow, or a combination thereof.

[0253] Fig.11 A diagram of a system 1100 is shown that includes a device 1105 that supports techniques for determining circadian chronotype according to aspects of the present disclosure. The device 1105 can be an example of the device 905 described herein or include a component of the device 905 described herein. The device 1105 can include an example of a user device 106, as described previously herein. The device 1105 can include components for two-way communication, including components for sending and receiving communications with the wearable device 104 and the server 110, such as a wearable application 1120, a communication module 1110, an antenna 1115, a user interface component 1125, a database (application data) 1130, a memory 1135, and a processor 1140. These components can be in electronic communication or otherwise coupled (e.g., operationally, communicatively, functionally, electronically, electrically) via one or more buses (e.g., bus 1145).

[0254] The communication module 1110 can manage the input and output signals of the device 1105 through the antenna 1115. The communication module 1110 can include Figure 2 In this regard, the communication module 1110 can manage the communication with the ring 104 and the server 110, such as Figure 2 As shown. The communication module 1110 can also manage peripheral devices that are not integrated into the device 1105. In some cases, the communication module 1110 can represent a physical connection or port to an external peripheral device. In some cases, the communication module 1110 can utilize an operating system, such as

[0255] Or other known operating systems. In other cases, the communication module 1110 may represent or interact with a wearable device (e.g., finger ring 104), a modem, a keyboard, a mouse, a touch screen, or a similar device. In some cases, the communication module 1110 may be implemented as part of the processor 1140. In some examples, a user may interact with the device 1105 through the communication module 1110, the user interface component 1125, or through a hardware component controlled by the communication module 1110.

[0256] In some cases, the device 1105 may include a single antenna 1115. However, in some other cases, the device 1105 may have more than one antenna 1115, which may be capable of simultaneously sending or receiving multiple wireless transmissions. The communication module 1110 may communicate bidirectionally via one or more antennas 1115, wired or wireless links as described herein. For example, the communication module 1110 may represent a wireless transceiver and may communicate bidirectionally with another wireless transceiver. The communication module 1110 may also include a modem for modulating data packets, providing the modulated data packets to one or more antennas 1115 for transmission, and demodulating data packets received from one or more antennas 1115.

[0257] The user interface component 1125 can manage the storage and processing of data in the database 1130. In some cases, a user can interact with the user interface component 1125. In other cases, the user interface component 1125 can run automatically without user interaction. The database 1130 can be an example of a single database, a distributed database, multiple distributed databases, a data store, a data lake, or an emergency backup database.

[0258] Memory 1135 may include RAM and ROM. Memory 1135 may store computer-readable, computer-executable software, including instructions that, when executed, cause processor 1140 to perform various functions described herein. In some cases, memory 1135 may contain, among other things, BIOS, which may control basic hardware or software operations, such as interaction with peripheral components or devices.

[0259] Processor 1140 may include an intelligent hardware device (e.g., a general purpose processor, a DSP, a CPU, a microcontroller, an ASIC, an FPGA, a programmable logic device, a discrete gate or transistor logic component, a discrete hardware component, or any combination thereof). In some cases, processor 1140 may be configured to operate a memory array using a memory controller. In other cases, the memory controller may be integrated into processor 1140. Processor 1140 may be configured to execute computer-readable instructions stored in memory 1135 to perform various functions (e.g., functions or tasks of methods and systems supporting sleep staging algorithms).

[0260] The wearable application 1120 may support determining a circadian chronotype on an application running on an operating system of a user device and associated with a wearable device according to examples disclosed herein. For example, the wearable application 1120 may be configured to or otherwise support a means for receiving a first set of physiological data collected over a period of time and measured by the wearable device from a user, the first set of physiological data including at least nighttime temperature data, activity data, and sleep pattern data from the wearable device. The wearable application 1120 may be configured to or otherwise support a means for receiving a second set of physiological data collected during a previous sleep day and measured by the wearable device from the user, the second set of physiological data including at least sleep pattern data from the wearable device. The wearable application 1120 may be configured to or otherwise support a means for classifying the first set of physiological data into a circadian chronotype using a machine learning model based at least in part on inputting the first set of physiological data into a machine learning model. The wearable application 1120 may be configured to or otherwise support a means for comparing the determined circadian chronotype and the received second set of physiological data by an application configured to process data received from the wearable device. The wearable application 1120 may be configured as or otherwise support means for causing a graphical user interface of the user device to display a message related to the comparison, the determined circadian chronotype, the received second set of physiological data, or a combination thereof.

[0261] By including or configuring the wearable application 1120 according to the examples described herein, the device 1105 can support techniques for improving communication reliability, reducing latency, improving user experience associated with reduced processing, reducing power consumption, more efficiently utilizing communication resources, improving coordination between devices, extending battery life, improving utilization of processing power, and the like.

[0262] The wearable application 1120 may include an application (e.g., an “app”), a program, software, or other component configured to facilitate communication with the ring 104, the server 110, other user devices 106, etc. For example, the wearable application 1120 may include an application executable on the user device 106 that is configured to receive data (e.g., physiological data) from the ring 104, perform processing operations on the received data, send and receive data with the server 110, and cause the data to be presented to the user 102.

[0263] Fig.12 1 shows a flow chart illustrating a method 1200 that supports techniques for determining a circadian rhythm chronotype according to aspects of the present disclosure. The operations of the method 1200 may be implemented by a user device or components thereof as described herein. For example, the operations of the method 1200 may be implemented by reference to Figures 1 to 11 In some examples, the user device may execute a set of instructions to control the functional elements of the user device to perform the functions. In addition, or as an alternative, the user device may use dedicated hardware to perform aspects of the functions.

[0264] At 1205, the method may include receiving from the wearable device a first set of physiological data collected over a period of time and measured by the wearable device from the user, the first set of physiological data including at least nighttime temperature data, activity data, and sleep pattern data. The operations of block 1205 may be performed according to examples disclosed herein. In some examples, aspects of the operations of block 1205 may be described by reference to Fig.10 The data acquisition component 1025 executes.

[0265] At 1210, the method may include receiving from the wearable device a second set of physiological data collected during a previous sleep day and measured by the wearable device from the user, the second set of physiological data including at least sleep pattern data. The operations of block 1210 may be performed according to examples disclosed herein. In some examples, aspects of the operations of block 1210 may be described with reference to Fig.10 The sleep component 1030 executes.

[0266] At 1215, the method may include using the machine learning model to classify the first set of physiological data into a circadian rhythm chronotype based at least in part on inputting the first set of physiological data into the machine learning model. The operations of block 1215 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1215 may be described by reference to Fig.10 The data classifier 1035 executes.

[0267] At 1220, the method may include comparing, by an application configured to process data received from the wearable device, the determined circadian rhythm chronotype and the received second set of physiological data. The operations of block 1220 may be performed according to examples disclosed herein. In some examples, aspects of the operations of block 1220 may be described by reference to Fig.10 The time type component 1040 executes.

[0268] At 1225, the method may include causing a graphical user interface of the user device to display a message associated with the comparison, the determined circadian chronotype, the received second set of physiological data, or a combination thereof. The operations of block 1225 may be performed according to examples disclosed herein. In some examples, aspects of the operations of block 1225 may be performed by user interface component 1045, as described in reference to Fig.10 described.

[0269] Fig.13 1 shows a flow chart illustrating a method 1300 of supporting techniques for determining circadian rhythm chronotype according to aspects of the present disclosure. The operations of method 1300 may be implemented by a user device or components thereof as described herein. For example, the operations of method 1300 may be implemented by reference to Figures 1 to 11 The user device described herein performs. In some examples, the user device may execute a set of instructions to control functional elements of the user device to perform the described functions. Additionally or alternatively, the user device may use dedicated hardware to perform aspects of the described functions.

[0270] At 1305, the method may include receiving from the wearable device a first set of physiological data collected over a period of time and measured by the wearable device from the user, the first set of physiological data including at least nighttime temperature data, activity data, and sleep pattern data. The operations of block 1305 may be performed according to examples disclosed herein. In some examples, aspects of the operations of block 1305 may be performed by data acquisition component 1025, as described in reference to Fig.10 described.

[0271] At 1310, the method may include receiving from the wearable device a second set of physiological data collected during a previous sleep day and measured by the wearable device from the user, the second set of physiological data including at least sleep pattern data. The operations of block 1310 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1310 may be performed by the sleep component 1030, as described in reference to Fig.10 described.

[0272] At 1315, the method may include using the machine learning model to classify the first set of physiological data into a circadian rhythm chronotype based at least in part on inputting the first set of physiological data into the machine learning model. The operations of block 1315 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1315 may be performed by data classifier 1035, as described in reference to Fig.10 described.

[0273] At 1320, the method may include comparing, by an application configured to process data received from the wearable device, the determined circadian rhythm chronotype and the received second set of physiological data. The operations of block 1320 may be performed according to examples disclosed herein. In some examples, aspects of the operations of block 1320 may be described by reference to Fig.10 The described time type component 1040 executes.

[0274] At 1325, the method may include determining a misalignment between the received second set of physiological data and the determined circadian chronotype based at least in part on comparing the determined circadian chronotype and the received second set of physiological data. The operations of block 1325 may be performed according to examples disclosed herein. In some examples, aspects of the operations of block 1325 may be described by reference to Fig.10 The described time type component 1040 executes.

[0275] At 1330, the method may include causing a graphical user interface of the user device to display a message associated with the comparison, the determined circadian chronotype, the received second set of physiological data, or a combination thereof. The operations of block 1330 may be performed according to examples disclosed herein. In some examples, aspects of the operations of 1330 may be performed by user interface component 1045, such as reference Fig.10 described.

[0276] It should be noted that the above methods describe possible implementations, and that operations and steps may be rearranged or otherwise modified, and other implementations may be implemented. Furthermore, aspects from two or more methods may be combined.

[0277] A method for determining a circadian chronotype on an application running on an operating system of a user device and associated with a wearable device is described. The method may include: receiving from the wearable device a first set of physiological data collected over a period of time and measured by the wearable device from a user, the first set of physiological data including at least nighttime temperature data, activity data, and sleep pattern data; receiving from the wearable device a second set of physiological data collected on a previous sleep day and measured by the wearable device from the user, the second set of physiological data including at least sleep pattern data; using a machine learning model to classify the first set of physiological data as a circadian chronotype based at least in part on inputting the first set of physiological data into the machine learning model; comparing the determined circadian chronotype and the received second set of physiological data by an application configured to process data received from the wearable device; and causing a graphical user interface of the user device to display a message related to the comparison, the determined circadian chronotype, the received second set of physiological data, or a combination thereof.

[0278] An apparatus for determining a circadian chronotype on an application running on an operating system of a user device and associated with a wearable device is described. The apparatus may include a processor, a memory coupled to the processor, and instructions stored in the memory. The instructions are executable by the processor to cause the apparatus to receive a first set of physiological data collected over a period of time and measured by the wearable device from a user, the first set of physiological data including at least nighttime temperature data, activity data, and sleep pattern data; receive a second set of physiological data collected on a previous sleep day and measured by the wearable device from the user, the second set of physiological data including at least sleep pattern data; classify the first set of physiological data into a circadian chronotype using a machine learning model based at least in part on inputting the first set of physiological data into a machine learning model; compare the determined circadian chronotype and the received second set of physiological data by an application configured to process data received from the wearable device; and cause a graphical user interface of the user device to display a message associated with the comparison, the determined circadian chronotype, the received second set of physiological data, or a combination thereof.

[0279] Another apparatus for determining a circadian chronotype on an application running on an operating system of a user device and associated with a wearable device is described. The apparatus may include: a module for receiving from the wearable device a first set of physiological data collected over a period of time and measured by the wearable device from a user, the first set of physiological data including at least nighttime temperature data, activity data, and sleep pattern data; a module for receiving from the wearable device a second set of physiological data collected on a previous sleep day and measured by the wearable device from the user, the second set of physiological data including at least sleep pattern data; a module for using a machine learning model to classify the first set of physiological data as a circadian chronotype based at least in part on inputting the first set of physiological data into the machine learning model; a module for comparing, by an application configured to process data received from the wearable device, the determined circadian chronotype and the received second set of physiological data; and a module for causing a graphical user interface of the user device to display a message associated with the comparison, the determined circadian chronotype, the received second set of physiological data, or a combination thereof.

[0280] A non-transitory computer-readable medium is described that stores code for determining a circadian chronotype on an application running on an operating system of a user device and associated with a wearable device. The code may include instructions executable by a processor to receive from the wearable device a first set of physiological data collected over a period of time and measured by the wearable device from a user, the first set of physiological data including at least nighttime temperature data, activity data, and sleep pattern data; receive from the wearable device a second set of physiological data collected on a previous sleep day and measured by the wearable device from the user, the second set of physiological data including at least sleep pattern data; classify the first set of physiological data as a circadian chronotype using a machine learning model based at least in part on inputting the first set of physiological data into a machine learning model; compare the determined circadian chronotype and the received second set of physiological data, via an application configured to process data received from the wearable device, and cause a graphical user interface of the user device to display a message related to the comparison, the determined circadian chronotype, the received second set of physiological data, or a combination thereof.

[0281] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, modules, or instructions for causing a graphical user interface of a user device to display a graphical representation of an average of sleep pattern data in the first set of physiological data over a period of time.

[0282] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the average of the sleep pattern data includes an average wake-up time when the user wakes up, an average bedtime when the user falls asleep, an average sleep midpoint time, an average sleep duration, or a combination thereof.

[0283] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, modules, or instructions for overlaying a graphical representation of an average of sleep pattern data in the first set of physiological data over a period of time relative to a representation of a twenty-four hour time span.

[0284] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, modules, or instructions for causing a graphical user interface of a user device to display a segment representing a twenty-four hour time span, the segment including an average of sleep pattern data in the first set of physiological data over the time period.

[0285] In some examples of the methods, devices, and non-transitory computer-readable media described herein, the segment represents an average of sleep pattern data in the first set of physiological data over the time period as a shaped portion having a first side, a second side, and a midpoint, the first side indicating an average time for a user to fall asleep, the second side indicating an average time for a user to wake up, and the midpoint may be located between the first side and the second side and indicating an average time for a user's midpoint sleep.

[0286] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, modules, or instructions for identifying nighttime times associated with nighttime temperature minimums based at least in part on receiving a first set of physiological data, wherein classifying the first set of physiological data into a circadian chronotype may be based at least in part on identifying nighttime times associated with nighttime temperature minimums.

[0287] Some examples of the methods, devices, and non-transitory computer-readable media described herein may also include operations, features, modules, or instructions for: processing sleep pattern data in the first set of physiological data by an application to extract at least a standard deviation of a midpoint of sleep, a median wake-up time when a user wakes up, a median bedtime when a user falls asleep, or a combination thereof; processing activity data of the first set of physiological data by an application to extract at least an average metabolic equivalent of a task (MET) value, a time when a user may be active, or both; processing nighttime temperature data by an application to extract at least an average skin temperature, an average skin temperature of multiple highest temperature values ​​over a continuous twenty-four hour time span, an average skin temperature of multiple lowest temperature values ​​over a continuous twenty-four hour time span, or a combination thereof, wherein classifying the first set of physiological data into a circadian rhythm time type may be based at least in part on processing the sleep pattern data, the activity data, and the nighttime temperature data by the application.

[0288] Some examples of the methods, apparatus, and non-transitory computer-readable media described herein may also include operations, features, modules, or instructions for determining a misalignment between the received second set of physiological data and the determined circadian chronotype based at least in part on comparing the determined circadian chronotype and the received second set of physiological data.

[0289] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the message includes a suggested time of day at which the user can be active, a suggested wake-up time at which the user can wake up, a suggested bedtime at which the user can fall asleep, a suggested sleep duration, a suggested time of day at which the user can rest, a suggested time of day at which the user can concentrate, a sleep alignment message, a sleep misalignment message, or a combination thereof.

[0290] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the nighttime temperature data includes continuous nighttime temperature data.

[0291] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the wearable device includes a wearable finger-ring device.

[0292] In some examples of the methods, apparatus, and non-transitory computer-readable media described herein, the wearable device collects a first set of physiological data and a second set of physiological data from the user based on arterial blood flow, capillary blood flow, arteriolar blood flow, or a combination thereof.

[0293] The description set forth herein in conjunction with the accompanying drawings describes example configurations and does not represent all examples that may be implemented or within the scope of the claims. The term "exemplary" as used herein means "used as an example, instance, or illustration," rather than "preferred" or "superior to other examples." The specific implementation includes specific details for the purpose of providing an understanding of the described techniques. However, these techniques may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form to avoid obscuring the concepts of the described examples.

[0294] In the drawings, similar components or features may have the same reference label. In addition, various components of the same type may be distinguished by following the reference label marked by a dash and a second label that distinguishes the similar components. If only the first reference label is used in the specification, the description applies to any of the similar components having the same first reference label, without regard to the second reference label.

[0295] Any of a variety of different technologies and means may be used to represent the information and signals described herein. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced in the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or optical particles, or any combination thereof.

[0296] The various illustrative blocks and modules described in conjunction with the present disclosure herein may be implemented or executed with a general purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in an alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).

[0297] The functions described herein can be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions can be stored on a computer-readable medium or transmitted by a computer-readable medium as one or more instructions or codes. Other examples and implementations are within the scope of the present disclosure and the appended claims. For example, due to the nature of software, the above functions can be implemented using software executed by a processor, hardware, firmware, hard wiring, or any combination of these. The features that implement the functions can also be physically located at various locations, including being distributed so that the various parts of the functions are implemented at different physical locations. In addition, as used herein, included in the claims, such as in a list of items (e.g., a list of items starting with phrases such as "at least one of" or "one or more of"), the "or" used indicates an inclusive list, so that, for example, a list of at least one of A, B, or C means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Moreover, as used herein, the phrase "based on" should not be interpreted as referring to a set of closed conditions. For example, without departing from the scope of the present disclosure, the exemplary steps described as "based on condition A" may be based on both condition A and condition B. In other words, as used herein, the phrase "based on" should be interpreted in the same manner as the phrase "based at least in part on."

[0298] Computer-readable medium includes both non-transitory computer storage medium and communication medium, and these communication mediums include any medium that promotes computer program to be transferred from one place to another place.Non-transitory storage medium can be any available medium that can be accessed by general or special-purpose computer.As an example and not limitation, non-transitory computer-readable medium can include RAM, ROM, electrically erasable programmable ROM (EEPROM), compact disk (CD) ROM or other optical disk storage, disk storage or other magnetic storage device, or can be used for carrying or storing instruction or data structure form of desired program code device and can be accessed by general or special-purpose computer or general or special-purpose processor any other non-transitory medium.In addition, any connection is appropriately referred to as computer-readable medium.For example, if using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wireless technology such as infrared, radio and microwave to transmit software from website, server or other remote source, then coaxial cable, fiber optic cable, twisted pair, DSL or wireless technology such as infrared, radio and microwave are included in the definition of medium. Disk and disc, as used herein, includes CD, laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of the above are also included within the scope of computer-readable media.

[0299] The description herein is provided to enable those skilled in the art to make or use the present disclosure. Various modifications to the present disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the scope of the present disclosure. Therefore, the present disclosure is not limited to the examples and designs described herein, but is intended to conform to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining a circadian rhythm chronotype on an application running on an operating system of a user device and associated with a wearable device, comprising: receiving, from the wearable device, a first set of physiological data collected over a period of time and measured by the wearable device from a user, the first set of physiological data comprising at least nighttime temperature data, activity data, and sleep pattern data; receiving from the wearable device a second set of physiological data collected during a previous sleep day and measured by the wearable device from the user, the second set of physiological data comprising at least sleep pattern data; classifying the first set of physiological data into the circadian chronotype using the machine learning model based at least in part on inputting the first set of physiological data into the machine learning model; comparing, by the application configured to process data received from the wearable device, the determined circadian chronotype with the received second set of physiological data; as well as Causing a graphical user interface of the user device to display a message associated with the comparison, the determined circadian chronotype, the received second set of physiological data, or a combination thereof.

2. The method according to claim 1, further comprising: The graphical user interface of the user device is caused to display a graphical representation of an average of sleep pattern data in the first set of physiological data over the period of time.

3. The method of claim 2, wherein the average of the sleep pattern data comprises an average wake-up time when the user wakes up, an average bedtime when the user falls asleep, an average sleep midpoint time, an average sleep duration, or a combination thereof.

4. The method according to claim 2, further comprising: The graphical representation of the average of the sleep pattern data in the first set of physiological data over the period of time is superimposed against a representation of a twenty-four hour time span.

5. The method according to claim 4, further comprising: The graphical user interface of the user device is caused to display a segment of the representation of the twenty-four hour time span, the segment including the average of the sleep pattern data in the first set of physiological data over the period of time.

6. A method according to claim 5, wherein the segment represents the average of the sleep pattern data in the first set of physiological data over the period of time as a shaped portion having a first side, a second side and a midpoint, the first side indicating the average time when the user falls asleep, the second side indicating the average time when the user wakes up, and the midpoint is located between the first side and the second side and indicates the average time of the user's sleep midpoint.

7. The method according to claim 1, further comprising: Identifying a night time associated with a night time temperature minimum based at least in part on receiving the first set of physiological data, wherein classifying the first set of physiological data as the circadian rhythm chronotype is based at least in part on identifying the night time associated with the night time temperature minimum.

8. The method according to claim 1, further comprising: processing, by the application, the sleep pattern data in the first set of physiological data to extract at least a standard deviation of a sleep midpoint, a median wake time at which the user wakes up, a median bedtime at which the user falls asleep, or a combination thereof; processing, by the application, the activity data of the first set of physiological data to extract at least a mean metabolic equivalent of a task (MET) value, a time the user was active, or both; as well as processing the nighttime temperature data by the application to extract at least an average skin temperature, an average skin temperature of a plurality of highest temperature values ​​over a continuous twenty-four hour time span, an average skin temperature of a plurality of lowest temperature values ​​over a continuous twenty-four hour time span, or a combination thereof, Wherein classifying the first set of physiological data into the circadian chronotype is based at least in part on processing the sleep pattern data, the activity data, and the nighttime temperature data by the application.

9. The method according to claim 1, further comprising: A misalignment between the received second set of physiological data and the determined circadian chronotype is determined based at least in part on comparing the determined circadian chronotype and the received second set of physiological data.

10. The method of claim 1, wherein the message comprises a recommended time of day for the user to be active, a recommended wake-up time for the user to wake up, a recommended bedtime for the user to fall asleep, a recommended sleep duration, a recommended time of day for the user to rest, a recommended time of day for the user to concentrate, a sleep alignment message, a sleep misalignment message, or a combination thereof.

11. The method of claim 1, wherein the nighttime temperature data comprises continuous nighttime temperature data.

12. The method of claim 1, wherein the wearable device comprises a wearable finger ring device.

13. The method of claim 1, wherein the wearable device collects the first set of physiological data and the second set of physiological data from the user based on arterial blood flow, capillary blood flow, arteriolar blood flow, or a combination thereof.

14. An apparatus for determining a circadian rhythm chronotype on an application running on an operating system of a user device and associated with a wearable device, comprising: processor; a memory coupled to the processor; as well as instructions stored in the memory and executable by the processor to cause the apparatus to: receiving, from the wearable device, a first set of physiological data collected over a period of time and measured by the wearable device from a user, the first set of physiological data comprising at least nighttime temperature data, activity data, and sleep pattern data; receiving from the wearable device a second set of physiological data collected during a previous sleep day and measured from the user by the wearable device, the second set of physiological data comprising at least sleep pattern data; classifying the first set of physiological data into the circadian chronotype using the machine learning model based at least in part on inputting the first set of physiological data into the machine learning model; comparing, by the application configured to process data received from the wearable device, the determined circadian chronotype with the received second set of physiological data; as well as A graphical user interface of the user device is caused to display a message associated with the comparison, the determined circadian chronotype, the received second set of physiological data, or a combination thereof.

15. The apparatus of claim 14, wherein the instructions are further executable by the processor to cause the apparatus to: The graphical user interface of the user device is caused to display a graphical representation of an average of sleep pattern data in the first set of physiological data over the period of time. 16 . The apparatus of claim 15 , wherein the average of the sleep pattern data comprises an average wake-up time when a user wakes up, an average bedtime when a user falls asleep, an average sleep midpoint time, an average sleep duration, or a combination thereof.

17. The apparatus of claim 15, wherein the instructions are further executable by the processor to cause the apparatus to: The graphical representation of the average of the sleep pattern data in the first set of physiological data over the period of time is superimposed against a representation of a twenty-four hour time span.

18. A non-transitory computer readable medium storing code for determining a circadian chronotype on an application running on an operating system of a user device and associated with a wearable device, the code comprising instructions executable by a processor to: receiving, from the wearable device, a first set of physiological data collected over a period of time and measured by the wearable device from a user, the first set of physiological data comprising at least nighttime temperature data, activity data, and sleep pattern data; receiving from the wearable device a second set of physiological data collected during a previous sleep day and measured by the wearable device from the user, the second set of physiological data comprising at least sleep pattern data; classifying the first set of physiological data into the circadian chronotype using the machine learning model based at least in part on inputting the first set of physiological data into the machine learning model; comparing, by the application configured to process data received from the wearable device, the determined circadian chronotype with the received second set of physiological data; as well as A graphical user interface of the user device is caused to display a message associated with the comparison, the determined circadian chronotype, the received second set of physiological data, or a combination thereof.

19. The non-transitory computer readable medium of claim 18, wherein the instructions are further executable by the processor to: The graphical user interface of the user device is caused to display a graphical representation of an average of sleep pattern data in the first set of physiological data over the period of time.

20. The non-transitory computer-readable medium of claim 19, wherein the average of the sleep pattern data comprises an average wake-up time that a user wakes up, an average bedtime that a user falls asleep, an average sleep midpoint time, an average sleep duration, or a combination thereof.

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