Biometric measurement application for measuring circadian rhythm

Abstract

A computing device includes one or more memories to store one or more instructions and one or more processors. The one or more processors execute the one or more instructions to obtain, via one or more optical sensors, heart rate information associated with a user associated with the computing device during a first predetermined duration of time, obtain filtered heart rate information by selecting heart rate measurements from the heart rate information which were taken at times at which, prior to a corresponding heart rate measurement, motion information associated with the user satisfies a threshold movement level for greater than a threshold duration of time, and determine, based on the filtered heart rate information, a heart rate circadian rhythm associated with the user for a second predetermined duration of time which is less than the first predetermined duration of time.

Description

BIOMETRIC MEASUREMENT APPLICATION FOR MEASURING CIRCADIAN RHYTHMFIELD

[0001] The disclosure relates generally to wearable computing devices. More particularly, the disclosure relates to wearable computing devices which are used to obtain and store biometric information of a user including circadian rhythm information.BACKGROUND

[0002] There are various existing techniques for assessing circadian rhythm.Conceptually, samples of a physiological variable are obtained as a time series (e.g., samples of heart rate, body temperature, cortisol level, kidney blood flow, etc.). These values can be plotted on a 24 hour clock, with each new day’s values "wrapping" around on itself. The circadian rhythm may be modeled as a sinusoidal wave. According to some methods, data is fit with a cosine curve based on cosinor analysis. Cosinor analysis can provide a parametric representation based on an acrophase (time of peak activity), amplitude (peak-to-nadir difference), and mesor (mean) of the fitted curve.

[0003] Another method for detecting the circadian rhythm is to track the levels of melatonin in the blood stream, as melatonin is a key hormone in signaling the body to fall asleep. For example, a clinical test using salivary samples taken at thirty minute or one-hour intervals can be used to determine a person’s circadian rhythm. However, this is quite a specialized and expensive test and not readily available to users on a continual basis.SUMMARY

[0004] Aspects and advantages of embodiments of the disclosure will be set forth in part in the following description, or can be learned from the description, or can be learned through practice of the example embodiments.

[0005] In an example embodiment, a computing device (e.g., a mobile phone, a smartphone, a biometric computing device, a wearable computing device including a smartwatch or tracker, a server computing device, etc.) is provided. The computing device includes one or more memories configured to store one or more instructions; and one or more processors configured to execute the one or more instructions stored in the one or more memories to: obtain, via one or more optical sensors, heart rate information associated with a user associated with the computing device during a first predetermined duration of time, obtain filtered heart rate information by selecting heart rate measurements from the heart rateinformation which were taken at times at which, prior to a corresponding heart rate measurement, motion information associated with the user satisfies a threshold movement level for greater than a threshold duration of time, and determine, based on the filtered heart rate information, a heart rate circadian rhythm associated with the user for a second predetermined duration of time which is less than the first predetermined duration of time.

[0006] In some implementations, the one or more optical sensors include one or more photoplethysmography (PPG) sensors, and the one or more PPG sensors and the one or more ECG sensors are disposed on the computing device.

[0007] In some implementations, the one or more processors are further configured to: determine motion coefficient values associated with the user during the first predetermined duration of time, and obtain the filtered heart rate information by selecting the heart rate measurements from the heart rate information: which were taken at times at which, prior to the corresponding heart rate measurement, the motion information associated with the user satisfies the threshold movement level for greater than the threshold duration of time, and which were taken at times at which, prior to the corresponding heart rate measurement, an average motion coefficient value associated with the user satisfies a threshold motion coefficient value, the average motion coefficient value being determined based on motion coefficient values determined during the threshold duration of time.

[0008] In some implementations, the one or more processors are configured to determine, based on the filtered heart rate information, the heart rate circadian rhythm associated with the user for the second predetermined duration of time by: parameterizing the heart rate circadian rhythm according to a Fourier series decomposition with two modes.

[0009] In some implementations, the one or more processors are configured to determine, based on the filtered heart rate information, the heart rate circadian rhythm associated with the user for the second predetermined duration of time by: parameterizing the heart rate circadian rhythm according to a cosine curve with twenty-four hour components and twelve hour components.

[0010] In some implementations, the threshold movement level corresponds to a predetermined number of steps taken by the user during the threshold duration of time.

[0011] In some implementations, the one or more processors are configured to determine, based on the filtered heart rate information, the heart rate circadian rhythm associated with the user for the second predetermined duration of time by: determining, based on the filtered heart rate information, an average heart rate for each hour during the first predetermined duration of time; and determining, based on the average heart rate for eachhour during the first predetermined duration of time, a median heart rate value for each hour in a twenty -four day.

[0012] In some implementations, determining, based on the filtered heart rate information, the average heart rate for each hour during the first predetermined duration of time comprises removing outlier heart rate values from the filtered heart rate information.

[0013] In some implementations, the one or more processors are configured to determine, via one or more motion sensors, the motion information while the heart rate information associated w ith the user is obtained during the first predetermined duration of time.

[0014] In some implementations, the one or more processors are configured to: control an operation of the computing device based on the heart rate circadian rhythm.

[0015] In some implementations, the operation of the computing device includes activating an output device to notify the user to take one or more actions based on the heart rate circadian rhythm.

[0016] In some implementations, the operation of the computing device includes changing a state of the computing device or of an external computing device, based on the heart rate circadian rhythm.

[0017] In some implementations, the first predetermined duration of time is at least one week and no more than four weeks, and the second predetermined duration of time is twenty- four hours.

[0018] In some implementations, the threshold duration of time is at least one minute and no more than ten minutes.

[0019] In some implementations, the computing device is a wearable computing device.

[0020] In some implementations, the one or more processors are configured to: receive, via one or more light sensors, light data associated with the computing device, and to control an operation of the computing device based on the heart rate circadian rhythm and the light data, wherein the operation of the computing device includes at least one of activating an output device to notify the user to take one or more actions based on the heart rate circadian rhythm and the light data, changing a state of the computing device based on the heart rate circadian rhythm and the light data, or changing a state of an external computing device, based on the heart rate circadian rhythm and the light data.

[0021] In an example embodiment, a computer-implemented method is provided. The computer-implemented method includes obtaining, by a computing device and via one or more optical sensors, heart rate information associated with a user associated with thecomputing device during a first predetermined duration of time; obtaining, by the computing device, filtered heart rate information by selecting heart rate measurements from the heart rate information which were taken at times at which, prior to a corresponding heart rate measurement, motion information associated with the user satisfies a threshold movement level for greater than a threshold duration of time; and determining, by the computing device and based on the filtered heart rate information, a heart rate circadian rhythm associated with the user for a second predetermined duration of time which is less than the first predetermined duration of time.

[0022] The computer-implemented method may include further operations to execute other aspects and operations of the computing device as described herein.

[0023] In an example embodiment, a non-transitory computer-readable medium which stores instructions that are executable by one or more processors of a computing device is provided. The non-transitory computer-readable medium stores instructions which are executable by one or more processors of the computing device. The instructions include: instructions to cause the one or more processors to perform operations including obtaining, via one or more optical sensors, heart rate information associated with a user associated with the computing device during a first predetermined duration of time, obtaining filtered heart rate information by selecting heart rate measurements from the heart rate information which were taken at times at which, prior to a corresponding heart rate measurement, motion information associated with the user satisfies a threshold movement level for greater than a threshold duration of time, and determining, based on the filtered heart rate information, a heart rate circadian rhythm associated with the user for a second predetermined duration of time which is less than the first predetermined duration of time.

[0024] The non-transitory computer-readable medium may store additional instructions to execute other aspects and operations of the computing device and computer-implemented method as described herein.

[0025] In an example embodiment, a computing device (e.g., a mobile phone, a smartphone, a biometric computing device, a wearable computing device including a smartwatch or tracker, a server computing device, etc.) is provided. The computing device includes one or more memories configured to store one or more instructions; and one or more processors configured to execute the one or more instructions stored in the one or more memories to: iteratively determine, over a first predetermined duration of time and via one or more motion sensors, whether motion information associated with a user associated with the computing device satisfies a threshold movement level for greater than a threshold durationof time, each time the motion information associated with the user is determined to satisfy the threshold movement level for greater than the threshold duration of time, obtain, via one or more heart rate sensors, heart rate information associated with the user, and determine, based on the heart rate information obtained during the first predetermined duration of time, a heart rate circadian rhythm associated with the user for a second predetermined duration of time, the second predetermined duration of time being less than the first predetermined duration of time.

[0026] In some implementations, the one or more processors are further configured to: iteratively determine motion coefficient values associated with the user over the first predetermined duration of time, and each time the motion information associated with the user is determined to satisfy the threshold movement level for greater than the threshold duration of time and an average motion coefficient value associated with the user was less than a threshold motion coefficient value, obtain, via the one or more heart rate sensors, the heart rate information associated with the user, the average motion coefficient value being determined based on motion coefficient values determined during the threshold duration of time.

[0027] In some implementations, the one or more processors are configured to: control an operation of the computing device based on the heart rate circadian rhythm, wherein the operation of the computing device includes at least one of activating an output device to notify the user to take one or more actions based on the heart rate circadian rhythm, changing a state of the computing device based on the heart rate circadian rhythm, or changing a state of an external computing device, based on the heart rate circadian rhythm.

[0028] In some implementations, the first predetermined duration of time is at least one week and no more than four weeks, the second predetermined duration of time is twenty-four hours, and the threshold duration of time is at least one minute and no more than ten minutes.

[0029] In some implementations, the one or more processors are configured to: receive, via one or more light sensors, light data associated with the computing device, and to control an operation of the computing device based on the heart rate circadian rhythm and the light data, wherein the operation of the computing device includes at least one of activating an output device to notify the user to take one or more actions based on the heart rate circadian rhythm and the light data, changing a state of the computing device based on the heart rate circadian rhythm and the light data, or changing a state of an external computing device, based on the heart rate circadian rhythm and the light data.

[0030] In an example embodiment, a computer-implemented method is provided. Thecomputer-implemented method includes iteratively determining, over a first predetermined duration of time and via one or more motion sensors, whether motion information associated with a user associated with the computing device satisfies a threshold movement level for greater than a threshold duration of time; each time the motion information associated with the user is determined to satisfy the threshold movement level for greater than the threshold duration of time, obtaining, via one or more heart rate sensors, heart rate information associated with the user; and determining, based on the heart rate information obtained during the first predetermined duration of time, a heart rate circadian rhythm associated with the user for a second predetermined duration of time, the second predetermined duration of time being less than the first predetermined duration of time.

[0031] The computer-implemented method may include further operations to execute other aspects and operations of the computing device as described herein.

[0032] In an example embodiment, a non-transitory computer-readable medium which stores instructions that are executable by one or more processors of a computing device is provided. The non-transitory computer-readable medium stores instructions which are executable by one or more processors of the computing device. The instructions include: instructions to cause the one or more processors to perform operations including iteratively determining, over a first predetermined duration of time and via one or more motion sensors, whether motion information associated with a user associated ith the computing device satisfies a threshold movement level for greater than a threshold duration of time; each time the motion information associated with the user is determined to satisfy' the threshold movement level for greater than the threshold duration of time, obtaining, via one or more heart rate sensors, heart rate information associated with the user; and determining, based on the heart rate information obtained during the first predetermined duration of time, a heart rate circadian rhythm associated with the user for a second predetermined duration of time, the second predetermined duration of time being less than the first predetermined duration of time.

[0033] The non-transitory computer-readable medium may store additional instructions to execute other aspects and operations of the computing device and computer-implemented method as described herein.

[0034] These and other features, aspects, and advantages of various embodiments of the disclosure will become better understood with reference to the following description, drawings, and appended claims. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate examples of the disclosure and, together withthe description, serve to explain the related principles.BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Detailed discussion of example embodiments directed to one of ordinary skill in the art is set forth in the specification, which makes reference to the appended drawings, in which:

[0036] FIG. 1 is an example system including block diagrams of a user computing device, a server computing system, and an external computing device, according to one or more examples of the disclosure;

[0037] FIG. 2 is an example illustration of a user computing device, according to one or more examples of the disclosure;

[0038] FIG. 3 is an example block diagram of a biometric measurement application, according to one or more examples of the disclosure;

[0039] FIG. 4 is an example graph illustrating collected heart rate data, according to one or more examples of the disclosure;

[0040] FIG. 5 is another example graph illustrating collected heart rate data, according to one or more examples of the disclosure;

[0041] FIG. 6 is an example graph illustrating collected motion data and motion coefficient data, according to one or more examples of the disclosure;

[0042] FIGS. 7A-7C are example graphs illustrating circadian rhythm data, according to one or more examples of the disclosure;

[0043] FIGS. 8A-8B are example graphs illustrating circadian rhythm data, according to one or more examples of the disclosure;

[0044] FIG. 9 depicts an example graph illustrating fitting a curve to circadian rhythm data, according to one or more examples of the disclosure;

[0045] FIG. 10 is another example graph illustrating fitting a curve to circadian rhythm data, according to one or more examples of the disclosure;

[0046] FIG. 11 illustrates an example flow diagram of a non-limiting computer- implemented method for determining circadian rhythm information of a user, according to one or more examples of the disclosure; and

[0047] FIG. 12 illustrates another example flow diagram of a non-limiting computer- implemented method for determining circadian rhythm information of a user, according to one or more examples of the disclosure.DETAILED DESCRIPTION

[0048] Reference now will be made to embodiments of the disclosure, one or more examples of which are illustrated in the drawings. Each example is provided by way of explanation of the disclosure and is not intended to limit the disclosure. In fact, it will be apparent to those skilled in the art that various modifications and variations can be made to the disclosure without departing from the scope or spirit of the disclosure. For instance, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the disclosure covers such modifications and variations as come within the scope of the appended claims and their equivalents.

[0049] Terms used herein are used to describe the example embodiments and are not intended to limit and / or restrict the disclosure. The singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. In this disclosure, terms such as "including", "having", “comprising”, and the like are used to specify features, numbers, steps, operations, elements, components, or combinations thereof, but do not preclude the presence or addition of one or more of the features, elements, steps, operations, elements, components, or combinations thereof.

[0050] It will be understood that, although the terms first, second, third, etc., may be used herein to describe various elements, the elements are not limited by these terms.Instead, these terms are used to distinguish one element from another element. For example, without departing from the scope of the disclosure, a first element may be termed as a second element, and a second element may be termed as a first element.

[0051] The term "and / or" includes a combination of a plurality of related listed items or any item of the plurality of related listed items. For example, the scope of the expression or phrase "A and / or B" includes the item "A", the item "B", and the combination of items "A and B”.

[0052] In addition, the scope of the expression or phrase "at least one of A or B" is intended to include all of the following: (1) at least one of A, (2) at least one of B, and (3) at least one of A and at least one of B. Likewise, the scope of the expression or phrase "at least one of A, B, or C" is intended to include all of the following: (1) at least one of A, (2) at least one of B, (3) at least one of C, (4) at least one of A and at least one of B, (5) at least one of A and at least one of C, (6) at least one of B and at least one of C. and (7) at least one of A. atleast one of B, and at least one of C.

[0053] A “circadian rhythm” refers to a natural cycle in human physiology that is approximately 24 hours long, and manifests as increases and decreases in various bodily functions and processes, such as a person’s sleep-wake cycle, digestion, metabolism, body temperature, and hormone secretion, which in turn influences the person’s cognitive and physical performance. For example, a person's resting heart rate may be at a lowest point in the middle of the night, and at its highest point in the middle of the day. Likewise, a person's core body temperature may reach a lowest point in the middle of sleep, and a highest point during daily activity. According to some studies, a “strong” circadian rhythm may be associated with improved physical and mental health and cognitive and physical performance. How ever, while people are somewhat aware of the period and phase and amplitude of their circadian rhythm (e.g., they will feel more tired at nighttime, and more alert during the day), these characteristics are not easily accessible to them. Likewise, when their circadian rhythm is disturbed (e.g., by travel or shift work), it is hard for people to assess the change. Note that there are many different circadian rhythms for a single person, and that they may have offset phases and characteristics (e.g., the time course of melatonin levels is less sinusoidal than that for body temperature and offset in time). There are also endogenous circadian rhythms (a rhythm that exists independent of external influences such as light or social conventions) and exogenous rhythms (a rhythm imposed by an external factor such as a work schedule). Typically, most endogenous rhythms can be influenced slowly by exogenous factors (e.g., a person may get used to waking up and going to sleep earlier if their body is given some time to adjust).

[0054] According to examples of the disclosure, a wearable computing device (e.g., smartwatches and trackers) can be configured to quantify an individual's circadian rhythm. In some implementations, information utilized by the wearable computing device to determine a circadian rhythm can also be augmented by information obtained from auxiliary devices such as a mobile phone or questionnaires.

[0055] In some implementations, the wearable computing device can be configured to provide precise daily guidance tailored to a users’ chronotype and circadian phase and may provide a personalized schedule that is best suited to the needs of the person. By measuring the circadian rhythm, the wearable computing device can be configured to provide information associated with optimal times for different health behaviors, which could include times for sleeping, winding down for bed, exercising, eating, focusing, being creative, takinga break, etc., with the overall goal of helping users feel their best and optimize their health. In some implementations, the wearable computing device can also analyze endogenous and exogenous rhythms, and their potential misalignment, to provide valuable insight to users.

[0056] According to examples of the disclosure, a wearable computing device includes a plurality of sensors (e.g., ph otoplethy smogram (PPG) sensors, accelerometers, skin temperature sensors, electrodermalconductance sensors, etc.). Raw data from these sensors can be processed to produce a time series of relevant physiological variables (e.g., heart rate, core body temperature, heart rate variability, movement, kidney function, liver function, digestive function, average movement level, average skin temperature, average skin conductance, individual heartbeat intervals, etc.). For example, the wearable computing device may be configured to utilize a raw PPG signal to provide an estimate of heart rate ( e . g . , every 1 second during the day). For example, the wearable computing device may be configured to extract or determine an average movement coefficient (e.g., every thirty seconds) by processing the output of the accelerometer.

[0057] According to examples of the disclosure, the wearable computing device may be configured to estimate or determine a heart rate circadian rhythm for an individual user based on the data output by the sensors. For example, the wearable computing device may be configured to extract or determine a user’s heart rate at "‘resting times” throughout the day. For example, a resting time can be defined as a period where the person has not made any significant movement (e.g., greater than a threshold movement level) for a predetermined duration of time (e.g., for the previous 10 minutes, the previous 5 minutes, etc.). Significant movement (or the threshold movement level) may be defined as a number of steps greater than 10 in the last minute, or an average motion coefficient which exceeds 4, or some other movement metric.

[0058] The wearable computing device may be configured to estimate the circadian rhythm based on the heart rate data which is obtained during the resting times over a course of a predetermined number of days (e.g., a period of the prior 7 days, the prior 14 days, the prior 30 days, etc.). Measurement points corresponding to resting heart rate values obtained at particular times (e.g., a resting heart rate of 59 bpm at 11 :00AM on day 1) throughout a particular day or over a course of multiple days can be tracked and stored. The wearable computing device may be configured to implement a least squares approach to fit a cosine curve (e.g., having a period of 24 hours) to the set of data points. The wearable computing device may be configured to determine an acrophase and bathyphase for the heart rate timeseries based on the generated cosine curve, and can also be configured to determine the magnitude of the circadian rhythm. For example, the magnitude of the circadian rhythm may be determined based on a value obtained by determining the difference between the peak heart rate value and minimum heart rate value and dividing the difference by the mean heart rate value peak (e.g., (peak HR fit - min HR fit) / mean HR).

[0059] In some implementations, the wearable computing device may be configured to implement a Fourier based approach which fits the time series using two or more Fourier modes.

[0060] In some implementations, the wearable computing device may be configured to utilize movement information associated with a user to determine a movement-based circadian rhythm of a user. For example, the wearable computing device may be configured to determine a “movement” and / or “sleep” coefficient. The motion coefficient, which is a movement detector, detects both small accelerations and orientation changes (e.g., on 30 second timescales) using the minimum and maximum accelerations along each axis of the accelerometer (e.g., a 3-axis accelerometer). The motion coefficient may be defined as: max [Lx max — x mean’ , y max — - vmean , z max - z mean ]J

[0061] where x, y, z are the raw accelerometer values (measured in m / s2), and max and mean refers to their maximum and average values over a predetermined period of time (e.g., a 30 second epoch).

[0062] In some implementations, the wearable computing device may be configured to determine a heart rate circadian rhythm and an average movement based circadian rhythm by averaging together the individual movement based circadian rhythms over a predetermined duration of time (e.g., the last 7 or 14 days).

[0063] In some implementations, the wearable computing device may be configured to utilize sleep information associated with a user to determine an estimated circadian rhythm. For example, the wearable computing device may be configured to determine a sleep period by evaluating the movement coefficient and using it to determine the most likely period of sleep. The w earable computing device may be configured to implement an algorithm to determine a sleep-wake pattern within the sleep period, or even to determine one or more stages of sleep (e.g., non-REM light sleep, non-REM deep sleep, REM etc.). For each night of sleep so determined, the wearable computing device may be configured to determine one or parameters: sleep onset timing, sleep offset timing, and sleep midpoint. The wearablecomputing device may be configured to determine a sleep pattern associated with a user over time and to also determine a person’s chronotype by using the actual measured time-to-bed, midpoint of sleep, and sleep-offset. A chronotype (diurnal preference) may refer to a person’s natural tendency for when they want to sleep and wake. This represents when a person organically feels the most energetic or focused throughout a 24-hour period. A person’s chronotype can impact when a person is best suited to perform an activity (e.g., exercise, focus for work or creative pursuits, eat, etc.) and may reflect various patterns including high energy levels and energy slumps during the course of a day.

[0064] In some implementations, the wearable computing device may be configured to obtain inputs from a user (e.g., via a graphical user interface, via voice inputs, etc.) in which a user can self-report various information (e.g., similar to filling out a questionnaire such as the Momingness-Eveningness Questionnaire) as additional information which can be used by the wearable computing device to determine a chronotype of the person.

[0065] In some implementations, the wearable computing device may be configured to, in response to determining a user’s circadian rhythm, provide precise daily guidance tailored to a user’s chronotype and circadian phase, recommending a personalized schedule that is best suited to the user’s body’s needs, etc. For example, the wearable computing device may be configured to provide personalized recommendations, nudges, and visualizations about timing of behaviors that can positively influence outcomes. In particular, the wearable computing device may be configured to recommend a daily schedule that could optimize a user’s circadian rhythm, health, and well-being. For example, the wearable computing device may be configured to provide information about an optimal wake time, morning energy peak, afternoon energy dip, evening energy peak, optimal bedtime, optimal time for exercise, information about optimal times for winding down, information about a user’s chronotype, information about an amount of sleep needed (e.g., in view of sleep debt), information about sleep goals, etc.

[0066] For example, the wearable computing device may be configured to provide notifications, provide an output (e.g., in the form of haptic feedback, audio feedback, display messages, etc.) to encourage a user to take certain actions. For example, the wearable computing device may be configured to provide an output that informs the user to wake up, go to bed, nap, exercise, take time to focus, take time to take a break, to expose oneself to sunlight or light, to wind down, to avoid sunlight or light, to eat, to take certain action to avoid becoming drowsy, to take certain action to avoid being alert, etc.

[0067] The wearable computing device may be configured to implement one or more machine-learned models to detect instances where a user may benefit from circadian rhythm optimization, identify scenarios where users’ physiology (e.g., heart rate ), behaviors, and / or environmental factors might be out of alignment with their circadian rhythm, or determine when nudges should be sent to users because they are acting out of sync with their circadian rhythm.

[0068] For example, when a user has an elevated heart rate before their usual bedtime, the wearable computing device may be configured to provide an output (e.g.. message, display of a graphic, etc.) that indicates to the user to do a relaxing wind down activity to allow their body to be ready for restful sleep. For example, if a user is exercising at a time that is out of alignment with their circadian rhythm, such as close to their bedtime, the wearable computing device may be configured to provide an output (e.g., message, display of a graphic, etc.) that indicates to the user to stop the activity or to warn the user that continuing with the activity may have a negative effect on their health and / or circadian rhythm.

[0069] Example aspects of the disclosure provide several technical effects, benefits, and / or improvements in computing technology and the technology of computing devices and health monitoring devices. For example, according to one or more examples of the disclosure, biometric measurements (e.g., a circadian rhythm) can be determined in an accurate and efficient manner by use of a biometric measurement application. Therefore, specialized equipment and complex clinical tests (e.g., melatonin testing) are not needed to determine the circadian rhythm.

[0070] Furthermore, according to one or more examples of the disclosure, a user computing device can determine the circadian rhythm of the heart rate accurately by implementing threshold motion values which are associated with movement of the user. For example, if the user computing device determines (e.g., via data measured by an inertial measurement unit) that motion of the user exceeds a threshold motion value, recording of the PPG signal can be delayed or stopped or values (data) obtained during such movement can be marked and subsequently filtered or ignored when determining heart rate values. Therefore, the data from the PPG signal that is utilized for determining the circadian rhythm may be more reliable by omitting data which is recorded when the user’s motion exceeds the threshold motion value. Further, computing resources may be conserved by delaying the capture of PPG signals for determining heart rate values until threshold motion values are satisfied (e.g., for threshold duration of time). Further, computing resources such as memoryor storage space may be conserved by delaying the capture of PPG signals for determining heart rate values until threshold motion values are satisfied (e.g., for threshold duration of time) so that heart rate values which are not useful for determining a resting heart rate to determine a circadian rhythm are not measured or stored unnecessarily. Further, computing resources such as bandwidth may be conserved in a case where heart rate values are determined remotely or are to be transmitted to a server computing system for analysis as PPG signal data or heart values which are not useful for determining a resting heart rate to determine a circadian rhythm may not be transmitted unnecessarily.

[0071] Referring now to the drawings, FIG. 1 illustrates an example system including block diagrams of a user computing device, a server computing system, and an external computing device, according to one or more examples of the disclosure. FIG. 2 is an example illustration of a user computing device which can be used for obtaining biometric information (e.g., a heart rate, a heart rate recovery metric, etc.) associated with a user via the biometric measurement application, according to one or more examples of the disclosure. FIG. 3 is an example block diagram of a biometric measurement application which may be provided to the user computing device, according to one or more examples of the disclosure.

[0072] In FIG. 1, the example system 1000 includes a user computing device 100, a server computing system 300, and an external computing device 400. For example, the user computing device 1 0, server computing system 300, and external computing device 400 may be connected with one another over a network 200. Any communications interfaces suitable for communicating via the network 200 (such as a network interface card) may be utilized as appropriate or desired by the user computing device 100, server computing system 300, and external computing device 400.

[0073] The user computing device 100 may include biometric wearable computing devices (e.g., a biometric smartwatch, a biometric tracker, etc.), a smartphone, and the like. In example embodiments described herein, the user computing device 100 may be any computing device that can measure biometric information of a user. The server computing system 300 may include a server, or a combination of servers (e.g., a web server, application server, etc.) in communication with one another, for example in a distributed fashion. The external computing device 400 may include a personal computer, a smartphone, a laptop, a tablet computer, and the like. In example embodiments described herein, the external computing device 400 may be a computing device that can communicate with the user computing device 100 to receive biometric information that is measured by the usercomputing device 100. The user computing device 100 may be configured to measure various biometrics, including biometrics associated with an ECG. PPG. heart rate, heart rate recovery, pulse information, BMI, heart rate variability, blood pressure, oxygen saturation, body temperature, sleep quality, physical activities (e.g., number of steps walked), a circadian rhy thm, a chronoty pe, and the like. Further, the user computing device 100 may be configured to generate or display information associated with an electrocardiogram, a photoplethysmogram, heart rate, heart rate recovery, blood pressure, oxygen saturation, respiration rate, body temperature, physical activity, a sleep metric, electrical conductance, a circadian rhythm, a chronotype, and the like.

[0074] Referring to FIG. 2, according to some implementations of the disclosure, in the illustrated overview 2000 the user computing device 2100 (e.g., a wearable computing device including a smartwatch, fitness tracker, etc.) may be configured to obtain various biometric information. For example, electrodes may be disposed at locations 2156, 2158 corresponding to sides or edges of the housing or body 2152 of the user computing device 2100. A first body part (e.g., a thumb) 2300 may contact a first electrode at a first location 2156 and a second body part (e.g., an index finger) 2400 may contact a second electrode at a second location 2158 to measure certain biometric information associated with the user (e.g., ECG information, heart rate information, etc.). Other electrodes may also be implemented to measure the ECG signal and the disclosure is not limited to the example of FIG. 2. For example, electrodes may be disposed at other locations including at the rear of the housing or body 2152 which contacts body part 2200, on the wrist strap 2500, integrated in the display device 2150, etc. In some implementations, one or more PPG sensors may additionally (or alternatively) be provided or disposed at the rear of the housing or body 2152 which contacts body part 2200. PPG signals output by the one or more PPG sensors may be used to measure biometric information associated with the user (e.g., heart rate information). The user computing device 2100 may include a display screen 2154 which may be touch-sensitive and can display biometric information (e.g., a ECG signal, PPG signal, heart rate, etc.), instructions for taking a biometric measurement, information indicating a biometric measurement is being taken (e.g., a countdown timer), etc.

[0075] For example, the network 200 may include any type of communications netw ork such as a local area network (LAN), wireless local area network (WLAN), wide area network (WAN), personal area network (PAN), virtual private network (VPN), or the like. For example, wireless communication between elements of the examples described herein may beperformed via a wireless LAN, Wi-Fi, Bluetooth, ZigBee, Wi-Fi direct (WFD), ultra wideband (UWB), infrared data association (IrDA). Bluetooth low energy (BLE), near field communication (NFC), a radio frequency (RF) signal, and the like. For example, wired communication betw een elements of the examples described herein may be performed via a pair cable, a coaxial cable, an optical fiber cable, an Ethernet cable, and the like.Communication over the network can use a wide variety of communication protocols (e.g., TCP / IP, HTTP, SMTP, FTP), encodings or formats (e.g., HTML, XML), and / or protection schemes (e.g., VPN, secure HTTP, SSL).

[0076] The user computing device 100 may include one or more processors 110, one or more memory devices 120, a biometric measurement application 130, an input device 140, a display device 150, an output device 160, one or more cameras 170, and one or more sensors 180. Each of the components of the user computing device 100 may be operatively connected with one another via a system bus. For example, the system bus may be any of several types of bus structures that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and / or a local bus using any of a variety of commercially available bus architectures.

[0077] The server computing system 300 may include one or more processors 310, one or more memory devices 320, and a biometric measurement application 330. Each of the features of the server computing system 300 may be operatively connected with one another via a system bus. For example, the system bus may be any of several ty pes of bus structures that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and / or a local bus using any of a variety of commercially available bus architectures.

[0078] The external computing device 400 may include a personal computer, a smartphone, a laptop, a tablet computer, and the like. In example embodiments described herein, the external computing device 400 may be a computing device that can communicate with the user computing device 100 to receive biometric information that is measured by the user computing device 100. The external computing device 400 can include some or all of the components described with respect to the user computing device 100 including the biometric measurement application 130. Therefore, descriptions of these components in the context of the user computing device 100 are also applicable to the external computing device 400 and will not be repeated for the sake of brevity. Each of the features of the external computing device 400 may be operatively connected with one another via a system bus. Forexample, the system bus may be any of several types of bus structures that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and / or a local bus using any of a variety of commercially available bus architectures.

[0079] For example, the one or more processors 110. 310 can be any suitable processing device that can be included in a user computing device 100 or server computing system 300. For example, such a processor 110, 310 may include one or more of a processor, processor cores, a controller and an arithmetic logic unit, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an image processor, a microcomputer, a field programmable array, a programmable logic unit, an applicationspecific integrated circuit (ASIC), a microprocessor, a microcontroller, etc., and combinations thereof, including any other device capable of responding to and executing instructions in a defined manner. The one or more processors 110, 310 can be a single processor or a plurality of processors that are operatively connected, for example in parallel.

[0080] The one or more memory devices 120, 320 can include one or more non- transitory computer-readable storage mediums, such as such as a Read Only Memory (ROM), Programmable Read Only Memory (PROM), Erasable Programmable Read Only Memory (EPROM), and flash memory, a USB drive, a volatile memory7device such as a Random Access Memory (RAM), an internal or external hard disk drive (HDD), floppy disks, a blueray disk, or optical media such as CD ROM discs and DVDs, and combinations thereof. However, examples of the one or more memory devices 120, 320 are not limited to the above description, and the one or more memory' devices 120, 320 may be realized by other various devices and structures as would be understood by those skilled in the art.

[0081] For example, the one or more memory7devices 120 can store instructions, that when executed, cause the one or more processors 110 to obtain, via one or more optical sensors, heart rate information associated with a user associated with the computing device during a first predetermined duration of time, obtain filtered heart rate information by selecting heart rate measurements from the heart rate information which w ere taken at times at which, prior to a corresponding heart rate measurement, motion information associated with the user satisfies a threshold movement level for greater than a threshold duration of time, and determine, based on the filtered heart rate information, a heart rate circadian rhythm associated with the user for a second predetermined duration of time which is less than the first predetermined duration of time, as described according to examples of the disclosure.

[0082] For example, the one or more memory devices 120 can store instructions, that when executed, cause the one or more processors 110 to iteratively determine, over a first predetermined duration of time and via one or more motion sensors, whether motion information associated w ith a user associated with the computing device satisfies a threshold movement level for greater than a threshold duration of time, each time the motion information associated with the user is determined to satisfy the threshold movement level for greater than the threshold duration of time, obtain, via one or more optical sensors, heart rate information associated with the user, and determine, based on the heart rate information obtained during the first predetermined duration of time, a heart rate circadian rhythm associated with the user for a second predetermined duration of time, the second predetermined duration of time being less than the first predetermined duration of time, as described according to examples of the disclosure.

[0083] For example, the one or more memory devices 320 can store instructions, that when executed, cause the one or more processors 310 to obtain, via one or more optical sensors of a computing device (e.g., user computing device 100), heart rate information associated with a user associated with the computing device during a first predetermined duration of time, obtain filtered heart rate information by selecting heart rate measurements from the heart rate information which were taken at times at which, prior to a corresponding heart rate measurement, motion information associated with the user satisfies a threshold movement level for greater than a threshold duration of time, and determine, based on the filtered heart rate information, a heart rate circadian rhythm associated with the user for a second predetermined duration of time which is less than the first predetermined duration of time, as described according to examples of the disclosure.

[0084] For example, the one or more memory devices 320 can store instructions, that when executed, cause the one or more processors 310 to iteratively determine, over a first predetermined duration of time and via one or more motion sensors of a computing device (e.g., user computing device 100), whether motion information associated with a user associated with the computing device satisfies a threshold movement level for greater than a threshold duration of time, each time the motion information associated with the user is determined to satisfy the threshold movement level for greater than the threshold duration of time, obtain, via one or more optical sensors of the computing device (e.g., user computing device 100), heart rate information associated with the user, and determine, based on the heart rate information obtained during the first predetermined duration of time, a heart ratecircadian rhythm associated with the user for a second predetermined duration of time, the second predetermined duration of time being less than the first predetermined duration of time, as described according to examples of the disclosure.

[0085] The one or more memory devices 120 can also include data 122 and instructions 124 that can be retrieved, manipulated, created, or stored by the one or more processors 1 10. In some examples, such data can be accessed and used as input to obtain and output the heart rate circadian rhythm, as described according to examples of the disclosure. The one or more memory devices 320 can also include data 322 and instructions 324 that can be retrieved, manipulated, created, or stored by the one or more processors 310. In some examples, such data can be accessed and used as input to obtain and output the heart rate circadian rhythm, as described according to examples of the disclosure.

[0086] The biometric measurement application 130 can include any biometric application which allows or is capable of detennining biometric information associated with a user (e.g., based on biometric measurements obtained via the one or more sensors 180). As explained with reference to FIG. 3, in some implementations, the biometric measurement application 130 includes a heart rate information determination application 132, a motion determiner 134, a motion coefficient value determiner 135, a circadian rhythm determiner 136, a chronotype determiner 138. and a user interface generator 139.

[0087] For example, in some implementations a user may execute the biometric measurement application 130 by providing an input to the user computing device 100 via input device 140 to measure, determine, and store a biometric measurement (e.g., a heart rate circadian rhythm that is obtained based on data associated with an optical signal output via the one or more optical sensors 184 and motion information that is obtained via the inertial measurement unit 182).

[0088] For example, in some implementations the biometric measurement application 130 may automatically be executed to measure, determine, and store a biometric measurement (e.g., a heart rate circadian rhythm that is obtained based on data associated wi th an optical signal output via the one or more optical sensors 184 and motion information that is obtained via the inertial measurement unit 182). For example, the biometric measurement application 130 may automatically be executed in response to the user computing device 100 detecting that the user wears the user computing device 100. For example, the biometric measurement application 130 may be configured to be automaticallydeactivated in response to the user computing device 100 detecting that the user has taken off the user computing device 100 (thereby saving computing resources including processing resources and energy resources).

[0089] The biometric measurement application 330 of the server computing system 300 can also include similar features as the biometric measurement application 130 (e.g., as shown in FIG. 3) which perform similar functions and operations, and therefore a description of those features will not be repeated for the sake of brevity.

[0090] The user computing device 100 may include an input device 140 configured to receive an input from a user and may include, for example, one or more of a keyboard (e.g., a physical keyboard, virtual keyboard, etc.), a mouse, a joystick, a button, a switch, an electronic pen or stylus, a gesture recognition sensor (e.g., to recognize gestures of a user including movements of a body part), an input sound device or voice recognition sensor (e.g., a microphone to receive a voice command), a track ball, a remote controller, a portable (e.g.. a cellular or smart) phone, and so on. The input device 140 may also be embodied by a touch-sensitive display device having a touchscreen capability, for example. The input device 140 may be used by the user of the user computing device 100 to provide an input to execute the biometric measurement application 130, to provide information about the user (e.g., biometric information, demographic information, user preferences, etc.). The input device 140 may be used by the user of the user computing device 100 to request a biometric measurement, to transmit biometric information of the user to the server computing system 300, external computing device 400, user biometric information data store 350, etc. For example, the input may be a voice input, a touch input, a gesture input, a click via a mouse or remote controller, and so on.

[0091] The user computing device 100 may include a display device 150 which presents information viewable by the user, for example on a user interface (e.g., a graphical user interface). For example, the display device 150 may be a touch sensitive display or a nontouch sensitive display. The display device 150 may include a liquid cry stal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, active matrix organic light emitting diode (AMOLED), flexible display. 3D display, a plasma display panel (PDP), a cathode ray tube (CRT) display, and the like, for example. However, the disclosure is not limited to these example display devices and may include other types of display devices.

[0092] The user computing device 100 may include an output device 160 configured to provide an output to the user and may include, for example, one or more of an audio device (e.g., one or more speakers), a haptic device to provide haptic feedback to a user, a light source (e.g., one or more light sources such as LEDs which provide visual feedback to a user), and the like. For example, in some implementations of the disclosure the user may be guided through a process for obtaining a biometric measurement including an ECG measurement, a PPG measurement, and the like.

[0093] The user computing device 100 may include one or more cameras 170. For example, the one or more cameras 170 may include an imaging sensor (e.g., a complementary metal-oxide-semiconductor (CMOS) or charge-coupled device (CCD)) to capture, detect, or recognize a user's behavior, figure, expression, etc.

[0094] The user computing device 100 may include one or more sensors 180. For example, the one or more sensors 180 may include an inertial measurement unit 182 which includes one or more accelerometers 182a and / or one or more gyroscopes 182b. The one or more accelerometers 182a may be used to capture motion information with respect to the user computing device 100. The one or more gyroscopes 182b may also be used additionally or alternatively to capture motion information with respect to the user computing device 100. For example, the inertial measurement unit 182 may be configured as a six-axis or sixdimensional inertial measurement unit (e.g., a tri-axial accelerometer and atri-axial gyroscope).

[0095] For example, the one or more sensors 180 may include one or more optical sensors 184 (e.g., one or more photoplethysmography (PPG) sensors) which can also be used to monitor the heart rate of the user. The one or more optical sensors 184 (e.g., one or more PPG sensors) may include one or more emitters (e.g., light-emitting diodes (LEDs)) and one or more detectors (e.g., photodiodes). For example, the one or more optical sensors 184 maybe configured to emit light (e g., green or red), onto the skin of the user and to measure variations in the intensity- of the reflected or transmitted light caused by changes in blood flow. For example, the one or more optical sensors 184 may be configured to capture the pulsatile nature of the blood flow which can be used to estimate various physiological parameters related to the cardiovascular system. For example, the heart rate of the user may be determined based on the frequency of the pulsatile signal. Additionally, the one or more optical sensors 184 may be configured to provide information about heart rate variability (HRV), blood oxygen saturation (SpO2) levels, and the like. Furthermore, in some examplesdescribed herein a pulse transit time (PTT) may be determined based on a measurement of the time delay between the R-wave peak of the ECG signal (representing the electrical activity of the heart) at a first location (e.g., a finger on the right hand) and the arrival of a corresponding pulse wave at a second location (e.g., a left wrist) where a PPG sensor is located. For example, the one or more optical sensors 184 may be disposed at a side of the user computing device 100 (e.g.. a rear side) such that the one or more optical sensors 184 are in contact with a body part of the user. For example, the one or more optical sensors 184 may be disposed such that a heart rate of the user may be passively monitored and measured without a user actively or consciously engaging the one or more optical sensors 184.

[0096] For example, the one or more sensors 180 may include one or more ECG sensors 186 which can also be used to monitor the heart rate of the user. The one or more ECG sensors 186 may be configured to measure and record the electrical activity of the heart by capturing the electrical impulses generated by the heart's contractions.

[0097] For example, an ECG sensor may include a plurality of electrodes that are disposed to contact different areas of a user’s body. The electrodes detect electrical signals or impulses generated by the heart’s contractions which are processed and analyzed to generate an electrocardiogram (ECG). For example, the electrodes may be disposed at various locations that can come into contact with the user's skin (e.g.. a band of a smartwatch, one or more sides of the body of the user computing device 100, integrated as part of a display screen of the display device 150, etc.). When the user places one or more body parts (e g., their finger(s) or thumbs) on specified electrodes, the ECG sensor may be configured to record the electrical signals or impulses produced by the heart's contractions. The electrical signals or impulses cause specific patterns on an ECG graph. These patterns include waves, segments, and intervals, including the P wave, QRS complex, R wave, and T wave, which represent different phases of the heart's electrical activity. The ECG signal may be used to obtain various biometric information about the user including a heart rate which can be obtained by analyzing the intervals between consecutive R-waves on the ECG waveform, heart rate variability, pulse transit time as discussed above, etc.

[0098] For example, the one or more sensors 180 may include one or more light sensors 188 which can be configured to detect an amount of light (e.g., ambient light) in an environment of the user computing device 100. The one or more light sensors 188 may include an ambient light sensor, an ultraviolet light sensor, etc. In some implementations, the user computing device 100 (e.g.. biometric measurement application 130) may be configuredto, based on knowledge of a circadian rhythm associated with a user and an output associated with the one or more light sensors 188, provide an output (e.g., via output device 160) recommending that the user take action to increase their light exposure to enhance their daytime performance and sleep quality (and thus their circadian rhythm).

[0099] For example, the user computing device 100 (e.g., biometric measurement application 130) may determine based on an output from the one or more light sensors 188 that the user is not exposed to sunlight at a time when the user could benefit from sun exposure, and the user computing device 100 (e.g., biometric measurement application 130) may control the output device 160 to indicate (e.g., via a message on a graphical user interface of the display device 150) that the user should move to a location to be exposed to light, and / or may output a control to a home appliance (e.g., motorized shades) to allow sunlight in to an area where the user is located. For example, the user computing device 100 (e.g., biometric measurement application 130) may determine based on an output from the one or more light sensors 188 that the user is exposed to sunlight at a time when the user could benefit from darkness, and the user computing device 100 (e.g., biometric measurement application 130) may control the output device 160 to indicate (e.g., via a message on a graphical user interface of the display device 150) that the user should move to a location to avoid light, and / or may output a control to a home appliance (e.g., motorized shades) to block sunlight from entering an area where the user is located. In some implementations, the user computing device 100 (e.g., biometric measurement application 130) may be configured to store (e.g., at the user computing device 100 or externally) and track an amount of light (e.g., sunlight) that the user is exposed to over time (e.g., over the course of a day, multiple days, weeks, etc.) based on an output associated with the one or more light sensors 188. The user computing device 100 (e.g., biometric measurement application 130) may be configured to, based on the stored data, assess a user's amount and timing of light exposure, and make recommendations for their light exposure to enhance their daytime performance and sleep quality (and thus their circadian rhythm) and / or control external devices to optimize an amount of light that the user is exposed to enhance their daytime performance and sleep quality (and thus their circadian rhythm).

[0100] FIG. 1 are merely examples and the user computing device 100 may include more or fewer sensors (e.g., the one or more ECG sensors 186 may be omitted, the one or more light sensors 188 may be omitted, etc.). The one or more sensors 180 may also include other sensors such as a magnetometer, GPS sensor, proximity sensor, and the like. Theexample sensors shown in

[0101] Example system 1000 may include a user biometric information data store 350. In some implementations, user biometric information data store 350 can represent a single database. In some implementations, the user biometric information data store 350 represents a plurality of different databases accessible to the user computing device 100, server computing system 300, and external computing device 400. In some examples, the user biometric information data store 350 can include biometric information of a user or a plurality of users (e.g.. a hospital or medical facility database). In some examples, the user biometric information data store 350 can include information regarding one or more user profiles, including a variety of user data such as user preference data, user demographic data, user calendar data, user social network data, user historical health data, user activity data, and the like. For example, the user biometric information data store 350 can include any biometric information or information associated with the biometric information (e.g., time information associated with the collection of the biometric information, location information associated with the collection of the biometric information, etc.). The user biometric information and associated information may be associated with a user account. In some implementations described herein, heart rate information of a user may be stored in user biometric information data store 350, heart rate circadian rhythm information of a user may be stored in user biometric information data store 350, motion information of a user may be stored in user biometric information data store 350, chronotype information of a user may be stored in user biometric information data store 350, motion coefficient values of a user may be stored in user biometric information data store 350, etc.

[0102] The user biometric information data store 350 is provided to illustrate potential data that could be analyzed or stored, in some embodiments, by the user computing device 100 and / or server computing system 300 to maintain a record of biometric information associated with a user, for example. However, such user data may not be collected, used, or analyzed unless the user has consented after being informed of what data is collected and how such data is used. Further, in some embodiments, the user can be provided with a tool (e.g., in a biometric measurement application or via a user account) to revoke or modify the scope of permissions. In addition, certain information or data can be treated in one or more ways before it is stored or used, so that personally identifiable information is removed or stored in an encrypted fashion. Thus, particular user information stored in the user biometric information data store 350 may or may not be accessible to the user computing device 100and / or server computing system 300 based on permissions given by the user, or such data may not be stored in the user biometric information data store 350 at all.

[0103] Referring to FIG. 3, an example block diagram of a biometric measurement application is shown, according to one or more examples of the disclosure. FIG. 3 illustrates that the biometric measurement application 130 includes a heart rate information determination application 132. a motion determiner 134, a motion coefficient value determiner 135, a circadian rhythm determiner 136, a chronotype determiner 138, and a user interface generator 139. However, the biometric measurement application 130 may include fewer or more features than that show n in FIG. 3. For example, any of the features or operations of the components of biometric measurement application 130 may be provided separately from the biometric measurement application 130. For example, some operations (such as the determination of the heart rate by heart rate information determination application 132, the determination of the circadian rhythm by the circadian rhythm determiner 136, etc.) may instead be performed by the server computing system 300 (e.g., via biometric measurement application 330).

[0104] Operations of the biometric measurement application 130 will now be described in more detail with reference to FIGS. 3 through 12.

[0105] FIG. 4 is an example graph illustrating collected heart rate data (e.g., based on photoplethysmogram measurements), according to one or more examples of the disclosure. FIG. 5 is another example graph illustrating collected heart rate data (e.g., based on photoplethysmogram measurements), according to one or more examples of the disclosure. FIG. 6 is an example graph illustrating motion data and motion coefficient data, according to one or more examples of the disclosure. FIGS. 7A-7C are example graphs illustrating circadian rhythm data, according to one or more examples of the disclosure. FIGS. 8A-8B are example graphs illustrating circadian rhythm data, according to one or more examples of the disclosure. FIG. 9 depicts an example graph illustrating fitting a curve to circadian rhythm data, according to one or more examples of the disclosure. FIG. 10 is another example graph illustrating fitting a curve to circadian rhythm data, according to one or more examples of the disclosure. FIG. 11 illustrates an example flow diagram of a non-limiting computer-implemented method for determining circadian rhythm information of a user, according to one or more examples of the disclosure. FIG. 12 illustrates another example flow diagram of a non-limiting computer-implemented method for determining circadian rhythm information of a user, according to one or more examples of the disclosure.

[0106] The flow diagram of FIG. 11 illustrates a method 1100 for determining circadian rhythm of the heart rate of a user. Although shown in a particular sequence or order, unless otherwise specified, the order of the processes can be modified. Thus, the illustrated embodiments should be understood only as examples, and the illustrated processes can be performed in a different order, and some processes can be performed in parallel.Additionally, one or more processes can be omitted in various embodiments. Thus, not all processes are required in even’ embodiment. Other process flows are possible.

[0107] At operation 1110 the method 1100 includes obtaining, via one or more optical sensors of a computing device, heart rate information associated with a user during a first predetermined duration of time. For example, the heart rate information determination application 132 can obtain heart rate information via the one or more optical sensors 184 (e.g., one or more PPG sensors). For example, the heart rate information can include a heart rate which is determined by the PPG signal analyzer 132a when the one or more optical sensors 184 include one or more PPG sensors and a PPG signal is obtained. For example, the heart rate information determination application 132 may determine heart rate values every second, even' five seconds, etc. In some implementations, the heart rate information determination application 132 can obtain heart rate information via the one or more ECG sensors 186 instead of, or in combination with, the one or more optical sensors 184 (e.g., one or more PPG sensors).

[0108] FIG. 4 is an example graph illustrating heart rate data collected over 28 day via photoplethysmogram measurements, according to one or more examples of the disclosure. As shown in FIG. 4, the graph 4000 includes heart rate data 4100 that is obtained based on original or raw data from one or more signals generated by the one or more optical sensors 184 (e.g., one or more PPG sensors). The graph 4000 also includes filtered heart rate data 4200 that is obtained based on the heart rate data 4100 which is acquired at a time when a movement associated with the user is less than a threshold level for a threshold duration of time. For example, the user computing device 100 may be configured to determine, for a particular heart rate measurement from the heart rate data 4100, whether movement associated with the user is less than a threshold movement level for a threshold duration of time, and if so, include the particular heart rate measurement as part of the filtered heart rate data 4200. In some implementations, the user computing device 100 may be configured to discard or delete heart rate data 4100 that is not determined to be part of the filtered heart rate data 4200, thereby saving computing resources such as memory or storage resources and / orbandwidth resources in the circumstance that such heart rate data may be transferred between user computing device 100 and server computing system 300 and / or external computing device 400.[01091 The filtered heart rate data 4200 may be associated with a particular confidence level based on the constraints associated with determining the filtered heart rate data 4200. For example, filtered heart rate data 4200 may have a confidence level of three where the filtered heart rate data 4200 corresponds to heart rate data from among the heart rate data 4100 which was measured at times at which, prior to a corresponding heart rate measurement, motion information associated with the user satisfied the threshold movement level (e.g., zero steps) for greater than the threshold duration of time (e.g., one minute, five minutes, etc.). For example, filtered heart rate data 4200 may have a confidence level of two where the filtered heart rate data 4200 corresponds to heart rate data from among the heart rate data 4100 which was measured at times at which, prior to a corresponding heart rate measurement, motion information associated with the user satisfied the threshold movement level (e.g., zero steps) for less than the threshold duration of time (e.g., thirty seconds) or satisfied another threshold movement level (e.g., less than 10 steps) for greater than the threshold duration of time (e.g., one minute, five minutes, etc.). For example, the filtered heart rate data 4200 may have a confidence level of one or zero for constraints on the heart rate data 4100 which are less strict than that of level two or level three.

[0110] For example, the threshold duration of time may be one minute, five minutes, ten minutes, etc. For example, the threshold movement level may be zero steps, zero movement of limbs of the user, etc. For example, the inertial measurement unit 182 may be configured to detect or measure movement associated with the user based on signals output via the one or more accelerometers 182a and / or one or more gyroscopes 182b.

[0111] For example, the first predetermined duration of time may be a period of one week, two weeks, four weeks, etc. The first predetermined duration of time may be determined by the user computing device 100 based on an amount of filtered heart rate data 4200 obtained. That is, a sparsity of filtered heart rate data may exist for a few days of heart rate data due to constraints on obtaining resting heart data (which is associated with little to no movement of the user for the threshold duration of time) and sufficient filtered heart rate data may need to be collected over a longer period of time so that a curve can be fitted (e.g., by circadian rhythm determiner 136) to the filtered heart rate data 4200 accurately to accurately denote the circadian rhythm of the user.

[0112] FIG. 5 is an example graph illustrating heart rate data collected over 28 day via photoplethysmogram measurements including an average or mean hourly heart rate data, according to one or more examples of the disclosure. As shown in FIG. 5, similar to graph 4000 from FIG. 4, the graph 5000 includes heart rate data 5100 that is obtained based on original or raw data from one or more signals generated by the one or more optical sensors 184 (e.g.. one or more PPG sensors) and filtered heart rate data 5200 that is obtained in a manner similar to that described for filtered heart rate data 4200 from FIG. 4. FIG. 5 also shows average or mean hourly heart rate data 5300. For example, user computing device 100 (e.g., heart rate information determination application 132) can be configured to determine an average heart rate value for each hour in a day among the 28 days based on the filtered heart rate data 5200. In some implementations, a median heart rate value can be determined rather than an average heart rate value.

[0113] At operation 1120 the method 1100 includes obtaining the filtered heart rate information based on motion information associated with the user.

[0114] As described herein, the heart rate data (heart rate information) may be filtered based on whether movement associated with the user is less than a threshold movement level for a threshold duration of time. In some implementations, the threshold movement level may be associated with a motion coefficient value.

[0115] For example, the heart rate information determination application 132 may be configured to obtain filtered heart rate information by selecting heart rate measurements from the heart rate information (e.g., heart rate data 4100, 5100) which were taken at times at which, prior to a corresponding heart rate measurement, motion information associated with the user satisfies a threshold movement level for greater than a threshold duration of time. For example, heart rate information which is taken at a measurement time tm where the user has not moved or taken a step in the last five minutes may satisfy the threshold movement level (zero steps or zero movement) for greater than the threshold duration of time (e.g., five minutes) and be included as part of the filtered heart rate information.

[0116] For example, motion determiner 134 may be configured to provide information about motion information associated with a user to any of the heart rate information determination application 132. motion coefficient value determiner 135, circadian rhythm determiner 136, chronotype determiner 138, and user interface generator 139. In some implementations, the motion determiner 134 may be configured to obtain motion informationof the user computing device 100 (which is associated with the user) based on data output by the inertial measurement unit 182 including the one or more accelerometers 182a and one or more gyroscopes 182b. The motion information may be obtained by the motion determiner 134 while the heart rate information is obtained by the heart rate information determination application 132 via the one or more optical sensors 184 or independently of heart rate measurements being taken (e.g., before heart rate measurements are taken).

[0117] In some implementations, the heart rate information determination application 132 may be configured to obtain filtered heart rate information by selecting heart rate measurements from the heart rate information (e.g., heart rate data 4100, 5100) which were taken at times at which, prior to a corresponding heart rate measurement, an average motion coefficient value associated with the user satisfies a threshold motion coefficient value, the average motion coefficient value being determined based on motion coefficient values determined during the threshold duration of time. For example, heart rate information which is taken at a measurement time tm where the average motion coefficient value satisfies the threshold motion coefficient value (e.g., less than five) may be included as part of the filtered heart rate information. For example, heart rate information which is taken at a measurement time where the average motion coefficient value does not satisfy the threshold motion coefficient value (e.g., more than five) may not be included as part of the filtered heart rate information and may be discarded or deleted.

[0118] For example, motion coefficient value determiner 135 may be configured to determine a motion coefficient value at regular intervals (e.g., every 30 seconds). The motion coefficient value can be indicative of movement of the user. The motion coefficient value determiner 135 may be configured to detect both small accelerations and orientation changes on predetermined timescales (e g., every 30 seconds). For example, the motion coefficient value determiner 135 may be configured to determine the motion coefficient value using the minimum and maximum accelerations along each axis of a 3-axis accelerometer (e.g., via the one or more accelerometers 182a). The motion coefficient value may be defined as max[xmax - xmean. ymax - ymean, zmax - zmean ], where x, y, z are the raw accelerometer values (measured in m / s2), and max and mean refers to their maximum and average values over a 30 second epoch. For example, the motion coefficient value determiner 135 may be configured to determine the average of the motion coefficient values over a predetermined duration of time which may correspond to the threshold duration of time (e.g., one minute, five minutes, etc.) to obtain the average motion coefficient value forthe threshold duration of time.

[0119] In some implementations, the filtered heart information may include the heart rate information (e g., heart rate data 4100, 5100) which corresponds to heart rate measurements that satisfy both the threshold movement level for greater than the threshold duration of time and the threshold motion coefficient value.

[0120] FIG. 6 is an example graph illustrating motion data and motion coefficient data, according to one or more examples of the disclosure. In FIG. 6, the graph 6000 includes motion data 6100 (e.g., number of steps taken) and average motion coefficient values 6200. For example, filtered heart rate information may be obtained at times when the motion data indicates movement information (e.g.. number of steps taken in the last minute or five minutes) associated with the user is less than a threshold movement level. For example, filtered heart rate information may be obtained at times when the average motion coefficient value is less than a threshold motion coefficient value (e.g., four, five, etc.).

[0121] At operation 1130 the method 1100 includes determining, based on the filtered heart rate information, a heart rate circadian rhythm associated with the user for a second predetermined duration of time which is less than the first predetermined duration of time.

[0122] For example, as described herein circadian rhythm determiner 136 may be configured to determine, based on the filtered heart rate information, a heart rate circadian rhythm associated with the user for a second predetermined duration of time which is less than the first predetermined duration of time.

[0123] As described herein, in some implementations, circadian rhythm determiner 136 may be configured to determine an average (mean) hourly heart rate for predetermined time periods over the first predetermined duration of time. For example, circadian rhythm determiner 136 may be configured to determine an average (mean) hourly heart rate for each hour over the first predetermined duration of time (e.g., over two weeks, four weeks, etc.). In some implementations, a median value may be determined instead of a mean value.

[0124] Circadian rhythm determiner 136 may be configured to determine a median of the mean hourly rates for the predetermined time period over the second predetermined duration of time. For example, circadian rhythm determiner 136 may be configured to determine a median of the mean (or median) hourly heart rates for each hour over the second predetermined duration of time (e.g., over one day).

[0125] For example, in some implementations, circadian rhythm determiner 136 may beconfigured to fit a curve to the median heart rate values to determine the circadian rhythm of the user. FIGS. 7A-7C are example graphs illustrating circadian rhythm data, according to one or more examples of the disclosure. In FIG. 7 A, graph 7100 illustrates mean hourly heart rate data for each hour of the day that is collected over a 28 day period (first predetermined duration of time). The graph 7100 indicates that many of the data points correspond to outliers, for example data point 7110. For example, an outlier data point may correspond to a mean hourly heart rate value that is greater than a 95thpercentile value among heart rate values (e g., filtered heart values) obtained during the first predetermined duration of time. However, a different percentile value can be used for removing outliers (e.g., 90thpercentile, 98thpercentile, etc.).

[0126] In FIG. 7B, graph 7200 illustrates median values of the mean hourly heart rates for each hour of the day for a 24 hour period (second predetermined duration of time). The graph 7200 includes median values of the mean hourly heart rates 7210 for each hour of the day for a 24 hour period based on the mean hourly heart rate values without removing outliers. The graph 7200 also includes median values of the mean hourly heart rates 7220 for each hour of the day for a 24 hour period based on the mean hourly heart rate values with outliers being removed (greater than 95thpercentile). For example, circadian rhythm determiner 136 may be configured to determine the median hourly value for each hour during the second predetermined duration of time from the mean hourly heart rate values. For example, in some implementations the circadian rhythm determiner 136 may be configured to determine the mean hourly value for each hour during the second predetermined duration of time from the mean hourly heart rate values. For example, in some implementations the circadian rhythm determiner 136 may be configured to remove the outliers from the mean hourly heart rate values before determining the median (or mean) values of the mean hourly heart rate values.

[0127] In FIG. 7C, graph 7300 illustrates median values of the mean hourly heart rates for each hour of the day for a 24 hour period (second predetermined duration of time) overlaid on the mean hourly heart rate values collected over 28 days (first predetermined duration of time). For example, the median values of the mean hourly heart rates for each hour of the day for a 24 hour period (second predetermined duration of time) may correspond to a circadian rhythm of the heart rate (CRHR). For example, in some implementations a visualization of the CRHR may be provided for display for on the display device 150 via the user interface generator 139. For example, the visualization may be in a form similar to thegraph 7200 or graph 7300.

[0128] FIGS. 8A-8B are example graphs illustrating circadian rhythm data, according to one or more examples of the disclosure. In FIG. 8A, graph 8100 illustrates mean hourly heart rate data for each hour of the day that is collected over a 28 day period (first predetermined duration of time) where the heart rate data has been filtered by removing average heart value data points where movement information associated with the user indicates the user has taken steps in the five minutes prior to the heart rate measurement. As can be seen from graph 8100 the heart rate data has become sparser such that median heart rate values of the mean hourly rates may not be obtained for one or more hours of the second predetermined duration of time, as illustrated in graph 8200 of FIG. 8B.

[0129] Circadian rhythm determiner 136 may further be configured to fit a curve to the median values of the mean hourly heart rates for each hour of the day for the 24 hour period (second predetermined duration of time). For example, circadian rhythm determiner 136 may be configured to implement a least squares approach to fit a cosine curve (e.g., having a period of 24 hours) to the set of data points. The circadian rhythm determiner 136 may be configured to determine an acrophase and bathyphase for the heart rate time series based on the generated cosine curve and can also be configured to determine the magnitude of the circadian rhythm. For example, the circadian rhythm determiner 136 may be configured to determine the magnitude of the circadian rhythm based on a value obtained by determining the difference between the peak heart rate value and minimum heart rate value and dividing the difference by the mean heart rate value peak (e.g., (peak HR fit - min HR fit) / mean HR).

[0130] For example, in some implementations the circadian rhythm determiner 136 may be configured to implement a Fourier based approach which fits the time series using two or more Fourier modes and / or to implement a cosine analysis which fits a cosine curve with 24 hour and 12 hour components.

[0131] FIG. 9 depicts an example graph illustrating fitting a curve to circadian rhythm data, according to one or more examples of the disclosure. In the example of FIG. 9, the circadian rhythm determiner 136 may fit a curve to average heart rate values 9100 for each hour of the second predetermined duration of time by performing cosine analysis to model the circadian rhythm of the heart rate (CRHR) to obtain the modeled heart rate curve shown in graph 9200. For example, the CRHR may be modeled according to CRHR = p + Ai sin (27rt / 24 + epi) + A2 sin(27tt / 12 + 2) where p may represent an average heart rate value of theaverage heart rate values, Ai may correspond to an amplitude that corresponds to a difference between p and a peak of the modeled CRHR curve over a 24 hour period, and A2 may correspond to an amplitude that corresponds to a difference between p and a peak of the modeled CRHR curv e over a 12 hour period. For example, the CRHR may be modeled according to CRHR = p + A cos [(2n / 24 (t - 0)] where p may represent an average heart rate value of the average heart rate values and A may be an amplitude that corresponds to a difference between p and a peak of the modeled CRHR curve.

[0132] FIG. 10 depicts another example graph illustrating fitting a curve to circadian rhythm data, according to one or more examples of the disclosure. In the example of FIG. 10, the circadian rhythm determiner 136 may fit a curve directly to heart rate values 1010 (rather than average heart rate values) for each hour of the second predetermined duration of time by performing cosine analysis to model the circadian rhythm of the heart rate (CRHR) to obtain the modeled heart rate curve shown in graph 1020. In FIG. 10, the heart rate values are obtained over a period of 14 days. The heart rate values may correspond to heart rate measurements which have a confidence level of 3. For example, the CRHR may be modeled according to CRHR = p + Ai sin (2at / 24 + (pi ) + A2 sin(2jrt / l 2 + 2) where p may represent an average heart rate value of the heart rate values, Ai may correspond to an amplitude that corresponds to a difference between p and a peak of the modeled CRHR curv e over a 24 hour period, and A2 may correspond to an amplitude that corresponds to a difference between p and a peak of the modeled CRHR curve over a 12 hour period. For example, the CRHR may be modeled according to CRHR = p + A cos [(2n / 24 (t - 0)] where p may represent an average heart rate value of the heart rate values and A may be an amplitude that corresponds to a difference between p and a peak of the modeled CRHR curve.

[0133] FIG. 12 illustrates another example flow diagram of a non-limiting computer- implemented method for determining circadian rhythm information of a user, according to one or more examples of the disclosure.

[0134] The flow diagram of FIG. 12 illustrates a method 1200 for determining circadian rhythm of the heart rate of a user. Although shown in a particular sequence or order, unless otherwise specified, the order of the processes can be modified. Thus, the illustrated embodiments should be understood only as examples, and the illustrated processes can be performed in a different order, and some processes can be performed in parallel.Additionally, one or more processes can be omitted in various embodiments. Thus, not all processes are required in every’ embodiment. Other process flows are possible.

[0135] At operation 1210 the method 1200 includes iteratively determining motion information associated with a user over a first predetermined duration of time (e.g., over two weeks, four weeks, etc.). For example, motion information may be measured via one or more motions sensors (e.g., inertial measurement unit 182) at regular intervals (e.g., every 30 seconds, every one minute, etc.).

[0136] At operation 1210 the method 1200 includes determining whether the motion information associated with the user satisfies a threshold movement level. For example, the motion determiner 134 may be configured to iteratively determine, over the first predetermined duration of time and via the one or more motion sensors, whether the motion information associated with the user satisfies a threshold movement level for greater than a threshold duration of time. For example, the inertial measurement unit 182 may be configured to provide motion information associated with the user to the motion determiner 134. The threshold movement level and threshold duration of time may be similar to that described with respect to FIG. 11. When the motion information does not satisfy the threshold movement level for greater than the threshold duration of time, the biometric measurement application 130 may be configured to delay taking a heart rate measurement via the heart rate information determination application 132. When the motion information does satisfy the threshold movement level for greater than the threshold duration of time, the biometric measurement application 130 may be configured to execute the heart rate information determination application 132 and take a heart rate measurement via the heart rate information determination application 132.

[0137] For example, at operation 1230 the method 1200 includes obtaining, via one or more heart rate sensors of a computing device, heart rate information associated with the user during the first predetermined duration of time. For example, the heart rate information determination application 132 can obtain heart rate information via the one or more optical sensors 184 (e.g., one or more PPG sensors), one or more ECG sensors 186, or combinations thereof. For example, the heart rate information can include a heart rate which is determined by the PPG signal analyzer 132a when the one or more optical sensors 184 include one or more PPG sensors and a PPG signal is obtained.

[0138] At operation 1240 the method 1200 includes determining whether the first predetermined duration of time has elapsed. For example, the biometric measurement application 130 (e.g., the motion determiner 134, heart rate information determination application 132, etc.) may be configured to determine whether the first predeterminedduration of time has elapsed. If the first predetermined duration of time has not elapsed the method 1200 may return to operation 1210. If the first predetermined duration of time has elapsed, the method 1200 may continue to operation 1250.[01391 At operation 1250, the method 1200 includes determining, based on the heart rate information obtained during the first predetermined duration of time, a heart rate circadian rhythm associated with the user for a second predetermined duration of time, the second predetermined duration of time being less than the first predetermined duration of time. For example, as described herein circadian rhythm determiner 136 may be configured to determine, based on the filtered heart rate information, a heart rate circadian rhythm associated with the user for a second predetermined duration of time which is less than the first predetermined duration of time. Operation 1250 may correspond to operation 1130 of FIG. 11 and therefore a repeated description of the operation will be omitted for the sake of brevity.

[0140] In some implementations, the method 1200 may be varied by also verifying that a motion coefficient threshold value is satisfied before proceeding to operation 1230. For example, the motion coefficient value determiner 135 may be configured to iteratively determine motion coefficient values associated with the user over the first predetermined duration of time, and each time the motion information associated with the user at operation 1220 is determined to satisfy the threshold movement level for greater than the threshold duration of time and an average motion coefficient value associated with the user was less than a threshold motion coefficient value, obtain, via the one or more optical sensors, the heart rate information associated with the user, the average motion coefficient value being determined based on motion coefficient values determined during the threshold duration of time.

[0141] Information which is determined or obtained during implementation of the method 1100 or method 1200 can be stored in user biometric information data store 350. For example, circadian rhythm data, motion information, threshold values, the first and second predetermined durations of time, motion coefficient values, heart rate values, etc., can be stored in user biometric information data store 350.

[0142] In some implementations, the method 1100 or method 1200 may include an operation of controlling an operation of the user computing device 100 based on the heart rate circadian rhythm. The operation of the user computing device 100 may include at least oneof activating the output device 1 0 to notify the user to take one or more actions based on the heart rate circadian rhythm, changing a state of the user computing device 100 based on the heart rate circadian rhythm, or changing a state of the external computing device 400, based on the heart rate circadian rhythm.

[0143] As described herein, the user computing device 1 0 may be configured to utilize the determined circadian rhythm of the heart rate of the user to provide various outputs to the user, to control operations of the user computing device 100, to control operations of the external computing device 400, etc.

[0144] In some implementations, the user computing device 100 (e.g., biometric measurement application 130) may be configured to, in response to determining a user’s circadian rhythm, provide precise daily guidance tailored to a user’s chronotype and circadian phase, recommend a personalized schedule that is best suited to the user’s body’s needs, etc. For example, the user computing device 100 (e.g., biometric measurement application 130) may be configured to provide personalized recommendations, nudges, and visualizations about timing of behaviors that can positively influence outcomes for the user. In particular, the user computing device 100 (e.g., biometric measurement application 130) may be configured to recommend a daily schedule that could optimize a user’s circadian rhythm, health, and well-being. For example, the user computing device 100 (e.g., biometric measurement application 130) may be configured to provide information about an optimal wake time, morning energy peak, afternoon energy dip, evening energy peak, optimal bedtime, optimal time for exercise, information about optimal times for winding dow n, information about a user’s chronotype, information about an amount of sleep needed (e.g., in view of sleep debt), information about sleep goals, etc.

[0145] For example, the user computing device 100 (e.g., biometric measurement application 130) may be configured to provide notifications, provide an output (e.g., in the form of haptic feedback, audio feedback, display messages, etc.) to encourage a user to take certain actions. For example, the user computing device 100 (e.g., biometric measurement application 130) may be configured to provide an output that informs the user to wake up, go to bed, nap, exercise, take time to focus, take time to take a break, to expose oneself to sunlight or light, to wind down, to avoid sunlight or light, to eat, to take certain action to avoid becoming drowsy, to take certain action to avoid being alert, etc.

[0146] The user computing device 100 (e.g., biometric measurement application 130)may be configured to implement one or more machine-learned models to detect instances where a user may benefit from circadian rhythm optimization, identify scenarios where users' physiology (e.g., heart rate ), behaviors, and / or environmental factors might be out of alignment with their circadian rhythm, or determine when nudges should be sent to users because they are acting out of sync with their circadian rhythm. For example, when a user has an elevated heart rate before their usual bedtime, the user computing device 100 (e g., biometric measurement application 130) may be configured to provide an output via the user interface generator 139 (e.g., a message, a display of a graphic, etc.) that indicates to the user to do a relaxing wind dow n activity to allow7their body to be ready for restful sleep. For example, if a user is exercising at a time that is out of alignment with their circadian rhythm, such as close to their bedtime, the user computing device 100 (e.g.. biometric measurement application 130) may be configured to provide an output via the user interface generator 139 (e.g., a message, a display of a graphic, etc.) that indicates to the user to stop the activity or to warn the user that continuing with the activity may have a negative effect on their health and / or circadian rhythm.

[0147] For example, the user computing device 100 (e.g., biometric measurement application 130) may be configured to control an external computing device based on the determined circadian rhythm. For example, based on the determined circadian rhythm, the user computing device 100 (e.g., biometric measurement application 130) may provide an output to control the external computing device 400 to aid with circadian rhythm optimization. For example, the user computing device 100 (e.g.. biometric measurement application 130) may control a home appliance, lighting equipment, curtains, etc. to enhance a lighting condition at a specified time according to the determined circadian rhythm (and, for example, according to data associated with the lighting exposure output by the one or more light sensors 188) when the user could benefit from additional lighting (e.g., to avoid becoming drowsy, to boost an energy level, etc.). For example, the user computing device 100 (e g., biometric measurement application 130) may control a home appliance, audio equipment, etc. to play a sound or music to help calm a user at a specified time according to the determined circadian rhythm when the user could benefit from a calming activity such as relaxing music (e.g., to aid with sleep).

[0148] Chronotype determiner 138 may be configured to determine a chronotype associated with the user. In some implementations, the user computing device 100 (e.g.. chronotype determiner 138) may be configured to determine a sleep period of a user byevaluating the movement coefficient and using it to determine the most likely period of sleep. The user computing device 100 may be configured to implement an algorithm to determine a sleep-wake pattern within the sleep period, or even to determine one or more stages of sleep (e.g., non-REM light sleep, non-REM deep sleep, REM etc.). For each night of sleep so determined, the user computing device 100 may be configured to determine one or parameters, for example, sleep onset timing, sleep offset timing, and sleep midpoint. The user computing device 100 may be configured to determine a sleep pattern associated with a user over time and to also determine the person’s chronotype by using the actual measured time-to-bed, midpoint of sleep, and sleep-offset. The chronotype (diurnal preference) may refer to the user’s natural tendency for when they want to sleep and wake. This represents when a person organically feels the most energetic or focused throughout a 24-hour period. The user’s chronotype can impact when the user is best suited to perform an activity (e.g., exercise, focus for work or creative pursuits, eat, etc.) and may reflect various patterns including high energy7levels and energy slumps during the course of a day.

[0149] In some implementations, the user computing device 100 (e.g., chronotype determiner 138) may be configured to obtain inputs from a user (e.g., via a graphical user interface, via voice inputs, etc.) in which the user can self-report various information (e.g.. similar to filling out a questionnaire such as the Momingness-Eveningness Questionnaire) as additional information which can be used by the user computing device 100 to determine a chronotype of the person. Example chronoty pes may include a momingness type, an intermediate type, and an eveningness type. The momingness type may generally have a mid-sleep time on free days (e.g., weekends) at a time before about 2:30 am to before about 3:00 am. The intermediate type may generally have a mid-sleep time on free days (e.g., weekends) between about 2:30 am to about 5:30 am. The eveningness type may generally have a mid-sleep time on free days (e.g., weekends) after about 5:00 am to after about 5:30 am. The times associated with each of the different chronotypes may vary or be adjusted according to an age of the user. For example, younger individuals (e.g., 20 to 24 years old) may have mid-sleep times which are delayed compared to older individuals (e.g., 70 to 74 years old) by about one hour to about two hours. For example, in some implementations the chronotype determiner 138 may be configured to use the mean midpoint of sleep on weekends to determine the chronotype of the user.

[0150] As mentioned above, aspects of the disclosure have been described in view of the biometric measurement application 130 provided in the user computing device 100 withrespect to FIGS. 3 through 12. However, each of those aspects can also be applied to the biometric measurement application 330 provided in the server computing system 300, and thus some or all of the functions and operations of the biometric measurement application 130 may also be applied and carried out by the biometric measurement application 330 in a similar fashion, but will not be described again for the sake of brevity .

[0151] Aspects of the above-described example embodiments may be recorded in non- transitory computer-readable media including program instructions to implement various operations embodied by a computer. The media may also include, alone or in combination with the program instructions, data files, data structures, and the like. Examples of non- transitory computer-readable media include magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD ROM disks, Blue-Ray disks, and DVDs; magneto-optical media such as optical discs; and other hardware devices that are specially configured to store and perform program instructions, such as semiconductor memory, readonly memory (ROM), random access memory (RAM), flash memory. USB memory, and the like. Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter. The program instructions may be executed by one or more processors. The described hardware devices may be configured to act as one or more software modules in order to perform the operations of the above-described embodiments, or vice versa. In addition, a non-transitory computer-readable storage medium may be distributed among computer systems connected through a network and computer-readable codes or program instructions may be stored and executed in a decentralized manner. In addition, the non- transitory computer-readable storage media may also be embodied in at least one application specific integrated circuit (ASIC) or Field Programmable Gate Array (FPGA).

[0152] Each block of the flowchart illustrations may represent a unit, module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks may occur out of order. For example, two blocks shown in succession may in fact be executed substantially concurrently (simultaneously) or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

[0153] While the disclosure has been described with respect to various example embodiments, each example is provided by way of explanation, not limitation of thedisclosure. Those skilled in the art, upon attaining an understanding of the foregoing, can readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, the disclosure does not preclude inclusion of such modifications, variations and / or additions to the disclosed subject matter as would be readily apparent to one of ordinary7skill in the art. For example, features illustrated or described as part of one embodiment can be used with another embodiment to yield a still further embodiment. Thus, it is intended that the disclosure covers such alterations, variations, and equivalents.

Claims

WHAT IS CLAIMED IS:

1. A computing device, comprising: one or more memories configured to store one or more instructions; and one or more processors configured to execute the one or more instructions stored in the one or more memories to: obtain, via one or more optical sensors, heart rate information associated with a user associated with the computing device during a first predetermined duration of time, obtain filtered heart rate information by selecting heart rate measurements from the heart rate information which were taken at times at which, prior to a corresponding heart rate measurement, motion information associated with the user satisfies a threshold movement level for greater than a threshold duration of time, and determine, based on the filtered heart rate information, a heart rate circadian rhythm associated with the user for a second predetermined duration of time which is less than the first predetermined duration of time.

2. The computing device of claim 1, wherein the one or more optical sensors include one or more photoplethysmography (PPG) sensors disposed on the computing device.

3. The computing device of claim 1, wherein the one or more processors are further configured to: determine motion coefficient values associated with the user during the first predetermined duration of time, and obtain the filtered heart rate information by selecting the heart rate measurements from the heart rate information: which were taken at times at which, prior to the corresponding heart rate measurement, the motion information associated with the user satisfies the threshold movement level for greater than the threshold duration of time, and which were taken at times at which, prior to the corresponding heart rate measurement, an average motion coefficient value associated with the user satisfies a threshold motion coefficient value, the average motion coefficient value being determined based on motion coefficient values determined during the threshold duration of time.

4. The computing device of claim 1 , wherein the one or more processors are configured to determine, based on the filtered heart rate information, the heart rate circadian rhythm associated with the user for the second predetermined duration of time by: parameterizing the heart rate circadian rhythm according to a Fourier series decomposition with two modes.

5. The computing device of claim 1, wherein the one or more processors are configured to determine, based on the filtered heart rate information, the heart rate circadian rhythm associated with the user for the second predetermined duration of time by: parameterizing the heart rate circadian rhythm according to a cosine curve with twenty -four hour components and twelve hour components.

6. The computing device of claim 1, wherein the threshold movement level corresponds to a predetermined number of steps taken by the user during the threshold duration of time.

7. The computing device of claim 1, wherein the one or more processors are configured to determine, based on the filtered heart rate information, the heart rate circadian rhythm associated with the user for the second predetermined duration of time by: determining, based on the filtered heart rate information, an average heart rate for each hour during the first predetermined duration of time; and determining, based on the average heart rate for each hour during the first predetermined duration of time, a median heart rate value for each hour in a twenty-four day.

8. The computing device of claim 7, wherein determining, based on the filtered heart rate information, the average heart rate for each hour during the first predetermined duration of time comprises removing outlier heart rate values from the filtered heart rate information.

9. The computing device of claim 1, wherein the one or more processors are configured to determine, via one or more motion sensors, the motion information while the heart rate information associated with the user is obtained during the first predetermined duration of time.

10. The computing device of claim 1, wherein the one or more processors are configured to: control an operation of the computing device based on the heart rate circadian rhythm.

11. The computing device of claim 10, wherein the operation of the computing device includes activating an output device to notify the user to take one or more actions based on the heart rate circadian rhythm.

12. The computing device of claim 10, wherein the operation of the computing device includes changing a state of the computing device or of an external computing device, based on the heart rate circadian rhythm.

13. The computing device of claim 1, wherein the first predetermined duration of time is at least one week and no more than four weeks, and the second predetermined duration of time is twenty-four hours.

14. The computing device of claim 1, wherein the threshold duration of time is at least one minute and no more than ten minutes.

15. The computing device of claim 1 , wherein the computing device is a wearable computing device.

16. A computer-implemented method, comprising: obtaining, by a computing device and via one or more optical sensors, heart rate information associated w ith a user associated with the computing device during a first predetermined duration of time; obtaining, by the computing device, filtered heart rate information by selecting heart rate measurements from the heart rate information which were taken at times at which, prior to a corresponding heart rate measurement, motion information associated with the user satisfies a threshold movement level for greater than a threshold duration of time; and determining, by the computing device and based on the filtered heart rate information, a heart rate circadian rhythm associated with the user for a second predetermined duration of time which is less than the first predetermined duration of time.

17. A computing device, comprising: one or more memories configured to store one or more instructions; and one or more processors configured to execute the one or more instructions stored in the one or more memories to: iteratively determine, over a first predetermined duration of time and via one or more motion sensors, whether motion information associated with a user associated with the computing device satisfies a threshold movement level for greater than a threshold duration of time, each time the motion information associated with the user is determined to satisfy the threshold movement level for greater than the threshold duration of time, obtain, via one or more heart rate sensors, heart rate information associated with the user, and determine, based on the heart rate information obtained during the first predetermined duration of time, a heart rate circadian rhythm associated with the user for a second predetermined duration of time, the second predetermined duration of time being less than the first predetermined duration of time.

18. The computing device of claim 17, wherein the one or more processors are further configured to: iteratively determine motion coefficient values associated with the user over the first predetermined duration of time, and each time the motion information associated with the user is determined to satisfy the threshold movement level for greater than the threshold duration of time and an average motion coefficient value associated with the user was less than a threshold motion coefficient value, obtain, via the one or more heart rate sensors, the heart rate information associated with the user, the average motion coefficient value being determined based on motion coefficient values determined during the threshold duration of time.

19. The computing device of claim 17, wherein the one or more processors are configured to: control an operation of the computing device based on the heart rate circadian rhythm, wherein the operation of the computing device includes at least one of activating an output device to notify the user to take one or more actions based on the heart rate circadian rhythm.changing a state of the computing device based on the heart rate circadian rhythm, or changing a state of an external computing device, based on the heart rate circadian rhythm.

20. The computing device of claim 17, wherein the one or more processors are configured to: receive, via one or more light sensors, light data associated with the computing device, and to control an operation of the computing device based on the heart rate circadian rhythm and the light data, and control an operation of the computing device based on the heart rate circadian rhythm, wherein the operation of the computing device includes at least one of activating an output device to notify the user to take one or more actions based on the heart rate circadian rhythm and the light data, changing a state of the computing device based on the heart rate circadian rhythm and the light data, or changing a state of an external computing device, based on the heart rate circadian rhythm and the light data.

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