Patent Publication Number: US-2021177342-A1

Title: Cycle management for physiological monitoring devices

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims priority to U.S. Provisional Application No. 62/949,240 filed on Dec. 17, 2019, the entire content of which is hereby incorporated by reference. 
    
    
     TECHNICAL FIELD 
     The present disclosure generally relates to physiological monitoring, and more specifically to management of cycles of physical activity monitored with a wearable device. 
     BACKGROUND 
     Physiological monitoring of an individual during different periods such as training, recovery, inactivity, and sleep is useful for assessing the individual&#39;s health and fitness. This data can also inform decision making regarding future training activities. However, an assessment may depend on correctly identifying cycles of activity marked by intervals of significant sleep. There remains a need for a continuous physiological monitoring platform that flexibly accommodates varying cycles of sleep and awakeness. 
     SUMMARY 
     Cycles of awakeness and sleep are flexibly but uniquely mapped to calendar days in order to facilitate user interactions with continuously monitored physiological data, and to facilitate meaningful quantitative assessments of sleep, recovery, and physical strain over user cycles that vary above and below twenty four hours in duration. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The foregoing and other objects, features, and advantages of the devices, systems, and methods described herein will be apparent from the following description of particular embodiments thereof, as illustrated in the accompanying figures. The figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the devices, systems, and methods described herein. 
         FIG. 1  illustrates a device for physiological monitoring. 
         FIG. 2  shows a block diagram illustrating exemplary components of a device for continuous physiological monitoring. 
         FIG. 3  is a flow chart illustrating a method for acquiring heart rate data. 
         FIG. 4  is a flow chart illustrating a method for detecting heart rate variability in sleep states. 
         FIG. 5  is a flow chart illustrating a method for automated activity detection. 
         FIG. 6  shows an allocation of physiological cycles to calendar days. 
         FIG. 7  shows a user interface. 
         FIG. 8  shows a user interface. 
         FIG. 9  shows a method for allocating cycles to calendar days. 
     
    
    
     DETAILED DESCRIPTION 
     The embodiments will now be described more fully hereinafter with reference to the accompanying figures, in which preferred embodiments are shown. The foregoing may, however, be embodied in many different forms and should not be construed as limited to the illustrated embodiments set forth herein. Rather, these illustrated embodiments are provided so that this disclosure will convey the scope to those skilled in the art. 
     All documents mentioned herein are hereby incorporated by reference in their entirety. References to items in the singular should be understood to include items in the plural, and vice versa, unless explicitly stated otherwise or clear from the text. Grammatical conjunctions are intended to express any and all disjunctive and conjunctive combinations of conjoined clauses, sentences, words, and the like, unless otherwise stated or clear from the context. Thus, the term “or” should generally be understood to mean “and/or” and so forth. 
     Recitation of ranges of values herein are not intended to be limiting, referring instead individually to any and all values falling within the range, unless otherwise indicated, and each separate value within such a range is incorporated into the specification as if it were individually recited herein. The words “about,” “approximately” or the like, when accompanying a numerical value, are to be construed as indicating any deviation as would be appreciated by one of ordinary skill in the art to operate satisfactorily for an intended purpose. Similarly, words of approximation such as “approximately” or “substantially” when used in reference to physical characteristics, should be understood to contemplate a range of deviations that would be appreciated by one of ordinary skill in the art to operate satisfactorily for a corresponding use, function, purpose, or the like. The use of any and all examples or exemplary language (“e.g.,” “such as,” or the like) is intended merely to better illuminate the embodiments and does not pose a limitation on the scope of the disclosed embodiments. No language in the specification should be construed as indicating any unclaimed element as essential to the practice of the embodiments. 
     In the following description, it is understood that terms such as “first,” “second,” “top,” “bottom,” “up,” “down,” “above,” “below,” and the like, are words of convenience and are not to be construed as limiting terms unless expressly stated otherwise. 
     Exemplary embodiments provide physiological measurement systems, devices, and methods for continuous health and fitness monitoring. A monitoring device may include a lightweight wearable system with a strap that collects various physiological data or signals from a wearer. The strap may be used to position the system on a user, for example, on a wrist, ankle, bicep, and the like. The term “continuous,” as used herein in connection with heart rate data collection, refers to the collection of heart rate data continuously over extended periods, e.g., for several hours, a day, a night, a span of twenty four hours, or more than twenty four hours, as well as to the collection of heart rate data over such periods with sufficient resolution to enable detection of heart beats and heart rate variability over any suitable window within such periods. 
     An exemplary system includes one or more light emitters for emitting light at one or more desired frequencies toward the user&#39;s skin, and one or more light detectors for received light reflected from the user&#39;s skin. The light detectors may include a photo-resistor, a photo-transistor, a photo-diode, and the like. As light from the light emitters (for example, green light) pierces through the skin of the user, the blood&#39;s natural absorbance or transmittance for the light provides fluctuations in the photo-resistor readouts. These waves have the same frequency as the user&#39;s pulse since increased absorbance or transmittance occurs only when the blood flow has increased after a heartbeat. The system includes a processing module implemented in software, hardware or a combination thereof for processing the optical data received at the light detectors and continuously determining the heart rate based on the optical data. The optical data may be combined with data from one or more motion sensors such as accelerometers, gyroscopes, and the like to minimize or eliminate noise in the heart rate signal caused by motion or other artifacts (or with other optical data of another wavelength), and to evaluate motion of a user, e.g., for sleep detection, device location detection, workout type detection, and so forth. 
       FIG. 1  illustrates a device for physiological monitoring. The device  100  may, for example, be configured as a bracelet including one or more straps  102  for securing the device  104  to a user for continuous wear. The bracelet may or may not include a display screen, e.g., a screen  106  such as a light emitting diode (LED) display for displaying any desired data (e.g., instantaneous heart rate). 
     The device  100  may include components configured to provide various functions such as data collection and streaming functions of the bracelet. In some embodiments, the device  100  may automatically detect when it is being worn and initiate data acquisition correspondingly. In another embodiment, the device  100  may be started and stopped manually, e.g., at the discretion of a user to begin storing information or otherwise to mark the start or end of an activity period such as sleep or a workout. In some embodiments, the button may be held to initiate a time stamp and held again to end a time stamp, which may be transmitted, directly or through a mobile communication device application, to a website as a time stamp. In another aspect, the device may not include a button, and may be controlled, e.g., remotely or through other user control techniques such as physically tapping a surface of the device. 
     The device  100  may include a heart rate monitor. In one example, the heart rate may be detected from the radial artery, e.g., using photoplethysmography or any other suitable technique or combination of techniques. Thus, the wearable system may include a pulse sensor such that, when a user wears the device  100  around their wrist and tightens it, the sensor portion of the device  100  is secured over the user&#39;s radial artery or other blood vessel. Secure connection and placement of the pulse sensor over the radial artery or other blood vessel may allow measurement of heart rate and pulse. It will be understood that this configuration is provided by way of example only, and that other sensors, sensor positions, and monitoring techniques may also or instead be employed without departing from the scope of this disclosure. Suitable devices are described, for example, in U.S. Pat. No. 9,596,997, issued on Mar. 21, 2017 and U.S. Pat. App. Pub. No. 2014/0350356. The entire content of these applications is hereby incorporated by reference. 
     In general, Reflectance PhotoPlethysmoGraphy (RPPG) may be used for the detection of cardiac activity, which may provide for non-intrusive data collection, usability in wet, dusty and otherwise harsh environments, and low power requirements. In order to facilitate continuous use, the wearable system may include one, two or more sources of battery life, e.g., two or more batteries. In some embodiments, it may have a battery that can slip in and out of the head of the wearable system and can be recharged using an included accessory. Additionally, the wearable system may have a built-in battery so that, while the first battery is being charged, the wearable system and can still record data (during sleep, for example). In exemplary embodiments, the wearable system is enabled to automatically detect when the user is asleep, awake but at rest and exercising based on physiological data collected by the system. 
       FIG. 2  shows a block diagram illustrating exemplary components of a device for continuous physiological monitoring. The device  200 , which may be a wearable device as generally described above, may generally include one or more sensors  202 , a battery  204 , storage  206 , a processor  208 , memory  210 , a network interface  214 , and a user interface  216 . 
     The sensors  202  may include any sensor or combination of sensors suitable for heart rate monitoring as contemplated herein, as well as sensors  202  for detecting calorie burn, position (e.g., through a Global Positioning System or the like), motion, activity and so forth. The sensors  202  may include one or more sensors for activity measurement. In some embodiments, the system may include one or more multi-axes accelerometers and/or gyroscope to provide a measurement of activity. In some embodiments, the accelerometer may further be used to filter a signal from the optical sensor for measuring heart rate and to provide a more accurate measurement of the heart rate. In some embodiments, the wearable system may include a multi-axis accelerometer to measure motion and calculate distance. Motion sensors may be used, for example, to classify or categorize activity, such as walking, running, performing another sport, standing, sitting or lying down. The sensors  202  may, for example, include a thermometer for monitoring the user&#39;s body or skin temperature. In one embodiment, the sensors  202  may be used to recognize sleep based on a temperature drop, Galvanic Skin Response data, lack of movement or activity according to data collected by the accelerometer, reduced heart rate as measured by the heart rate monitor, and so forth. The body temperature, in conjunction with heart rate monitoring and motion, may be used to interpret whether a user is sleeping or just resting, as well as how well an individual is sleeping. The body temperature, motion, and other sensed data may also be used to determine whether the user is exercising, and to categorize and/or analyze activities as described in greater detail below. More generally, the sensors  202  may include any sensor or combination of sensors suitable for monitoring geographic location, physiological state, exertion, movement, and so forth in any manner useful for physiological monitoring as contemplated herein. 
     The battery  204  may include one or more batteries configured to allow continuous wear and usage of the wearable system. In one embodiment, the wearable system may include two or more batteries, such as a removable battery that may be removed and recharged using a charger, along with an integral battery that maintains operation of the device  200  while the main battery charges. In another aspect, the battery  204  may include a wireless rechargeable battery that can be recharged using a short range or long range wireless recharging system. 
     The storage  206  may include any computer-readable media for storing data collected by sensors  202  during operation of the device  200 . This may include volatile or non-volatile memory, removable memory, and so forth. 
     The processor  208  may include any microprocessor, microcontroller, signal processor or other processor or combination of processors and other processing circuitry suitable for performing the processing steps described herein. In general, the processor  208  may be configured by computer executable code stored in the memory  210  to provide activity recognition and other physiological monitoring functions described herein. 
     The network interface  214  may be configured to wirelessly communicate data to a server  220 , e.g., through an external network  218  such as any public or private network, or combination of the foregoing including, e.g., local area networks, the Internet, cellular data networks, and so forth. The network interface  214  may be used, e.g., to transmit raw or processed sensor data stored on the device  200  to the server  220 , as well as to receive updates, receive configuration information, and otherwise communicate with remote resources and the user to support operation of the device. 
     The user interface  216  may include any components suitable for supporting interaction with a user. This may, for example include a keypad, display, buzzer, speaker, light emitting diodes, and any other components for receiving input from, or providing output to, a user. In one aspect, the device  200  may be configured to receive tactile input, such as by responding to sequences of taps on a surface of the device to change operating states, display information and so forth. 
       FIG. 3  is a flow chart illustrating a method for acquiring heart rate data. 
     As shown in step  302 , the method  300  may include emitting light toward a user&#39;s skin, e.g., from light emitters in a physiological monitoring device such as any of those described herein. As shown in step  304 , light reflected from the user&#39;s skin may be detected at one or more light detectors positioned in the device, e.g., to detect light reflected from the light emitters. As shown in step  306 , the method  300  may include pre-processing signals or data associated with the reflected light using any suitable technique to facilitate detection of heart beats. As shown in step  308 , a processing module of the system may execute one or more computer-executable instructions associated with a peak detection algorithm to process data corresponding to the reflected light to detect a plurality of peaks associated with a plurality of beats of the user&#39;s heart. As shown in step  310 , the processing module may determine an RR interval based on the plurality of peaks detected by the peak detection algorithm. As shown in step  312 , the processing module may determine a confidence level associated with the RR interval. 
     Based on the confidence level associated with the RR interval estimate, the processing module may select either the peak detection algorithm or a frequency analysis algorithm to process data corresponding to the reflected light to determine the sequence of instantaneous heart rates of the user. The frequency analysis algorithm may process the data corresponding to the reflected light based on the motion of the user detected using, for example, an accelerometer. The processing module may select the peak detection algorithm or the frequency analysis algorithm regardless of a motion status of the user. It is advantageous to use the confidence in the estimate in deciding whether to switch to frequency-based methods as certain frequency-based approaches are unable to obtain accurate RR intervals for heart rate variability analysis. Therefore, an implementation maintains the ability to obtain the RR intervals for as long as possible, even in the case of motion, thereby maximizing the information that can be extracted. 
     As shown in step  314 , the method may include determining whether the confidence level associated with the RR interval is above (or equal to or above) a threshold. In certain embodiments, the threshold may be predefined, for example, about 50%-90% in some embodiments and about 80% in one non-limiting embodiment. In other embodiments, the threshold may be adaptive, i.e., the threshold may be dynamically and automatically determined based on previous confidence levels. For example, if one or more previous confidence levels were high (i.e., above a certain level), the system may determine that a present confidence level that is relatively low compared to the previous levels is indicative of a less reliable signal. In this case, the threshold may be dynamically adjusted to be higher so that a frequency-based analysis method may be selected to process the less reliable signal. 
     If the confidence level is above (or equal to or above) the threshold, the method  300  may proceed to step  316 , where the processing module uses a time domain technique such as detecting a plurality of peaks in time domain data to determine an instantaneous heart rate of the user. If the confidence level is not above the threshold, the method  300  may proceed to step  320 , which may include determining a heart rate using frequency domain data such as a frequency transform of available time domain data. As shown in step  322 , based on the instantaneous heart rate from step  316  or step  320 , the method  300  may include determining a heart rate variability for the user based on a sequence of instantaneous heart rate measurements. 
     As described above, a system applying the method  300  described above may switch between different analytical techniques for determining a heart rate such as a statistical technique for detecting a heart rate and a frequency domain technique for detecting a heart rate. These two different modes have different advantages in terms of accuracy, processing efficiency, and information content, and as such may be useful at different times and under different conditions. Rather than selecting one such mode or technique as an attempted optimization, the system may usefully switch back and forth between these differing techniques, or other analytical techniques, using a predetermined criterion. The system may include a display device configured to render a user interface for displaying the sequence of the instantaneous heart rates of the user, the RR intervals and/or the heart rate variability determined by the processing module. The system may include a storage device configured to store the sequence of the instantaneous heart rates, the RR intervals and/or the heart rate variability determined by the processing module. 
       FIG. 4  is a flow chart illustrating a method for detecting heart rate variability in sleep states. The method  400  may be used in cooperation with any of the devices, systems, and methods described herein, such as by operating a wearable, continuous physiological monitoring device to perform the following steps. The wearable, continuous physiological monitoring system may for example include a processor, one or more light emitting diodes, one or more light detectors configured to obtain heart rate data from a user, and one or more other sensors to assist in detecting stages of sleep. In general, the method  400  aims to measure heart rate variability in the last phase of sleep before waking in order to provide a consistent and accurate basis for calculating a physical recovery score. 
     As shown in step  402 , the method  400  may include detecting a sleep state of a user. This may, for example, include any form of continuous or periodic monitoring of sleep states using any of a variety of sensors or algorithms as generally described herein. 
     Sleep states (also be referred to as “sleep phases,” “sleep cycles,” “sleep stages,” or the like) may include rapid eye movement (REM) sleep, non-REM sleep, or any states/stages included therein. The sleep states may include different phases of non-REM sleep, including Stages 1-3. Stage 1 of non-REM sleep generally includes a state where a person&#39;s eyes are closed, but the person can be easily awakened; Stage 2 of non-REM sleep generally includes a state where a person is in light sleep, i.e., where the person&#39;s heart rate slows and their body temperature drops in preparation for deeper sleep; and Stage 3 of non-REM sleep generally includes a state of deep sleep, where a person is not easily awakened. Stage 3 is often referred to as delta sleep, deep sleep, or slow wave sleep (i.e., from the high amplitude but small frequency brain waves typically found in this stage). Slow wave sleep is thought to be the most restful form of sleep, which relieves subjective feelings of sleepiness and restores the body. 
     REM sleep on the other hand typically occurs 1-2 hours after falling asleep. REM sleep may include different periods, stages, or phases, all of which may be included within the sleep states that are detected as described herein. During REM sleep, breathing may become more rapid, irregular and shallow, eyes may jerk rapidly (thus the term “Rapid Eye Movement” or “REM”), and limb muscles may be temporarily paralyzed. Brain waves during this stage typically increase to levels experienced when a person is awake. Also, heart rate, cardiac pressure, cardiac output, and arterial pressure may become irregular when the body moves into REM sleep. This is the sleep state in which most dreams occur, and, if awoken during REM sleep, a person can typically remember the dreams. Most people experience three to five intervals of REM sleep each night. 
     Homeostasis is the balance between sleeping and waking, and having proper homeostasis may be beneficial to a person&#39;s health. Lack of sleep is commonly referred to as sleep deprivation, which tends to cause slower brain waves, a shorter attention span, heightened anxiety, impaired memory, mood disorders, and general mental, emotional, and physical fatigue. Sleep debt (the effect of not getting enough sleep) may result in the diminished abilities to perform high-level cognitive functions. A person&#39;s circadian rhythms (i.e., biological processes that display an endogenous, entrainable oscillation of about 24 hours) may be a factor in a person&#39;s optimal amount of sleep. Thus, sleep may in general be usefully monitored as a proxy for physical recovery. However, a person&#39;s heart rate variability at a particular moment during sleep—during the last phase of sleep preceding a waking event—can further provide an accurate and consistent basis for objectively calculating a recovery score following a period of sleep. 
     According to the foregoing, sleep of a user may be monitored to detect various sleep states, transitions, and other sleep-related information. For example, the device may monitor/detect the duration of sleep states, the transitions between sleep states, the number of sleep cycles or particular states, the number of transitions, the number of waking events, the transitions to an awake state, and so forth. Sleep states may be monitored and detected using a variety of strategies and sensor configurations according to the underlying physiological phenomena. For example, body temperature may be usefully correlated to various sleep states and transitions. Similarly, galvanic skin response may be correlated to sweating activity and various sleep states, any of which may also be monitored, e.g., with a galvanic skin response sensor, to determine sleep states. Physical motion can also be easily monitored using accelerometers or the like, which can be used to detect waking or other activity involving physical motion. In another aspect, heart rate activity itself may be used to infer various sleep states and transitions, either alone or in combination with other sensor data. Other sensors may also or instead be used to monitor sleep activity, such as brain wave monitors, pupil monitors, and so forth, although the ability to incorporate these types of detection into a continuously wearable physiological monitoring device may be somewhat limited depending on the contemplated configuration. 
     As shown in step  404 , the method  400  may include monitoring a heart rate of the user substantially continuously with the continuous physiological monitoring system. Continuous heart rate monitoring is described above in significant detail, and the description is not repeated here except to note generally that this may include raw sensor data, heart rate data or peak data, and heart rate variability data over some historical period that can be subsequently correlated to various sleep states and activities. 
     As shown in step  406 , the method  400  may include recording the heart rate as heart rate data. This may include storing the heart rate data in any raw or processed form on the device, or transmitting the data to a local or remote location for storage. In one aspect, the data may be stored as peak-to-peak data or in some other semi-processed form without calculating heart rate variability. This may be useful as a technique for conserving processing resources in a variety of contexts, for example where only the heart rate variability at a particular time is of interest. Data may be logged in some unprocessed or semi-processed form, and then the heart rate variability at a particular point in time can be calculated once the relevant point in time has been identified. 
     As shown in step  410 , the method  400  may include detecting a waking event at a transition from the sleep state of the user to an awake state. It should be appreciated that the waking event may be a result of a natural termination of sleep, e.g., after a full night&#39;s rest, or in response to an external stimulus that causes awakening prior to completion of a natural sleep cycle. Regardless of the precipitating event(s), the waking event may be detected via the various physiological changes described above, or using any other suitable techniques. While the emphasis herein is on a wearable, continuous monitoring device, it will be understood that the device may also receive inputs from an external device such as a camera (for motion detection) or an infrared camera (for body temperature detection) that can be used to aid in accurately assessing various sleep states and transitions. 
     Thus, the wearable, continuous physiological monitoring system may generally detect a waking event using one or more sensors including, for example, one or more of an accelerometer, a galvanic skin response sensor, a light sensor, and so forth. For example, in one aspect, the waking event may be detected using a combination of motion data and heart rate data. 
     As shown in step  412 , the method  400  may include calculating a heart rate variability of the user at a moment in a last phase of sleep preceding the waking event based upon the heart rate data. While a waking event and a history of sleep states are helpful information for assessing recovery, the method  400  described herein specifically contemplates use of the heart rate variability in a particular last phase of sleep as a consistent foundation for calculating recovery scores for a device user. Thus, step  412  may also include detecting a most recent slow wave sleep period immediately prior to the waking event, or otherwise determining the end of a slow wave or deep sleep episode immediately preceding the waking event. 
     It will be appreciated that the last phase of sleep preceding a natural waking event may be slow wave sleep. However, where a sleeper is awakened prematurely, this may instead include a last recorded episode of REM sleep or some other phase of sleep immediately preceding the waking event. In one aspect, this heart rate variability may be used. However, the end of a particular phase of sleep may provide a more consistent benchmark for assessing recovery. Thus, in such premature waking instances, a prior history may be reviewed to locate a point in time at or near the end of a particular phase of sleep, such as slow wave sleep or any other useful benchmark. This moment—the end of the last phase of slow wave sleep (or any other suitable phase) before waking—is the point at which heart rate variability data provides the most accurate and consistent indicator of physical recovery. Thus, with the appropriate point of time identified, the historical heart rate data (in whatever form) may be used with the techniques described above to calculate the corresponding heart rate variability. 
     It will be further noted that the time period for this calculation may be selected with varying degrees of granularity depending on the ability to accurate detect the last phase of sleep and an end of the last phase of sleep. Thus, for example, the time may be a predetermined amount of time before waking, or at the end of slow wave sleep, or some predetermined amount of time before the end of slow wave sleep is either detected or inferred. In another aspect, an average heart rate variability or similar metric may be determined for any number of discrete measurements within a window around the time of interest. Thus, for example, heart rate variability may be measured over one minute, or some other interval, preceding the end of the relevant phase (e.g., slow wave sleep), and this may be used to provide a consistent benchmark from sleep event to sleep event, and among different users. 
     As shown in step  414 , the method  400  may include calculating a duration of the sleep state. The quantity and quality of sleep may be highly relevant to physical recovery, and as such the duration of the sleep state may be used to calculate a recovery score. 
     As shown in step  418 , the method  400  may include evaluating a quality of heart rate data using a data quality metric for a slow wave sleep period, e.g., the slow wave sleep period occurring most recently before the waking event. As noted above, the quality of heart rate measurements may vary over time for a variety of reasons. Thus, the quality of heart rate data may be evaluated prior to selecting a particular moment or window of heart rate data for calculating heart rate variability, and the method  400  may include using this quality data to select suitable values for calculating a recovery score. For example, the method  400  may include calculating the heart rate variability for a window of predetermined duration within the slow wave sleep period having the highest quality of heart rate data according to the data quality metric. 
     As shown in step  420 , the method  400  may include calculating a recovery score for the user based upon the heart rate variability from the last phase of sleep. The calculation may be based on other sources of data. For example, the calculation of recovery score may be based on the duration of sleep, the stages of sleep detected or information concerning the stages (e.g., amount of time in certain stages), information regarding the most recent slow wave sleep period or another sleep period/state, information from the GSR sensor or other sensor(s), and so on. The method  400  may further include calculating additional recovery scores after one or more other waking events of the user for comparison to the previously calculated recovery score. The actual calculation of a discovery score is described in substantial detail above, and this description is not repeated here except to note that the use of a heart rate variability measurement from the last phase of sleep provides an accurate and consistent basis for evaluating the physical recovery state of a user following a period of sleep. 
     As shown in step  430 , the method  400  may include calculating a sleep score and communicating this score to a user. 
     In one aspect, the sleep score may be a measure of prior sleep performance. For example, a sleep performance score may quantify, on a scale of 0-100, the ratio of the hours of sleep during a particular resting period compared to the sleep needed. On this scale, if a user sleeps six hours and needed eight hours of sleep, then the sleep performance may be calculated as 75%. The sleep performance score may begin with one or more assumptions about needed sleep, based on, e.g., age, gender, health, fitness level, habits, genetics, and so forth and may be adapted to actual sleep patterns measured for an individual over time. 
     The sleep score may also or instead include a sleep need score or other objective metric that estimates an amount of sleep needed by the user of the device in a next sleep period. In general, the score may be any suitable quantitative representation including, e.g., a numerical value over some predetermined scale (e.g., 0-10, 1-100, or any other suitable scale) or a representation of a number of hours of sleep that should be targeted by the user. In another aspect, the sleep score may be calculated as the number of additional hours of sleep needed beyond a normal amount of sleep for the user. 
     The score may be calculated using any suitable inputs that capture, e.g., a current sleep deficit, a measure of strain or exercise intensity over some predetermined prior interval, an accounting for any naps or other resting, and so forth. A variety of factors may affect the actual sleep need, including physiological attributes such as age, gender, health, genetics and so forth, as well as daytime activities, stress, napping, sleep deficit or deprivation, and so forth. The sleep deficit may itself be based on prior sleep need and actual sleep performance (quality, duration, waking intervals, etc.) over some historical window. In one aspect, an objective scoring function for sleep need may have a model of the form: 
       SleepNeed=Baseline+ f   1 (strain)+ f   2 (debt)−Naps
 
     In general, this calculation aims to estimate the ideal amount of sleep for best rest and recovery during a next sleep period. When accounting for time falling asleep, periods of brief wakefulness, and so forth, the actual time that should be dedicated to sleep may be somewhat higher, and this may be explicitly incorporated into the sleep need calculation, or left for a user to appropriately manage sleep habits. 
     In general, the baseline sleep may represent a standard amount of sleep needed by the user on a typical rest day (e.g., with no strenuous exercise or workout). As noted above, this may depend on a variety of factors, and may be estimated or measured for a particular individual in any suitable manner. The strain component, f 1 (strain), may be assessed based on a previous day&#39;s physical intensity, and will typically increase the sleep need. Where intensity or strain is measured on an objective scale from 0 to 21, the strain calculation may take the following form, which yields an additional sleep time needed in minutes for a strain, i: 
     
       
         
           
             
               f 
                
               
                 ( 
                 i 
                 ) 
               
             
             = 
             
               
                 1 
                 . 
                 7 
               
               
                 1 
                 + 
                 
                   e 
                   
                     
                       
                         1 
                          
                         7 
                       
                       - 
                       i 
                     
                     3.5 
                   
                 
               
             
           
         
       
     
     The sleep debt, f 2 (debt), may generally measure a carryover of needed sleep that was not attained in a previous day. This may be scaled, and may be capped at a maximum, according to individual sleep characteristics or general information about long term sleep deficit and recovery. Naps may also be accounted for directly by correcting the sleep need for any naps that have been taken, or by calculating a nap factor that is scaled or otherwise manipulated or calculated to more accurately track the actual effect of naps on prospective sleep need. 
     However calculated, the sleep need may be communicated to a user, such as by displaying a sleep need on a wrist-worn physiological monitoring device, or by sending an e-mail, text message or other alert to the user for display on any suitable device. 
     In addition to sleep detection, a device such as any of the wearable physiological monitoring devices described herein may also or instead automatically detect activities such as workouts and the like. A technique suitable for deployment on a wearable physiological monitoring device is described in greater detail below. 
       FIG. 5  is a flow chart illustrating a method for automated activity detection, e.g., a method of determining the timing and classification of a workout. In general, the technique described below determines starting and ending points for a workout, e.g., using a workout detection algorithm. Provided that the workout meets predetermined criteria for heart rate and motion, and further provided that a location of the monitor on a user&#39;s body can be determined, a workout classification algorithm may be used to automatically determine a user&#39;s activity. Thus, the workout classification process may be sequenced to facilitate use of a deep convolutional neural network (or similar artificial intelligence tool) only when data suitable for the neural network is available. This filtering and identification strategy permits the creation of a compact, dedicated neural network that can be selectively employed only when it is likely to produce meaningful results. As a significant advantage, this phased approach yields a more compact, computationally efficient model that facilitates real time workout recognition and permits deployment of the entire workout recognition algorithm on a wearable device even when the device has limited computational resources selected to reduce size and preserve battery life. 
     As shown in step  502 , the method  500  may include training models such as machine learning models to assist in the activity recognition process. In general, this may begin by acquiring data from a number of users of a wearable physiological monitor over time, which may include labeled data, e.g., where a user self-identifies activities, unlabeled data, or some combination of these. 
     In one aspect, this includes training models for position detection and workout recognition using data from wearers of physiological monitoring devices. Using accumulated data from such systems, which may include heart rate and accelerometer data with user-reported and self-labeled activities for e.g., thousands of users, a process can be developed to pick out a workout from a stream of gathered biometric data. Then, artificial intelligence techniques such as a deep convolutional neural network may be used to classify a sport or other activity being performed during such a workout. 
     This process may use a variety of different machine learning tools or the like. In one aspect, training the models may include training a machine learning algorithm or other artificial intelligence tool to estimate a probability of a location of a wearable physiological monitor on a body of the user using data from an accelerometer in the wearable physiological monitor, which may be used as described below to derive a probability-based estimate of the location. This may also or instead include training a machine learning algorithm or other artificial intelligence tool or the like to estimate a probability of a location based on heart rate data from the monitor instead of, or in addition to, the accelerometer data. In another aspect, training the models may include training a model for auto-classification of a workout, such as by training a deep convolutional neural network to calculate a probability that a chunk of data including at least one of accelerometer data and heart rate data from the wearable physiological monitor during the workout is each of a number of candidate types for the workout. 
     It will be understood that models such as an automatic workout classification algorithm may be continuously updated over time as new user data becomes available. Thus, in one aspect, the method  500  may include receiving data from a group of users and updating the automatic workout classification algorithm and/or other models such as the position detection model based on a continuous stream of new user data. This data may be updated on a central server or other suitable facility based on new data from a plurality of existing users, from new users, or some combination of these, and models may be updated on any suitable schedule. Determining the position of the wearable physiological monitor on a body of a user may similarly include training a machine learning algorithm to identify the position and then updating the machine learning algorithm on a central server based on new data from a plurality of users. 
     In another aspect, the method  500  may include customizing the automatic workout classification algorithm for a particular user over time or otherwise adapting the automatic classification algorithm to a specific user based on prior workout data for the specific user. As a user creates and labels workout data, the models may learn temporal patterns such as when workouts typically begin and end, as well as the types of workouts typical for a user. Thus, for example, if a user consistently jogs several miles each morning, and plays tennis on Tuesday and Friday evenings, this user history may be incorporated into a user-specific model and applied prospectively for more accurate workout type classification. This may be particularly useful, for example, in discriminating among activities where a number of different types of sports, e.g., racquetball, tennis, and squash, have similar motion patterns. According to the foregoing, in one aspect, the automatic workout classification algorithm may detect the type of the workout based on a history of exercise for the user. This may include training the classification algorithm with user-specific data, or more heavily weighting user-specific data during training. This may also or instead include using independent discriminators, filters, rules, or the like based on observed user-specific data. 
     As shown in step  504 , the method  500  may include receiving data from a device such as a wearable physiological monitor, such as accelerometer data and heart rate data acquired by the wearable physiological monitor while being worn by a user. In general, receiving data in this context may include receiving data into a memory on the device, e.g., for local analysis, or receiving data at a remote server, or some combination of these. The remote server may be used, e.g., to process the data and report results to a user concerning workout classifications, or to aggregate the data with other data for updating or refining a machine learning algorithm or the like. 
     As shown in step  506 , the method may include transforming data into a form suitable for detection of activity such as a workout. In general, both heart rate data and accelerometer data may be useful in discriminating between workouts and non-workout activities. However, these data types can have different magnitudes, frequencies, and distributions that render it difficult to form useful conclusions based on a concurrent use of both data types. To address this variability, the heart rate data and/or accelerometer data may be transformed, e.g., into the log domain, where acceleration data appears to generally follow a lognormal distribution (logarithm of continuous probability of a random variable is normally distributed) while heart rate data appears to generally follow a normal distribution. The data can be added and further logarithmically transformed to provide a single, normally distributed variable that is separable beyond a standard deviation for workout versus non-workout activity. The probability of a workout can then be estimated using probability distributions for workout and non-workout activities based on the transformed and combined data. Further, when a workout is detected, a true/false discriminator may be applied to confirm or reject the identification based on, e.g., a model trained using a sample of known workout and non-workout activity using any suitable parameters, preferably uncorrelated parameters, derived from the heart rate and/or accelerometer data in a linear discriminant analysis or the like. 
     As shown in step  508 , the method  500  may include identifying a workout by a user of the wearable physiological monitor based on the heart rate data and the accelerometer data. For example, this may include detecting workout endpoints that mark a timewise beginning and end of a workout, such as by applying a threshold based on the accelerometer data and the heart rate data to identify two endpoints of an interval of increased physical activity indicative of a workout by a user of the wearable physiological monitor. This may more generally include any suitable technique for automatically identifying or selecting two endpoints of a workout. 
     As shown in step  510 , the method  500  may include segmenting the data in a workout, such as by dividing the data including the accelerometer data and the heart rate data into a number of sequential segments. This segmenting approach advantageously facilitates segment-by-segment processing of the transformed data in order to locally apply a probabilistic technique to individual segments in order to determine a position of the device on a user&#39;s body. 
     As shown in step  512 , the method  500  may include detecting a position of the wearable physiological monitor on a body of the user during the workout. This may begin, for example, by applying a machine learning algorithm or any other suitable algorithm to each of the number of sequential segments of the accelerometer data (and optionally heart rate data where this provides additional information useful for discriminating among device positions) to determine a probability that each one of the number of sequential segments includes data from one or more locations on a body of the user. This may further include selecting one of the one or more locations having a highest overall probability of being a current position from all of the number of sequential segments, and using this selected position as a position of the wearable physiological monitor on the body of the user. Or stated otherwise, determining the position may include determining the position based on one of a number of candidate positions from among the candidates created by the machine learning algorithm for any of the number of sequential segments that has the highest overall probability, aggregated across all of the segments, of being an actual position of the wearable physiological monitor. 
     This step may also or instead be implemented to specifically detect predetermined positions that have corresponding auto-classification models. Thus, determining the position may include identifying the position from a plurality of predetermined positions including the number of predetermined positions on the body for which the automatic workout classification algorithm is configured to detect a type of workout. The number of predetermined positions may include one or more of a wrist and an ankle. Other positions, such as the forearm and the bicep may also be used for positioning a wearable device, and may be one of the predetermined positions as contemplated herein. In one aspect, any other position, whether identified or not, may result in a nominal failure to detect a position if there is no corresponding workout auto-classification model available. 
     The position of a wearable physiological monitoring device may also or instead inform different modes of an existing algorithm, or be used to choose/select different algorithms for running on a system. For example, knowing the position of the device may improve the accuracy of workout auto-detection and classification (e.g., classifying cycling from the wrist may not be preferred, but classifying from the leg/ankle would be preferred). 
     If a position cannot be detected, the method  500  may return to step  504  and further data may be received from sensors of the device for analysis. In this case, the method  500  may generally include additional steps of receiving additional data from the wearable physiological monitor and identifying a second workout as previously described. If a position is detected, the method  500  may proceed to step  514  for position-based processing. 
     It should also be appreciated that the order of steps preceding the workout classification may be altered. For example, while the figure illustrates workout detection preceding position detection, a position of the device may instead be continuously monitored, and a threshold workout detection may only be performed when the device is in one or more predetermined locations on a body of the user/wearer. 
     As shown in step  514 , the method  500  may include conditionally employing an automatic workout classification algorithm to detect a type of the workout when the position is one of a number of predetermined positions on the body for which the automatic workout classification algorithm is configured to detect the type. For example, if a classification algorithm is only available for a wrist-worn device, step  514  may include conditionally employing an automatic workout classification algorithm to detect a type of the workout only when the position is a wrist of the user. If the position detected in step  512  is a position for which there is an automatic workout classification, then the method  500  may proceed to step  516  where a workout can be automatically classified. If the position detected in step  512  is a position for which there is not an automatic workout classification, then the method  500  may return to step  504  where additional data can be received from sensors of the device. 
     Additional processing steps may be performed. For example, selecting an algorithm as shown in step  514  may also or instead include determining whether a different position for the wearable physiological monitor can provide more accurate data for the type of the workout. When the position is identified and a different position can provide more accurate data (particularly when the different position can provide substantially more accurate data), the method  500  may include providing a notification to the user suggesting a movement of the wearable physiological monitor to the different position. 
     As shown in step  516 , the method  500  may include detecting a workout type. As described above, by constraining this inquiry to a known workout interval and data from a particular device location, the automated type determination becomes a tractable computing problem amenable to the use of artificial intelligence techniques such as a deep convolutional neural network. 
     Thus in one aspect, detecting the workout type may include conditionally detecting the workout type with an automatic workout classification algorithm including a deep convolutional neural network trained to calculate a probability that a chunk of data including at least one of accelerometer data and heart rate data from the wearable physiological monitor during the workout is each of a number of candidate types for the workout. More specifically, this may include applying the deep convolutional neural network to a number of chunks of data from the wearable physiological monitor to obtain a posterior distribution of the number of candidate types for the workout. The workout type may then be determined by selecting one of the number of candidate types in the posterior distribution having a highest probability of characterizing a workout type for the workout. An example of a type detection algorithm using a deep convolutional neural network as contemplated in step  516  is now described in greater detail. 
     The data for a workout interval may be divided into chunks or segments. For example, the workout may be split into smaller chunks that have some minimum total acceleration. This may include dividing the data into tensors, where the tensors contain accelerometer magnitude, as well as x, y, and z dimensional data. For each chunk received, a splitting procedure may be applied based on the amount of total accelerometer magnitude within some n-minute chunk. An example splitting procedure would be a 20-minute running activity that is split into four total 5-minute chunks, where each chunk has some minimum amount of total acceleration above a threshold. 
     Each chunk may be scored using a deep convolutional neural network for activities. The objective of the deep convolutional neural network in this context is to compute the probability that a chunk belongs to a pre-defined workout category. In certain implementations, examples of these categories may include “running,” “weightlifting,” and “cycling,” for which there may be a labeled dataset in the training sets for the neural network. Each input to the network is a collection of mini-batched n-minute chunks, represented by a tensor of three-dimensional accelerometer data. Each chunk may pass through four instances of a convolutional block including a convolutional operator that computes abstractions of the incoming data, and then passes that output through a non-linear activation function. The data may then be post-processed via a normalization operator. The last step may include block-pooling of adjacent elements in the tensor with an averaging function. Once the convolutional blocks have completed their computations, the model may use a combination of tensor-tensor dot products, non-linear activations, dropout algorithms, and normalization operators to compute the probability of a given chunk being a specific workout. As noted above, this neural network may be trained using training sets including labeled data from a group of users. While clustering or unsupervised learning is also suitable, the initial use of labeled data sets can provide more efficient training for discriminating among human-recognizable categories of activities such as specific sports or exercise types. 
     Standard convolutional neural networks may have strict API rules, particularly with the shape of the input data. Because workouts are generally variable in length, it may be advantageous to split the workout into K-minute chunks and classify based on that information. Thus, each input to the convolutional neural network may be shaped with three channels, with each channel corresponding to a K-minute one-dimensional array. Each chunk may then be mapped to the sport or activity to which the workout belonged. Preprocessing may be minimal, where the enforcement of orientation, continuity, and magnitude are the only hurdles. For orientation, while the user is provided information on a correct manner to orient the wearable physiological device, there may be no reliable way to enforce this orientation. Additionally, the handedness of a user can affect the read out of the accelerometer data. Transforming and scaling input data by user characteristics may enforce this notion. For continuity, the user removing their wearable physiological device during an activity, or in some way losing data, could negatively affect results. Thus, simple logic may be included to verify that there are no relatively large gaps in data. For magnitude, in order to verify the model is training on movement, a minimum magnitude of motion may be enforced for each potential sample. 
     In one aspect, each timewise chunk of data may have one or more activities and corresponding probabilities based on the output of the neural network. The chunks may be aggregated on their mean scores for each activity. In order to select a particular type of workout activity, the probabilities for these chunks are combined into an aggregated score in order to determine the probability that one activity is significantly more likely than the rest. If the probability is sufficiently high, the activity is classified, e.g., as one of the example categories or else it is marked as an unknown activity category. The output matrix from the network may include probabilities of each class on a per chunk basis. In one aspect, each model may output probabilities based exclusively on K-minute chunks. Because each activity often includes many chunks, the opportunity arises to intelligently determine an aggregate result. If it is assumed that each column forms a probability distribution for a particular sport or activity, a group of parametrized distributions can be compared from all rows and columns, such as: 
     
       
         
           
             
               
                 
                   [ 
                   
                     
                       
                         .15 
                       
                       
                         .1 
                       
                       
                         .1 
                       
                       
                         .65 
                       
                     
                     
                       
                         .10 
                       
                       
                         .05 
                       
                       
                         .05 
                       
                       
                         .80 
                       
                     
                     
                       
                         .25 
                       
                       
                         .025 
                       
                       
                         .025 
                       
                       
                         .70 
                       
                     
                     
                       
                         .60 
                       
                       
                         .05 
                       
                       
                         .05 
                       
                       
                         .30 
                       
                     
                   
                   ] 
                 
                 → 
                 μ 
               
               = 
               
                 [ 
                 
                   
                     
                       .275 
                     
                   
                   
                     
                       .056 
                     
                   
                   
                     
                       .056 
                     
                   
                   
                     
                       .613 
                     
                   
                 
                 ] 
               
             
             , 
             
               σ 
               = 
               
                 [ 
                 
                   
                     
                       .195 
                     
                   
                   
                     
                       .027 
                     
                   
                   
                     
                       .027 
                     
                   
                   
                     
                       .188 
                     
                   
                 
                 ] 
               
             
           
         
       
     
     If it is assumed that the model outputs roughly normally distributed values, and some reasonable prior values for the parameters are established, a posterior distribution can be created, and each workout can be expressed as a posterior distribution. Once a posterior for each sport or activity column is acquired, a probability can be computed that one of the probability distributions is larger than all the others. If that probability for one of the types of workouts is sufficiently high (P(B&gt;A)&gt;T), the workout can be classified to the corresponding type. 
     The method  500  may then return to step  504  where further data may be received and the process may continue. 
     Several advantages accrue to processing data in this manner. By initially restricting workout detection to intervals where a workout is detected, the overall detection scheme can be run continuously on a wearable device, with a simple initial filter (workout detection) for further processing. Further, by limiting use of a workout type detection algorithm such as a deep convolutional neural network to those situations in which the algorithm can produce meaningful results—when a workout is occurring and a position on the body has a corresponding detection model—the device can reduce or avoid the use of battery power and processing resources under conditions that are poor candidates for workout type detection. Additionally, by training the model for a more limited number of detection conditions, the size and complexity of the neural network can also be significantly reduced, thus permitting deployment, e.g., on a wearable device or other capacity constrained platform where memory or processing resources are limited. 
     According to the foregoing, there is also disclosed herein a wearable physiological monitoring device incorporating the features discussed above. Thus in one aspect, there is disclosed herein a system including a wearable housing, one or more sensors in the wearable housing configured to provide heart rate data and accelerometer data for a user of the wearable housing, a memory, and a processor. The processor may be configured by computer executable code stored in the memory to identify a workout by the user based on the heart rate data and the accelerometer data, determine a position of the wearable housing on a body of the user during the workout, and conditionally employ an automatic workout classification algorithm to detect a type of the workout when the position is one of a number of predetermined positions on the body for which the automatic workout classification algorithm is configured to detect the type. The processor may also be configured to conditionally employ the automatic workout classification algorithm to detect the type of the workout only when the position is a wrist of the user. The processor may further be configured to create a notification to the user to move the wearable housing to a different position when the position is not one of the number of predetermined positions on the body for which the automatic workout classification algorithm is configured to detect the type. As discussed above, the automatic workout classification algorithm may include a deep convolutional neural network trained to calculate a probability that a chunk of data including at least one of accelerometer data and heart rate data from the one or more sensors during the workout is each of a number of candidate types for the workout. 
     In one aspect, systems and methods contemplated herein may use cycles rather than twenty four hour days to process user information. While calendar days provide a useful and generally agreed upon standard for measuring the passage of time, individuals may have varying activity and sleep patterns that render calendar days unsuitable for accurate, quantitative assessments of sleep, strain, and recovery. Furthermore, specific disruptors such as napping, shift work, extended sleepless periods, and travel across multiple time zones can cause significant variations in the duration of cycles of physiological activity from the fixed, twenty four hour period of calendar days. To address these issues, physiological cycles, or simply “cycles,” may be used instead of calendar days to encapsulate periods of physiological data. 
     Using the following techniques, cycles of awakeness and sleep can be flexibly but uniquely mapped to calendar days in order to facilitate user interactions with continuously monitored physiological data through a calendar-based interface, and to facilitate meaningful quantitative assessments of sleep, recovery, and physical strain that are tied to physiological intervals rather than calendar intervals. These cycles may, for example, be generally demarcated by the passage of time from one significant sleep event to the next, or using any other physiologically occurring baseline or the like that is useful for fitting intervals of physical activity into a daily calendaring scheme. 
     The following techniques may obtain data from any of the physiological monitoring devices described above, or any other suitable source of intermittent, or preferably continuous, physiological data. In general, the physiological data may be a heart rate, heart rate variability, or other heart rate data, or other physiological data or physical data suitable for evaluating physical strain and recovery. The physiological data may be timestamped to support time-based processing as described herein, and may also usefully include time zone and/or other location data to assist in determining a time zone in which physiological data has been obtained. The physiological monitoring device, or an associated system, may automatically detect sleep events based on the physiological data, and/or may receive manual entry of sleep event information from a user, any of which may be used in evaluating boundaries for cycles of physiological activity. 
     In one implementation, physiological data may be encapsulated into cycles using a set of rules. Each cycle may be referenced to, and accessible based on, one or more dates, e.g., in a date-based user interface, and a cycle mapping function is preferably well-defined, such that each day maps to exactly one cycle. However, it is not generally necessary that each cycle maps uniquely to one day. For example, if someone completely skips sleep for a night, the resulting cycle of significantly more than twenty four hours may span two calendar days, with each of the days mapping to the single cycle and the cycle spanning multiple days. The cycle may also usefully map to data relevant to a physiological day experienced by the user on a corresponding calendar day, e.g., a day that timewise intersects the cycle. It will also be appreciated that a day where there is no data for the physiological monitoring device may map to a null cycle, with no quantitative data calculated or displayed (or both) for that day. 
     The relevant physiological day for a cycle may be demarcated by intervals of significant sleep. For any such cycle, a method may include evaluating all days that have at least some timewise overlap with the cycle, and finding a day that has at least twelve hours of overlap with the cycle. If no day has at least twelve hours of overlap, then the cycle may be assigned to a day with the greatest overlap, and in general, no assignment can be made that will cause a day to map to more than one cycle. Stated differently, a method for processing mapping physiological cycles into calendar days may include, for a first cycle in a number of cycles: if the first cycle has at least twelve hours of overlap with a first day of the calendar days, assigning the first cycle to the first day; and, if the first cycle does not have at least twelve hours of overlap with any day, assigning the first cycle to one of the calendar days having a greatest timewise overlap with the first cycle. 
     A predicted cycle end may then be calculated based on a beginning of the first cycle. This may, for example, be twenty four hours after the beginning of the first cycle. Or, for a collection of cycles forming non-overlapping intervals ordered by time, where each cycle C i  has a duration d(C i ) defined as an absolute length of time from the start of the cycle to the end of the cycle, a new predicted end may be determined using an equation such as: 
         d ( C   i )=max { 18 , 48 − d ( C   i−1 )}
 
     As each cycle is closed in the manner described above, e.g., upon the occurrence of a significant sleep event intersecting or adjacent to the new predicted end, a new cycle may be created and a new predicted end for the new cycle may be calculated. A sleep event may be added manually by a user, detected automatically based on physiological data, or some combination of these. In one aspect, a cycle-determination algorithm may try to determine if a sleep is a “significant” sleep that marks the beginning of a new cycle or if it is just a nap (or the like) in an existing cycle. Further, a user may explicitly identify a detected sleep event as a nap or as significant sleep. It will be understood that cycles may referenced to the beginning of each significant sleep event or the end of each significant sleep event, provided one such reference point is consistently applied, and suitable adaptations may be made to the corresponding calculations for sleep and recovery in each cycle. 
     In general, calculations of strain and calorie burn based on the physiological data may be reset at cycle boundaries instead of calendar days. This can support the calculation of metrics that are more closely tied to the cadence of a user&#39;s activity, and more reflective of cycles of actual physiological activity. Similarly, a recovery score, or more broadly any estimation of the amount of sleep required for a specific night may be based on the total physiological exertion of the previous cycle, not calendar day, to give the most accurate physiological representation. Special handling may also be provided for users who periodically take off a monitoring device so that no data is available. In these cases, after time has elapsed beyond the end of a current predicted cycle end, a cycle may be closed and a new one will not be created. The cycle count may then be bootstrapped using any suitable technique, such as the bootstrapping described above, the next time a user puts the monitoring device on again. 
     While this overall approach can provide a general improvement over strict calendar-based schedules, the above techniques may still present difficulties. By way of an example, assume a collection of five timewise contiguous, non-overlapping cycles denoted in forward chronological order as C i−2 , C i−1 , C i , C i+1 , C i+2  and correspondingly calendar days D i−3 , D i−2 , D i−1 , D i , D i+1 , D i+2 . Let d(C i )=34. The C i  midpoint (halfway through the cycle in time) is exactly midnight—i.e. the start—of D i . Let durations d(C i+1 )=18 hours and d(C i−1 )=18 hours. 
     Continuing with this example, this yields the following situation:
         C i−2  has at least 17 hours of overlap on D i−3  and exactly 13 hours of overlap on D i−3 .   C i−1  has 11 hours of overlap on D i−2  and 7 hours of overlap on D i−1 .   C i  has 17 hours of overlap on D i−1  and 17 hours of overlap on D i .       

     As data streams in, according to the simplified algorithm and continuing with this example, it yields the following mapping:
         D i−3 →C i−2      D i−2 →C i−2      D i−1 →C i−1      D i →C i          

     This scenario may appear to yield a sub-optimal date mapping because for C i−1  there are only 7 hours of activity on D i−1  while the user&#39;s physiological day starts and contains mainly activity and time occurring on D i−2 . Thus, while the preceding can provide a general strategy for mapping physiological cycles to calendar days. This general strategy may be improved with any one or more of the following, more specific techniques. 
     In one aspect, the predicted cycle end may usefully be characterized as a range rather than a single point in time in order to accommodate various sleep/activity scenarios. For example, this can permit separate handling of different types of events such as: 1) activities that occur before the beginning of the PCE range, 2) activities that cross the beginning of the PCE range but not the end, 3) activities that cross the end of the PCE range but not the beginning, 4) activities that contain both the PCE range beginning and end, and 5) activities that occur after the PCE range end. For example, if a sleep is received/detected but it is short (e.g., &lt;2 hours) and only crossed the PCE range beginning, it may be considered to be a nap and not a significant sleep. This nuance may not be possible using a strict endpoint technique to predicted cycle ends. 
     In one aspect, the midpoint of a range for a predicted cycle end may be evaluated as a maximum of eighteen hours and forty-eight hours minus the duration of the immediately preceding cycle. The range may then be this midpoint plus or minus two hours. Stated mathematically: 
       PCE mid   :=d ( C   i )=max {18,48− d ( C   i−1 )}
 
       and 
       PCE range =(2 hours−PCE mid , 2 hours+PCE mid )
 
     As each cycle is closed based on an intersection of sleep with the range for the predicted cycle end, a new cycle may be created and a new predicted end midpoint and range can be determined as described above. 
     While the ranges described above appear to empirically capture a useful range of physiological cycles, it will be understood that other midpoints and ranges may also or instead be used. Furthermore, other techniques may also or instead be used to predict the cycle end, or to apply cycle ending rules. For example, a system or method may employ machine learning based on individual user behavior to learn individualized characteristics of significant sleep and physiological cycles. In a data-driven approach to PCE determination (based on either a range or a single point in time), a data structure can be bootstrapped using any of the above techniques over a series of nights, say twenty eight calendar days, and then for each subsequent cycle the system may find an average significant sleep onset time for a given user based on the previous nights. The system may then modulate this value based on learned specific behavior for, e.g., a given night of the week (e.g., nurse shift workers who start their night shifts on Wednesdays), a specific activity pattern (e.g., very strenuous workouts), or any other abnormalities or features in the days preceding it that might indicate a shift to PCE. This process may also be iterative in nature. That is, initial assumptions may be made as each new cycle is mapped to a calendar day, and then these mappings may be revisited, both in the short term (e.g., the most recent twenty four or forty eight hours) or the medium term (e.g., the preceding week or month), to see if other allocations of cycles might more conveniently or accurately map to calendar days. 
     In another aspect, the mapping may be adjusted to accommodate changes in time zones. In general, this may include adapting the length of days—for purposes of calendar assignments—to changes in the time zone. 
     The process may vary depending on whether a user is traveling westward or eastward. For example, a user of a physiological system may take a flight west from London to Los Angeles. Such a flight may take roughly 11 hours in absolute time. During this flight, however, a user will cross eight time zones from +0000 (GMT) in London to −8000 in Los Angeles. Thus, if the user took off at 8:00 p.m. (+0000) from London they will land at 11:00 p.m. (−8000) in Los Angeles. If one takes the implementation of the algorithm described above, a sleep on the plane that started at 9:00 p.m. might be categorized as a significant sleep and thus start a new cycle that will have a predicted cycle end (using a twenty four hour cycle estimate) at 9:00 p.m. in local Los Angeles time. Thus, when the user goes to sleep in Los Angeles following the flight, but wakes up before 9:00 p.m., the intervening sleep will not be considered significant and a new cycle will not start. 
     In order to avoid this outcome and adjust physiological cycles for westward travel, the process may detect a difference in time zones and then responsively adjust the predicted cycle end by subtracting this difference from the predicted cycle end (or range of predicted cycle ends). A difference in time zones may first be computed, based on, e.g., time zone data associated with the physiological data, GPS or other geographic data received from the monitoring device, or similar data received from another device (e.g., a smartphone, laptop, and the like) associated with the user of the physiological device. When data from the physiological monitoring device is uploaded in the new time zone, the predicted cycle end for the cycle may be adjusted for this time zone difference by subtracting the amount of hours of time zone change from the local time. For example, in the case above, the predicted cycle end may adjust from 9:00 p.m. to 1:00 p.m. For a twenty four hour cycle, e.g., any sleep occurring after this time may be treated as a significant sleep event rather than a nap or the like, while sleep ending before the predicted cycle range may be treated as a non-significant sleep event. A similar approach may be used with a range-based predicted cycle end as described above. In general, this technique has the effect of expanding the length of the cycle, and thus the corresponding expected physiological day, immediately following westward travel, which mimics the expected physiological experience of a human user adjusting to the new time zone. 
     In order to adjust physiological cycles for eastward travel, the process may detect a difference in time zones and then responsively adjust the predicted cycle end by adding this difference to the predicted cycle end (or range of predicted cycle ends). For example, for a user flying from Los Angeles (−0800) to London (+0000), the user may leave Los Angeles on December 1 at 5:00 p.m. (−0800). The flight may take a similar amount of time (e.g., eleven hours) and a predicted cycle end based on a sleep event on the plane (e.g., starting around 10:00 p.m. (−0800) may product an adjusted predicted cycle end of 2 p.m. (+0000) on December 2 in London. 
     More generally, when data from a physiological monitoring device is uploaded in a new time zone, the predicted cycle end (or range of predicted cycle ends) for the cycle may be adjusted by the time zone difference by adding hours for eastward travel and subtracting hours for westward travel. It will be appreciated that time zone offsets are generally signed and negative relative to Greenwich Mean Time (GMT). Thus, the same operation may be equivalently represented by calculating a cycle end offset for travel equal to the GMT offset for the destination minus the GMT offset for the point of origin. This cycle end offset may then be added to the local time, regardless of direction of travel, when calculating a value or range for a predicted cycle end. This technique has the general effect of adjusting the length of the physiological cycle to more closely correspond to the expected physiological experience of a human user during travel that spans multiple time zones. 
     For travel crossing the international date line, the adjustment may include adding a day to the date mapping when traveling in one direction, e.g., when going west from New York City to Hong Kong, and subtracting a day when going east from Hong Kong to New York City. 
     In another aspect, a user interface may permit a user to override automated detection of cycles and allocation of cycles to calendar days. For example, the system may automatically detect a sleep event that is determined to be a non-significant sleep event, i.e., a sleep event that does not begin a new physiological cycle. The user may manually override this determination, and force the system to ignore cycle detection rules and create a new cycle beginning with the sleep event. Similarly, the user may manually enter periods as sleep events even when no sleep, or very little sleep, was detected by the system. 
     Similarly, the user interface may permit (and the mapping algorithms may support) a user to explicitly identify or adjust the predicted cycle end, e.g., to accommodate known or anticipated, dramatic shifts to a sleeping schedule or activities. For example, if a user knows that they are going to pull an all-nighter, they can input this data into the user interface so that the system will not create a new cycle in response to a brief 2:00 a.m. nap. 
       FIG. 6  shows a mapping of physiological cycles to calendar days. In general physiological cycles  602  of irregular duration (e.g., varying length) may be mapped to calendar days  604  of regular duration (e.g., a fixed length of twenty four hours) using the techniques described above, with each physiological cycle  602  chronologically demarcated by significant sleep events  606  (or as illustrated, the beginning or end of significant sleep events  606 , which occur at a point in time rather than over an interval of sleep). Each physiological cycle  602  may be mapped to only one of the calendar days  604 . The resulting mapping may be stored in any suitable data structure. It will be noted that certain sleep events  608  may be excluded from the significant sleep events  606 , e.g., by applying the algorithms described herein, in order to preserve the mapping of each calendar day to one unique physiological cycle  602 . 
       FIG. 7  shows a user interface. In one aspect, a user interface  702  for viewing and managing physiological data may display physiological cycles  704  instead of, or in addition to, calendar days  706 . Each physiological cycle  704  may be characterized by one or more metrics. For example, a physiological cycle  704  may begin with a sleep score  708  and a recovery score  710  based on historical data for the user, e.g., as described above, and these scores may be displayed in the user interface  702  for viewing and interaction by a user. In addition to summarizing historical data for sleep and physical condition, these metrics can provide an indication of readiness for additional physical strain in the current physiological cycle  704 . In addition, each physiological cycle may, upon conclusion, receive a strain score  712  indicative of the physical strain experienced during that physiological cycle  704 , which may also be displayed within the user interface  702  in an interactive manner. In addition, one or more activity strain scores  714  for individual activities during the physiological cycle  704  may also be determined when those activities include increased strain. These high-strain activities may be automatically detected, manually identified, or some combination of these. A user may interact with the underlying data in this user interface in a number of ways, such as by scrolling chronologically through activities, days, and cycles, or by linking to underlying data for calculated scores, sleep or activity detections, and so forth. The user interface  702  may be rendered on a smart phone, tablet, laptop, desktop, or any other suitable user device. 
     In one aspect, this user interface  702  may advantageously facilitate a clear chronological presentation of physiological cycles without visual or organization interference by artificially rigid calendar days. 
       FIG. 8  shows a user interface. In one aspect, a user interface  802  for viewing and managing physiological data may display calendar days  806  instead of, or in addition to, physiological cycles. As described above, each calendar day  806  may be uniquely associated with one physiological cycle. As described above, in some instances, a particular physiological cycle  804  may span multiple calendar days  806 . In other instances, e.g., where a monitoring device has not been worn, a particular calendar day  810  may have no physiological cycle, or corresponding data, associated with that particular calendar day  810 . In general, a physiological cycle associated with one of the calendar days  806  may have an associated sleep score, recovery score, strain score, and activity strain score, any or all of which may be displayed within the user interface  802 , or accessed for particular days or other time periods using the user interface  802  as a navigational tool. The user interface  802  may be rendered on a smart phone, tablet, laptop, desktop, or any other suitable user device. 
     In one aspect, this user interface may use the mapping of cycles to days in a manner that advantageously facilitates the presentation of data, and the exploration of the data by a human user, through a daily calendar format that is both familiar and well-coordinated with a regular daily schedule. 
       FIG. 9  shows a method for allocating cycles to calendar days. And more generally, the method  900  may include a technique for managing physiological data. It will be understood that the method  900  may be performed using any of the devices and systems described herein. 
     As shown in step  902 , the method  900  may include storing continuous physiological data from a physiological monitoring device, such as any of the physiological monitoring devices described herein. Similarly, the physiological data may include any as described herein, e.g., a heart rate, heart rate variability, or other heart rate data, or other physiological data or physical data suitable for evaluating physical strain and recovery. For example, the physiological data may include photoplethysmography data for a wearer of the physiological monitoring device. The physiological data may be timestamped to support time-based processing and may also usefully include time zone and/or other location data to assist in determining a time zone in which physiological data has been obtained. 
     As shown in step  904 , the method  900  may include detecting sleep events based on patterns in the continuous physiological data indicative of sleep. The sleep events that are detected may include “significant” sleep events—e.g., a longer or deeper period of sleep, as opposed to a relatively quick nap or other period of inactivity that may not constitute actual sleep by a user. In this manner, this step  904  may also or instead include detecting significant sleep events based on the continuous physiological data, e.g., where detecting the significant sleep events includes locating patterns in the continuous physiological data indicative of sleep. 
     As shown in step  906 , the method  900  may include segmenting the continuous physiological data into cycles demarcated by significant sleep events. Detecting the significant sleep events may include generating a predicted end of one of the cycles after a first sleep event that is detected in the physiological data and categorizing a second sleep event as a significant sleep event if the second sleep event has an interval timewise intersecting with the predicted end of the cycle. Generating the predicted end of one of the cycles may include predicting an end of one of the cycles about one day after a beginning of the cycle. Also or instead, generating the predicted end of one of the cycles may include predicting an end of the cycle at a maximum of eighteen hours or forty-eight hours minus a duration of an immediately preceding the cycle. 
     As shown in step  908 , the method  900  may include detecting a time zone change of a number of hours by the physiological monitoring device during one of the cycles. As further shown in step  908 , in response to detecting a time zone change, the method  900  may include adjusting the predicted end of one of the cycles by subtracting the number of hours when the time zone change indicates a westward movement of the physiological monitoring device and adjusting the predicted end of one of the cycles by adding the number of hours when the time zone change indicates an eastward movement of the physiological monitoring device. Moreover, the method  900  may include adjusting the predicted end of one of the cycles by twenty four hours in response to a time zone change that crosses the international date line. 
     As shown in step  910 , the method  900  may include allocating the cycles to calendar days using a function that allocates each calendar day to exactly one of the cycles containing data acquired during that calendar day without requiring that each one of the cycles be allocated to exactly one calendar day, thereby providing a cycle allocation. By way of example, for a first cycle in the cycles, allocating the cycles to calendar days may include assigning the first cycle to the first day if the first cycle has at least twelve hours of overlap with a first day of the calendar days, and, if the first cycle does not have at least twelve hours of overlap with any day, allocating the cycles to calendar days may include assigning the first cycle to one of the calendar days having a greatest timewise overlap with the first cycle. In this manner, allocating the cycles may include assigning multiple consecutive days of the calendar days to the first cycle if the first cycle has more than twelve hours of overlap with each of the multiple consecutive days. And, if a second cycle immediately following the first cycle would be unassigned or is assigned to a date that it has a least amount of overlap with, allocating the cycles may include removing an assignment of a later of the multiple consecutive days from the first cycle and allocating the assignment of the later of the multiple consecutive days to the second cycle. 
     As shown in step  912 , the method  900  may include receiving a manual adjustment to a sleep event and/or a cycle. For example, the method  900  may include receiving a manual allocation of one of the cycles to one of the calendar days. By way of further example, the method  900  may include receiving a manual input of a time interval for a sleep event. 
     As shown in step  914 , the method  900  may include iteratively revising the allocations of the cycles to calendar days. That is, as each new cycle is mapped to a calendar day, these mappings may be revisited—e.g., both in the short term (such as the most recent twenty four or forty eight hours) or the medium term (such as the preceding week or month)—to see if other allocations of cycles might more conveniently or accurately map to calendar days. 
     As shown in step  916 , the method  900  may include storing allocation data, e.g., storing the cycle allocation as a day mapping data structure. 
     As shown in step  918 , the method  900  may include providing a user interface for interacting with physiological cycles. For example, the user interface may include a calendar displaying a number of calendar days, and the method  900  may further include providing a user control within the user interface for accessing one or more of the cycles having a corresponding calendar day within the number of calendar days in the calendar. Also or instead, the user interface may include a timeline showing endpoints for one or more cycles and one or more associated, concurrent calendar days. Such a timeline may illustrate metrics for sleep, recovery, day strain for one or more of the cycles, and so forth. 
     According to the foregoing, there is further described herein a system for managing physiological data having a server including a processor and a memory storing code. The code may configure the server to receive and store continuous physiological data from a physiological monitoring device, process the continuous physiological data by segmenting the continuous physiological data into cycles demarcated by significant sleep events, allocating the cycles to calendar days using a function that allocates each calendar day to exactly one of the cycles containing data acquired during that calendar day without requiring that each one of the cycles be allocated to exactly one calendar day, thereby providing a cycle allocation, to store the cycle allocation as a day mapping data structure, and to provide a user interface for interactive access by a user to the day mapping data structure. In one aspect, the user interface may include a calendar displaying a number of calendar days and a user control within the user interface for accessing one or more of the cycles having a corresponding calendar day within the number of calendar days in the calendar. In another aspect, the user interface may include a timeline showing endpoints for one or more cycles and one or more associated, concurrent calendar days. The timeline may also or instead illustrate metrics for sleep, recovery, and day strain for one or more of the cycles. 
     The above systems, devices, methods, processes, and the like may be realized in hardware, software, or any combination of these suitable for the control, data acquisition, and data processing described herein. This includes realization in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable devices or processing circuitry, along with internal and/or external memory. This may also, or instead, include one or more application specific integrated circuits, programmable gate arrays, programmable array logic components, or any other device or devices that may be configured to process electronic signals. It will further be appreciated that a realization of the processes or devices described above may include computer-executable code created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low-level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and software. 
     Thus, in one aspect, each method described above and combinations thereof may be embodied in computer executable code that, when executing on one or more computing devices, performs the steps thereof. In another aspect, the methods may be embodied in systems that perform the steps thereof, and may be distributed across devices in a number of ways, or all of the functionality may be integrated into a dedicated, standalone device or other hardware. The code may be stored in a non-transitory fashion in a computer memory, which may be a memory from which the program executes (such as random access memory associated with a processor), or a storage device such as a disk drive, flash memory or any other optical, electromagnetic, magnetic, infrared or other device or combination of devices. In another aspect, any of the systems and methods described above may be embodied in any suitable transmission or propagation medium carrying computer-executable code and/or any inputs or outputs from same. In another aspect, means for performing the steps associated with the processes described above may include any of the hardware and/or software described above. All such permutations and combinations are intended to fall within the scope of the present disclosure. 
     Embodiments disclosed herein may include computer program products comprising computer-executable code or computer-usable code that, when executing on one or more computing devices, performs any and/or all of the steps thereof. The code may be stored in a non-transitory fashion in a computer memory, which may be a memory from which the program executes (such as random-access memory associated with a processor), or a storage device such as a disk drive, flash memory or any other optical, electromagnetic, magnetic, infrared or other device or combination of devices. In another aspect, any of the systems and methods described above may be embodied in any suitable transmission or propagation medium carrying computer-executable code and/or any inputs or outputs from same. 
     It will be appreciated that the devices, systems, and methods described above are set forth by way of example and not of limitation. Absent an explicit indication to the contrary, the disclosed steps may be modified, supplemented, omitted, and/or re-ordered without departing from the scope of this disclosure. Numerous variations, additions, omissions, and other modifications will be apparent to one of ordinary skill in the art. In addition, the order or presentation of method steps in the description and drawings above is not intended to require this order of performing the recited steps unless a particular order is expressly required or otherwise clear from the context. 
     The method steps of the implementations described herein are intended to include any suitable method of causing such method steps to be performed, consistent with the patentability of the following claims, unless a different meaning is expressly provided or otherwise clear from the context. So, for example, performing the step of X includes any suitable method for causing another party such as a remote user, a remote processing resource (e.g., a server or cloud computer) or a machine to perform the step of X. Similarly, performing steps X, Y, and Z may include any method of directing or controlling any combination of such other individuals or resources to perform steps X, Y, and Z to obtain the benefit of such steps. Thus, method steps of the implementations described herein are intended to include any suitable method of causing one or more other parties or entities to perform the steps, consistent with the patentability of the following claims, unless a different meaning is expressly provided or otherwise clear from the context. Such parties or entities need not be under the direction or control of any other party or entity and need not be located within a particular jurisdiction. 
     Thus, while particular embodiments have been shown and described, it will be apparent to those skilled in the art that various changes and modifications in form and details may be made therein without departing from the spirit and scope of this disclosure and are intended to form a part of the invention as defined by the following claims.