Patent Publication Number: US-2022225929-A1

Title: Sleep scoring based on physiological information

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This is a continuation application of U.S. patent application Ser. No. 16/781,969, filed on Feb. 4, 2020, which is a continuation application of U.S. patent application Ser. No. 16/155,139, filed on Oct. 9, 2018, now U.S. Pat. No. 10,555,698, which is a continuation of U.S. patent application Ser. No. 15/456,494, filed on Mar. 11, 2017, now U.S. Pat. No. 10,111,615, all of which are entitled “SLEEP SCORING BASED ON PHYSIOLOGICAL INFORMATION,” which are hereby incorporated herein by reference in their entirety. 
    
    
     BACKGROUND 
     Field 
     The present disclosure generally relates to the field of computing devices with one or more sensors to collect physiological information of a user. 
     Description of Related Art 
     Computing devices, such as wearable computing devices, can incorporate or interact with one or more sensors for receiving physiological information of a user, used for making health-based assessments. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments are depicted in the accompanying drawings for illustrative purposes, and should in no way be interpreted as limiting the scope of the inventions. In addition, various features of different disclosed embodiments can be combined to form additional embodiments, which are part of this disclosure. Throughout the drawings, reference numbers may be reused to indicate correspondence between reference elements. 
         FIG. 1  is a block diagram illustrating an embodiment of a computing device in accordance with one or more embodiments. 
         FIG. 2A  shows perspective front and side views of a wearable computing device in accordance with one or more embodiments. 
         FIG. 2B  shows perspective back and side views of a wearable computing device in accordance with one or more embodiments. 
         FIG. 3  is a table of sleep quality assessment metrics and associated physiological data in accordance with one or more embodiments. 
         FIG. 4A  is a block diagram of a basis of determining a unified sleep score in accordance with one or more embodiments. 
         FIG. 4B  is a table of a basis of determining a unified sleep score in accordance with one or more embodiments. 
         FIG. 5  illustrates a networked relationship between a wearable computing device and an external computing device in accordance with one or more embodiments. 
         FIG. 6  illustrates a system of client devices and a server system for performing sleep quality assessment in accordance with one or more embodiments. 
         FIG. 7A  illustrates embodiments of wearable computing devices having displays for presenting a representation of a unified sleep score in accordance with one or more embodiments. 
         FIG. 7B  illustrates embodiments of wearable computing devices having touchscreen displays for presenting a representation of a unified sleep score in accordance with one or more embodiments. 
         FIG. 8  illustrates a flow diagram for a process of determining a sleep quality assessment in accordance with one or more embodiments. 
         FIG. 9  illustrates a flow diagram for a process of determining a sleep quality assessment in accordance with one or more embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The headings provided herein are for convenience only and do not necessarily affect the scope or meaning of the claimed invention. Like reference numbers and designations in the various drawings may or may not indicate like elements. 
     Although certain preferred embodiments and examples are disclosed below, inventive subject matter extends beyond the specifically disclosed embodiments to other alternative embodiments and/or uses and to modifications and equivalents thereof. Thus, the scope of the claims that may arise herefrom is not limited by any of the particular embodiments described below. For example, in any method or process disclosed herein, the acts or operations of the method or process may be performed in any suitable sequence and are not necessarily limited to any particular disclosed sequence. Various operations may be described as multiple discrete operations in turn, in a manner that may be helpful in understanding certain embodiments; however, the order of description should not be construed to imply that these operations are order dependent. Additionally, the structures, systems, and/or devices described herein may be embodied as integrated components or as separate components. For purposes of comparing various embodiments, certain aspects and advantages of these embodiments are described. Not necessarily all such aspects or advantages are achieved by any particular embodiment. Thus, for example, various embodiments may be carried out in a manner that achieves or optimizes one advantage or group of advantages as taught herein without necessarily achieving other aspects or advantages as may also be taught or suggested herein. 
     Overview 
     As computing devices become more ubiquitous and portable, many advantages are being seen in the field of health monitoring and diagnostics. Computing devices, particularly ones that can be worn or carried by a user, may include one or more sensors to detect physiological information about the user and/or the environment around the user. This information can be used to observe, detect or diagnose various health conditions outside of a traditional clinic or laboratory setting. For example, in the context of sleep therapy, a portable or wearable electronic device may be able to detect when a user moves at night, or monitor various physiological factors such as a heart rate. Additionally, a computing device may be able to record and interpret the detected information about the user and/or environment, to determine a health assessment. As in the previous example, a wearable electronic device may be able to record a relatively high frequency of movement while the user is sleeping, and generate an assessment that the user did not have a restful period of sleep. 
     Assessment of sleep quality may be achieved in various ways. For example, a patient may be monitored overnight in a sleep laboratory using equipment such as electrodes and an electroencephalogram (EEG) machine. In contrast, another diagnostic technique may involve a patient performing self-assessment of nightly quality of sleep, in a journal for example. However, factors such as discomfort and unreliability pose challenges to these established techniques for sleep quality assessment. As a result, a reliable, user-friendly approach to determining sleep quality is needed. For example, by determining sleep quality of a user wearing a portable electronic device from the comfort of home, reliable physiological information can be obtained and analyzed, without the use of extensive laboratory equipment. 
     Certain embodiments disclosed herein provide systems, devices and methods for assessing sleep quality of a user, based on collected physiological and/or environmental data. Some embodiments implement a computing device with one or more sensors for collecting physiological data from the user and/or environmental data from the surrounding environment. For example, a portable computing device worn on the wrist of a user may include a sensor to detect a heart rate of the user, as well as a sensor to detect an external temperature. Additionally, some embodiments implement a computing device or computing system configured to use the collected physiological and/or environmental information to determine values of one or more sleep quality metrics, and to use the determined values of sleep quality metrics to create a unified sleep quality score. For example, the same portable computing device worn on the wrist of the user may determine a value of a sleep restlessness metric, and determine a unified score for sleep quality of the user, using the sleep restlessness metric value. 
     As described herein, in some implementations, the present disclosure is related to biometric monitoring devices. The term “biometric monitoring device” is used in the present disclosure according to its broad and ordinary meaning, and may be used in various contexts herein to refer to any type of biometric tracking devices, personal health monitoring devices, portable monitoring devices, portable biometric monitoring devices, or the like. In some embodiments, biometric monitoring devices in accordance with the present disclosure may be wearable devices, such as may be designed to be worn (e.g., continuously) by a person (i.e., “user,” “wearer,” etc.). When worn, such biometric monitoring devices may be configured to gather data regarding activities performed by the wearer, or regarding the wearer&#39;s physiological state. Such data may include data representative of the ambient environment around the wearer or the wearer&#39;s interaction with the environment. For example, the data may comprise motion data regarding the wearer&#39;s movements, ambient light, ambient noise, air quality, etc., and/or physiological data obtained by measuring various physiological characteristics of the wearer, such as heart rate, perspiration levels, and the like. 
     In some cases, a biometric monitoring device may leverage other devices external to the biometric monitoring device, such as an external heart rate monitor in the form of an EKG sensor for obtaining heart rate data, or a GPS receiver in a smartphone used to obtain position data. In such cases, the biometric monitoring device may communicate with these external devices using wired or wireless communications connections. The concepts disclosed and discussed herein may be applied to both stand-alone biometric monitoring devices as well as biometric monitoring devices that leverage sensors or functionality provided in external devices, e.g., external sensors, sensors or functionality provided by smartphones, etc. 
     In some implementations, a method of assessing sleep quality of a user is performed at one or more electronic devices (e.g., a wearable computing device and/or a biometric monitoring device), where at least one electronic device has one or more processors, one or more physiological sensors, and memory for storing programs to be executed by the one or more processors. An electronic device may detect an initial attempt by a user to fall asleep, for example by detecting a lack of movement for a threshold period of time and/or other physiological or environmental factors such as ambient light conditions or body temperature. In some embodiments, physiological information of the user is collected or received, including at least one sleeping heart rate, such as an average heart rate since the attempt to fall asleep or a detected onset of sleep. Values for one or more sleep quality metrics may be determined, based at least in part on the collected physiological information and at least one wakeful resting heart rate of the user. For example, a wearable computing device may collect heart rate information of a user over the course of time between periods of sleep (e.g., while awake), and determine an average heart rate value for periods of relative inactivity or minimal exertion during the day (e.g., wakeful resting heart rate). Additionally, a unified score for sleep quality of the user may be determined, based at least in part on the determined sleep quality metric values. 
     In some implementations, the method includes presenting a representation of the unified score to the user, and/or generating this representation of the unified score. For example, a representation of the score is a number between 1 and 100, or a qualitative assessment of good, neutral or poor sleep quality. 
     In some implementations, the one or more sleep quality metrics includes a first set of sleep quality metrics associated with sleep quality of a plurality of users, and a second set of sleep quality metrics associated with historical sleep quality of the user. In some implementations, determining the unified score for sleep quality includes determining a respective metric-score for each of the one or more sleep quality metrics and applying a respective weighting for each of the one or more sleep quality metrics. 
     In some implementations, the method includes determining a wakeful resting heart rate of the user before detecting an attempt by the user to fall asleep. For example, an electronic device worn by a user periodically detects and records a heart rate (e.g., speed of a heart beat) of the user during one or more periods of time while the user is awake, and determines an average, median or another representative value for a wakeful resting heart rate. 
     In some implementations, collecting physiological information about the user includes collecting one or more sets of values associated with movement of the user, total sleep duration, total deep sleep duration, duration of wake time after sleep onset (WASO), total rapid-eye-movement (REM) sleep duration, total light sleep duration, breathing patterns of the user, breathing disturbances of the user and/or temperature of the user. 
     In some implementations, the method further includes collecting sleep quality feedback information from the user and/or receiving collected sleep quality feedback, and determining the unified score for sleep quality of the user based at least in part on the sleep quality feedback information. For example, a user may be prompted to answer one or more questions about perceived sleep quality, after waking up from a period of sleep. In this same example, the answers to the one or more questions may each be assigned a value, which is incorporated into the determination of the unified sleep score. 
     In some implementations, determining respective values for the one or more sleep quality metrics includes comparing the at least one sleeping heart rate of the user and the at least one resting heart rate of the user. For example, a particular user may have had an average wakeful resting heart rate of 75 beats per minute on a particular day, and an average sleeping heart rate of 45 beats per minute during the corresponding night. In this same example, if either value is relatively different from a historical average for the user, this may indicate a change in health or sleep quality. 
     In some implementations, the method includes detecting an onset of sleep by the user, and determining a duration of time from the attempt to fall asleep to the onset of sleep. For example, a wearable computing device may detect that the user has lain down in a darkened environment to attempt sleep at 11:05 pm, and has transitioned from wakefulness to either non-rapid eye movement (NREM) sleep or rapid-eye movement (REM) sleep at 11:15 pm. 
     In some implementations, the method includes detecting that the user is awake after the detected onset of sleep, and determining the respective values for one or more sleep quality metrics, in response to detecting that the user is awake. For example, detection of a user entering a state of wakefulness triggers determination of one or more sleep quality metrics, and may also trigger generation of the unified sleep quality score. 
     In some implementations, detecting the attempt by the user to fall asleep includes detecting contact between the device and the user. In certain embodiments, the electronic device is configured to be worn by the user. Although certain embodiments are disclosed herein in the context of a wearable electronic device worn by a user, it should be understood that detection of physiological activity and/or determination of sleep quality metrics and a unified sleep score in accordance with the present disclosure may be performed by any suitably configured electronic device, including but not limited to a computer, a server system, a smart phone, a tablet, a laptop computer, and an electronic device configured to be placed under a user during a period of sleep (e.g., a bed, pillow, blanket or mat). 
     In some implementations, a method of assessing sleep quality of a user is performed at one or more electronic devices, where at least one electronic device has one or more processors, one or more physiological sensors, and memory for storing programs to be executed by the one or more processors. An electronic device may detect an initial attempt by a user to fall asleep, and/or receive one or more signals indicating an attempt by a user to fall asleep, collect physiological information associated with the user and/or receive collected physiological information associated with the user, determine respective values for one or more sleep quality metrics, based at least in part on the collected physiological information, wherein the one or more sleep quality metrics includes a first set of sleep quality metrics associated with sleep quality of a plurality of users, and a second set of sleep quality metrics associated with historical sleep quality of the user, and determined a unified sleep quality score based at least in part on the values of the one or more sleep quality metrics. 
     In some implementations, the method includes determining the unified score for sleep quality includes using a first weighting for the first set of sleep quality metrics and a second weighting for the second set of sleep quality metrics. In some implementations, the first set of sleep quality metrics is associated with clinical sleep quality data of a demographic comparable to the user, and the second set of sleep quality metrics is associated with historical physiological information about the user for a minimum number of M days and a maximum number of N days. 
     In some implementations, collecting physiological information about the user includes collecting at least one sleeping heart rate and determining respective values for the one or more sleep quality metrics includes using at least one wakeful resting heart rate of the user. 
     The present disclosure includes certain embodiments of an electronic device comprising one or more physiological sensors, one or more processors, memory and control circuitry configured to detect an attempt by the user to fall asleep, collect physiological information about the user, including at least one sleeping heart rate, determine respective values for one or more sleep quality metrics, using the collected physiological information and at least one wakeful resting heart rate of the user and determine a unified score for sleep quality of the user, using the respective values of the one or more sleep quality metrics. Additionally, the control circuitry may be configured to perform any of the methods described herein. 
     The present disclosure includes certain embodiments of a non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device with one or more physiological sensors, cause the device to detect an attempt by the user to fall asleep, collect physiological information about the user, including at least one sleeping heart rate, determine respective values for one or more sleep quality metrics, using the collected physiological information and at least one wakeful resting heart rate of the user and determine a unified score for sleep quality of the user, using the respective values of the one or more sleep quality metrics. Additionally, the non-transitory computer readable storage medium may include instructions to perform any of the methods described herein. 
     The present disclosure includes certain embodiments of a method of performing sleep quality assessment at a sleep quality assessment server comprising one or more processors, and memory for storing programs to be executed by the one or more processors for receiving physiological information about a user, including at least one sleeping heart rate and at least one wakeful resting heart rate of the user, determining respective values for one or more sleep quality metrics, using the collected physiological information and determining a unified score for sleep quality of the user, using the respective values of the one or more sleep quality metrics. The method may further include transmitting the unified sleep score and/or the determined values of the sleep quality metrics to a wearable computing device and/or an external computing device, such as a smart phone. Additionally, the sleep quality assessment server may be configured to perform any of the methods described herein, with respect to an electronic device and/or a server. 
     In some embodiments, the method includes transmitting the determined values for one or more sleep quality metrics to an electronic device. In some embodiments, the electronic device is a wearable computing device and/or the electronic device is an external computing device. In some embodiments, the electronic device has a display configured to present the unified score and/or the determined values for one or more sleep quality metrics. In some implementations, determining the unified score for sleep quality includes determining a respective metric-score for each of the one or more sleep quality metrics and using a respective weighting for each of the one or more sleep quality metrics. In some implementations, the method includes receiving sleep quality feedback information collected from the user (e.g., at a wearable computing device and/or at an external computing device), and determining the unified score for sleep quality of the user by additionally using the collected sleep quality feedback information. In some implementations, determining respective values for the one or more sleep quality metrics includes comparing the at least one sleeping heart rate of the user and the at least one wakeful resting heart rate of the user. In some implementations, the method includes receiving a trigger to determine the unified sleep quality score (e.g., receiving notice that the user is awake or has requested the score), and determining the respective values for one or more sleep quality metrics, in response to receiving the trigger to determine the unified sleep quality score. In some implementations, the method includes updating a profiles database with the received physiological information and/or the determined values of sleep quality metrics and/or the unified sleep score. In some implementations, determining respective values for one or more sleep quality metrics (e.g., of the user) includes retrieving values for one or more sleep quality metrics corresponding to a plurality of users (e.g., other users of a similar demographic to the user). 
     The present disclosure includes certain embodiments of a method of performing sleep quality assessment at a sleep quality assessment server comprising one or more processors, and memory for storing programs to be executed by the one or more processors for receiving physiological information about a user, determining respective values for one or more sleep quality metrics using the collected physiological information, wherein the one or more sleep quality metrics includes a first set of sleep quality metrics associated with sleep quality of a plurality of users, and a second set of sleep quality metrics associated with historical sleep quality of the user and determining a unified score for sleep quality of the user, using the respective values of the one or more sleep quality metrics. The method may further include transmitting the unified sleep score and/or the determined values of the sleep quality metrics to a wearable computing device and/or an external computing device, such as a smart phone. Additionally, the sleep quality assessment server may be configured to perform any of the methods described herein, with respect to an electronic device and/or a server. 
     Portable Computing Devices 
     Systems, devices and/or methods/processes in accordance with the present disclosure may comprise, or be implemented in connection with, a biometric monitoring device. Embodiments of the present disclosure may provide biometric monitoring devices configured to collect physiological data of a user from one or more physiological metric sensors and/or environmental data from one or more environmental sensors. Embodiments of the present disclosure may further provide biometric monitoring devices configured to analyze and interpret the collected data and/or communicate with another computing device to analyze and interpret the collected data. It is to be understood that while the concepts and discussion included herein are presented in the context of biometric monitoring devices, these concepts may also be applied in other contexts as well if the appropriate hardware is available. For example, some or all of the relevant sensor functionality may be incorporated in one or more external computing devices (e.g., smartphone) or computing systems (e.g., a server) communicatively coupled to the biometric monitoring device. 
       FIG. 1  is a block diagram illustrating an embodiment of a computing device  100  in accordance with one or more embodiments disclosed herein. In certain embodiments, the computing device  100  may be worn by a user  10 , such as with respect to embodiments in which the computing device  100  is a wearable biometric or physiological monitoring device. For example, the computing device  100  may comprise a wearable biometric monitoring device configured to gather data regarding activities performed by the wearer, or regarding the wearer&#39;s physiological state. Such data may include data representative of the ambient environment around the wearer or the wearer&#39;s interaction with the environment. For example, the data may comprise motion data regarding the wearer&#39;s movements, ambient light, ambient noise, air quality, etc., and/or physiological data obtained by measuring various physiological characteristics of the wearer, such as heart rate, body temperature, blood oxygen levels, perspiration levels, and the like. Although certain embodiments are disclosed herein in the context of wearable biometric monitoring devices, it should be understood that biometric/physiological monitoring and health assessment principles and features disclosed herein may be applicable with respect to any suitable or desirable type of computing device or combination of computing devices, whether wearable or not. 
     The computing device  100  may include one or more audio and/or visual feedback modules  130 , such as electronic touchscreen display units, light-emitting diode (LED) display units, audio speakers, LED lights or buzzers. In certain embodiments, the one or more audio and/or visual feedback modules  130  may be associated with the front side of the computing device  100 . For example, in wearable embodiments of the computing device  100 , an electronic display may be configured to be externally presented to a user viewing the computing device  100 . 
     The computing device  100  includes control circuitry  110 . Although certain modules and/or components are illustrated as part of the control circuitry  110  in the diagram of  FIG. 1 , it should be understood that control circuitry associated with the computing device  100  and/or other components or devices in accordance with the present disclosure may include additional components and/or circuitry, such as one or more of the additional illustrated components of  FIG. 1 . Furthermore, in certain embodiments, one or more of the illustrated components of the control circuitry  110  may be omitted and/or different than that shown in  FIG. 1  and described in association therewith. The term “control circuitry” is used herein according to its broad and/ordinary meaning, and may include any combination of software and/or hardware elements, devices or features, which may be implemented in connection with operation of the computing device  100 . Furthermore, the term “control circuitry” may be used substantially interchangeably in certain contexts herein with one or more of the terms “controller,” “integrated circuit,” “IC,” “application-specific integrated circuit,” “ASIC,” “controller chip,” or the like. 
     The control circuitry  110  may comprise one or more processors, data storage devices, and/or electrical connections. For example, the control circuitry  110  may comprise one or more processors configured to execute operational code for the computing device  100 , such as firmware or the like, wherein such code may be stored in one or more data storage devices of the computing device  100 . In one embodiment, the control circuitry  110  is implemented on an SoC (system on a chip), though those skilled in the art will recognize that other hardware/firmware implementations are possible. 
     The control circuitry  110  may comprise a sleep quality assessment module  113 . The sleep quality assessment module  113  may comprise one or more hardware and/or software components or features configured to make an assessment of sleep quality of a user, optionally using inputs from one or more environmental sensors  155  (e.g., ambient light sensor) and information from the physiological metric module  141 . In certain embodiments, the sleep assessment module  111  includes a sleep score determination module  113  configured to determine a unified sleep quality score, using information accumulated by the sleep assessment module  111 , such as the values of one or more physiological metrics determined by the physiological metric calculation module  142  of the physiological metric module  141 . In certain embodiments, the physiological metric module  141  is optionally in communication with one or more internal physiological sensors  140  embedded or integrated in the biometric monitoring device  100 . In certain embodiments, the physiological metric module  141  is optionally in communication with one or more external physiological sensors  145  not embedded or integrated in the biometric monitoring device  100  (e.g., an electrode, or sensor integrated in another electronic device). Examples of internal physiological sensors  140  and external physiological sensors  145  include, but are not limited to, sensors for measuring body temperature, heart rate, blood oxygen level and movement. 
     The computing device may further comprise one or more data storage modules  151 , which may include any suitable or desirable type of data storage, such as solid-state memory, which may be volatile or non-volatile. Solid-state memory of the computing device  100  may comprise any of a wide variety of technologies, such as flash integrated circuits, Phase Change Memory (PC-RAM or PRAM), Programmable Metallization Cell RAM (PMC-RAM or PMCm), Ovonic Unified Memory (OUM), Resistance RAM (RRAM), NAND memory, NOR memory, EEPROM, Ferroelectric Memory (FeRAM), MRAM, or other discrete NVM (non-volatile solid-state memory) chips. The data storage  151  may be used to store system data, such as operating system data and/or system configurations or parameters. The computing device  100  may further comprise data storage utilized as a buffer and/or cash memory for operational use by the control circuitry  110 . 
     Data storage modules  151  may include various sub-modules, including, but not limited to one or more of a sleep detection module for detecting an attempt or onset of sleep by the user  10 , an information collection module for managing the collection of physiological and/or environmental data relevant to a sleep quality assessment, a sleep quality metric calculation module for determining values of one or more sleep quality metrics as described in the present disclosure, a unified score determination module for determining a representation of a unified sleep quality score as described in the present disclosure, a presentation module for managing presentation of sleep quality assessment information to user  10 , a heart rate determination module for determining values and patterns of one or more types of heart rates of user  10 , a feedback management module for collecting and interpreting sleep quality feedback from user  10 . 
     The computing device  100  further comprises power storage  153 , which may comprise a rechargeable battery, one or more capacitors, or other charge-holding device(s). The power stored by the power storage module  153  maybe utilized by the control circuitry  110  for operation of the computing device  100 , such as for powering the touchscreen display  130 . The power storage module  153  may receive power over the host interface  176  or through other means. 
     The computing device  100  may comprise one or more environmental sensors  155 . Examples of such environmental sensors  155  include, but are not limited to sensors for measuring ambient light, external (non-body) temperature, altitude and global-positioning system (GPS) data. 
     The computing device  100  may further comprise one or more connectivity components  170 , which may include, for example, a wireless transceiver  172 . The wireless transceiver  172  may be communicatively coupled to one or more antenna devices  195 , which may be configured to wirelessly transmit/receive data and/or power signals to/from the computing device using, but not limited to peer-to-peer, WLAN or cellular communications. For example, the wireless transceiver  172  maybe utilized to communicate data and/or power between the computing device  100  and an external host system (not shown), which may be configured to interface with the computing device  100 . In certain embodiments, the computing device  100  may comprise additional host interface circuitry and/or components  176 , such as wired interface components for communicatively coupling with a host device or system to receive data and/or power therefrom and/or transmit data thereto. 
     The connectivity circuitry  170  may further comprise user interface components  174  for receiving user input. For example, the user interface  174  may be associated with one or more audio/visual feedback modules  130 , wherein the touchscreen display is configured to receive user input from user contact therewith. The user interface module  174  may further comprise one or more buttons or other input components or features. 
     The connectivity circuitry  170  may further comprise the host interface  176 , which may be, for example, an interface for communicating with a host device or system (not shown) over a wired or wireless connection. The host interface  176  may be associated with any suitable or desirable communication protocol and/or physical connector, such as Universal Serial Bus (USB), Micro-USB, WiFi, Bluetooth, FireWire, PCIe, or the like. For wireless connections, the host interface  176  may be incorporated with the wireless transceiver  172 . 
     Although certain functional modules and components are illustrated and described herein, it should be understood that authentication management functionality in accordance with the present disclosure may be implemented using a number of different approaches. For example, in some implementations the control circuitry  110  may comprise one or more processors controlled by computer-executable instructions stored in memory so as to provide functionality such as is described herein. In other implementations, such functionality may be provided in the form of one or more specially-designed electrical circuits. In some implementations, such functionality may be provided by one or more processors controlled by computer-executable instructions stored in a memory coupled with one or more specially-designed electrical circuits. Various examples of hardware that may be used to implement the concepts outlined herein include, but are not limited to, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and general-purpose microprocessors coupled with memory that stores executable instructions for controlling the general-purpose microprocessors. 
     Wearable Computing Devices 
     In some implementations, a portable computing device, such as computing device  100  of  FIG. 1 , may be designed so that it can be inserted into a wearable case or into one or more of multiple different wearable cases (e.g., a wristband case, a belt-clip case, a pendant case, a case configured to be attached to a piece of exercise equipment such as a bicycle, etc.). In other implementations, a portable computing device may be designed to be worn in limited manners, such as a computing device that is integrated into a wristband in a non-removable manner, and may be intended to be worn specifically on a person&#39;s wrist (or perhaps ankle). Irrespective of configuration, wearable computing devices having one or more physiological and/or environmental sensors may be configured to collect physiological and/or environmental data in accordance with various embodiments disclosed herein. Wearable computing devices may also be configured to analyze and interpret collected physiological and/or environmental data to perform a sleep quality assessment of a user (e.g., wearer) of the computing device, or may be configured to communicate with another computing device or server that performs the sleep quality assessment. 
     Wearable computing devices according to embodiments and implementations described herein may have shapes and sizes adapted for coupling to (e.g., secured to, worn, borne by, etc.) the body or clothing of a user. An example of a wearable computing device  201  is shown in  FIG. 2A .  FIG. 2A  shows perspective front and side views of the wearable computing device  201 . The wearable computing device  201  includes both a computing device  200 , as well as a band portion  207 . In certain embodiments, the band portion  207  includes first and second portions that may be connected by a clasp portion  209 . The computing device portion  200  may be insertable, and may have any suitable or desirable dimensions. Wearable computing devices may generally be relatively small in size so as to be unobtrusive for the wearer, and therefore, an optional touchscreen display  230  may be relatively small in size relative to certain other computing devices. The computing device  200  may be designed to be able to be worn without discomfort for long periods of time and to not interfere with normal daily activity or with sleep. 
     The electronic display  230  may comprise any type of electronic display known in the art. For example, the display  230  may be a liquid crystal display (LCD) or organic light emitting diode (OLED) display, such as a transmissive LCD or OLED display. The electronic display  230  may be configured to provide brightness, contrast, and/or color saturation features according to display settings maintained by control circuitry and/or other internal components/circuitry of the computing device  200 . 
     The touchscreen  230  may be a capacitive touchscreen, such as a surface capacitive touchscreen or a projective capacitive touch screen, which may be configured to respond to contact with electrical charge-holding members or tools, such as a human finger. 
     Wearable computing devices, such as biometric monitoring devices, in accordance with the present disclosure may incorporate one or more existing functional components or modules designed for determining one or more physiological metrics associated with a user (e.g., wearer) of the device, such as a heart rate sensor (e.g., photoplethysmograph sensor), body temperature sensor, environment temperature sensor or the like. Such components/modules may be disposed or associated with an underside/backside of the biometric monitoring device, and may be in contact or substantially in contact with human skin when the biometric monitoring device is worn by a user. For example, where the biometric monitoring device is worn on the user&#39;s wrist, the physiological metric component(s)/module(s) may be associated with an underside/backside of the device substantially opposite the display and touching the arm of the user. 
     In one embodiment, the physiological and/or environmental sensors (sources and/or detectors) may be disposed on an interior or skin-side of the wearable computing device (i.e., a side of the computing device that contacts, touches, and/or faces the skin of the user (hereinafter “skin-side”). In another embodiment, the physiological and/or environmental sensors may be disposed on one or more sides of the device, including the skin-side and one or more sides of the device that face or are exposed to the ambient environment (environmental side). 
       FIG. 2B  is a perspective back and side view of the wearable biometric monitoring device  201  of  FIG. 2A . The wearable biometric monitoring device  201  comprises a band portion  207 , which may be configured to be latched or secured about a user&#39;s arm or other appendage via a securement mechanism of any suitable or desirable type. For example, the band  207  may be secured using a hook and loop clasp component  209 . In certain embodiments, the band  207  is designed with shape memory to promote wrapping around the user&#39;s arm. 
     The wearable biometric monitoring device  201  includes a biometric monitoring device component  200 , which may be at least partially secured to the band  207 . The view of  FIG. 2B  shows a backside  206  (also referred to herein as the “underside”) of the biometric monitoring device  200 , which may generally face and/or contact skin or clothing associated with the user&#39;s arm, for example. The terms “backside” and “underside” are used herein according to their broad and ordinary meaning, and may be used in certain contexts to refer to a side, panel, region, component, portion and/or surface of a biometric monitoring device that is positioned and/or disposed substantially opposite to a user display screen, whether exposed externally of the device, or at least partially internal to an electronics package or housing of the device. 
     The wearable biometric monitoring device  201  may include one or more buttons  203 , which may provide a mechanism for user input. The wearable biometric monitoring device  201  may further comprise a device housing, which may comprise one or more of steel, aluminum, plastic, and/or other rigid structure. The housing  208  may serve to protect the biometric monitoring device  200  and/or internal electronics/components associated therewith from physical damage and/or debris. In certain embodiments, the housing  208  is at least partially waterproof. 
     The backside  206  of the biometric monitoring device  200  may have an optical physiological metric sensor  243  associated therewith, which may comprise one or more sensor components, such as one or more light sources  240  and/or light detectors  245 , the collection of which may represent an example of an internal physiological metric sensor  140  of  FIG. 1 . In certain embodiments, the optical physiological metric sensor  243  comprises a protrusion form protruding from the back surface of the biometric monitoring device  200 . The sensor components may be used to determine one or more physiological metrics of a user wearing the wearable biometric monitoring device  201 . For example, the optical physiological sensor components associated with the sensor  243  may be configured to provide readings used to determine heart rate (e.g., in beats-per-minute (BPM)), blood oxygenation (e.g., SpO 2 ), blood pressure, or other metric. In certain embodiments, the biometric monitoring device  200  further includes an electrical charger mating recess  207 . 
     Although the sensor  243  is illustrated as comprising a protrusion from certain figures herein, it should be understood that backside sensor modules in accordance with the present disclosure may or may not be associated with a protrusion form. In certain embodiments, the protrusion form on the backside of the device may be designed to engage the skin of the user with more force than the surrounding device body. In certain embodiments, an optical window or light-transmissive structure may be incorporated in a portion of the protrusion  243 . The light emitter(s)  240  and/or detector(s)  245  of the sensor module  243  may be disposed or arranged in the protrusion  243  near the window or light-transmissive structure. As such, when attached to the user&#39;s body, the window portion of the protrusion  243  of the biometric monitoring device  200  may engage the user&#39;s skin with more force than the surrounding device body, thereby providing a more secure physical coupling between the user&#39;s skin and the optical window. That is, the protrusion  243  may cause sustained contact between the biometric monitoring device and the user&#39;s skin that may reduce the amount of stray light measured by the photodetector  245 , decrease relative motion between the biometric monitoring device  200  and the user, and/or provide improved local pressure to the user&#39;s skin, some or all of which may increase the quality of the cardiac signal of interest generated by the sensor module. Notably, the protrusion  243  may contain other sensors that benefit from close proximity and/or secure contact to the user&#39;s skin. These may be included in addition to or in lieu of a heart rate sensor and may include sensors such as a skin temperature sensor (e.g., noncontact thermopile that utilizes the optical window or thermistor joined with thermal epoxy to the outer surface of the protrusion), pulse oximeter, blood pressure sensor, EMG, or galvanic skin response (GSR) sensor. 
     Collecting Physiological and/or Environmental Information 
     The wearable computing device  201  may be configured to collect one or more types of physiological and/or environmental data from embedded sensors and/or external devices and communicate or relay such information to other devices, including devices capable of serving as an Internet-accessible data sources, thus permitting the collected data to be viewed, for example, using a web browser or network-based application. For example, while the user is wearing the wearable computing device  201 , the wearable computing device  201  may calculate and store the user&#39;s step count using one or more biometric sensors. The wearable computing device  201  may then transmit data representative of the user&#39;s step count to an account on a web service, computer, mobile phone, or health station where the data may be stored, processed, and visualized by the user. Indeed, the wearable computing device  201  may measure or calculate a plurality of other physiological metrics in addition to, or in place of, the user&#39;s step count. These include, but are not limited to, energy expenditure, e.g., calorie burn, floors climbed and/or descended, heart rate, heart rate variability, heart rate recovery, location and/or heading, e.g., through GPS or a similar system, elevation, ambulatory speed and/or distance traveled, swimming lap count, swimming stroke type and count detected, bicycle distance and/or speed, blood pressure, blood glucose, skin conduction, skin and/or body temperature, muscle state measured via electromyography, brain activity as measured by electroencephalography, weight, body fat, caloric intake, nutritional intake from food, medication intake, sleep periods, e.g., clock time, sleep phases, sleep quality and/or duration, pH levels, hydration levels, respiration rate, and other physiological metrics. 
     The wearable computing device  201  may also measure or calculate metrics related to the environment around the user such as barometric pressure, weather conditions (e.g., temperature, humidity, pollen count, air quality, rain/snow conditions, wind speed), light exposure (e.g., ambient light, UV light exposure, time and/or duration spent in darkness), noise exposure, radiation exposure, and magnetic field. Furthermore, the wearable computing device  201  or the system collating the data streams from the wearable computing device  201  may calculate metrics derived from such data. For example, the device or system may calculate the user&#39;s stress and/or relaxation levels through a combination of heart rate variability, skin conduction, noise pollution, and body temperature. Similarly, the wearable computing device  201  or the system collating the data streams from the wearable computing device  201  may determine values corresponding to sleep quality metrics derived from such data. For example, the device or system may calculate the user&#39;s restlessness and/or quality of rapid-eye movement (REM) sleep through a combination of heart rate variability, blood oxygen level, sleep duration, and body temperature. In another example, the wearable computing device  201  may determine the efficacy of a medical intervention, e.g., medication, through the combination of medication intake, sleep data, and/or activity data. In yet another example, the biometric monitoring device or system may determine the efficacy of an allergy medication through the combination of pollen data, medication intake, sleep and/or activity data. These examples are provided for illustration only and are not intended to be limiting or exhaustive. Further embodiments and implementations of sensor devices may be found in U.S. Pat. No. 9,167,991, titled “Portable Biometric Monitoring Devices and Methods of Operating Same” filed Jun. 8, 2011, which is hereby incorporated herein by reference in its entirety. 
     Sleep Quality Metrics 
     As described above, a reliable, yet user-friendly approach is desired in the field of sleep therapy and sleep quality assessment.  FIG. 3  is a table  300  of sleep quality assessment metrics and associated physiological data in accordance with one or more embodiments of the present disclosure. Table  300  includes a metric column  302  and input column  304  as well as three rows of types of sleep quality metrics, namely user goal-driven metrics  306 , user-normalized metrics  308  and population-normalized metrics  310 . In some embodiments, inputs in input column  304  include directly detected physiological and/or environmental sensor readings (e.g., instantaneous heart rate), while in some embodiments, inputs in input column  304  include intermediate parameters derived from directly detected physiological and/or environmental sensor readings (e.g., average heart rate over a period of sleep). Further embodiments and implementations of collecting and interpreting physiological and/or environmental sensor readings for sleep quality assessment may be found in U.S. patent application Ser. No. 15/438,643, titled “Methods and Systems for Labeling Sleep States” filed Feb. 21, 2017, which is hereby incorporated herein by reference in its entirety. 
     The various sleep quality metrics conveyed in table  300  are each associated with a distinct diagnostic basis for sleep quality. For example, one aspect of good sleep health is having an adequate total duration of sleep corresponding to the age, gender and overall health of a respective user. The first type of sleep quality metric that may be used to determine a unified sleep score, is goal-driven  306 . Goal-driven metrics  306  may require input from a user to set one or more sleep goals, and may require feedback from a user regarding perceived quality of sleep. Goal-driven metrics  306 , include, but are not limited to, a total sleep time metric and a sleep consistency metric. 
     Determining a value for the total sleep time metric may rely on one or more inputs, including, but not limited to a user-specified minimum target duration of sleep goal, a maximum target duration of sleep goal and/or a target range of sleep goal. For example, a biometric monitoring device performing a sleep quality assessment, may prompt a user to enter a goal for achieving a duration of sleep for a given night (e.g., sleep for at least 7 hours tonight), or to enter a goal for achieving a duration of sleep every night (e.g., sleep for 7-8 hours every night). Determining a value for the total sleep time metric may also rely on a determined value of total sleep duration achieved by the user for a respective period of sleep. For example, a wearable computing device may detect that a user fell asleep at 11 PM on Monday night, and woke up at 6 AM on Tuesday morning, for a total sleep duration of 7 hours. In some embodiments, the onset of sleep and the onset of wakefulness are detected by one or more physiological sensors. Determining a value for the total sleep time metric may use the user-specified sleep goal along with the detected total sleep duration for a respective period of sleep (e.g., for a given night), and generate a value corresponding to a level of achievement for reaching the user-specified goal. For example, if the user aimed to sleep for at least 7 hours, and had a total sleep duration for 7.5 hours, the total sleep time metric would have a maximum value (e.g., 10 out of 10 or 100%) for a given range of possible values. 
     Another of the possible goal-driven metrics  306 , is sleep consistency. In some implementations, a value of a sleep consistency metric relies on feedback obtained from a user after waking from a period of sleep. For example, a biometric monitoring device may detect that a user has awoken from a night of sleep and prompt the user to provide a self-assessment of perceived quality of sleep or feeling of restfulness. This feedback may be in a numerical format (e.g., 7 out of 10) or a subjective form, which is converted into a numerical value (e.g., good=1, neutral=0, bad=−1). 
     The second type of sleep quality metric that may be used to determine a unified sleep score, is user-normalized  308 . User-normalized metrics  308  are specific to a respective user undergoing sleep assessment, and rely on historical physiological information about the user. In certain embodiments, determining a respective value for a respective user-normalized metric  308  may require at least M days worth of detected physiological and/or environmental data (e.g., at least 7 days). In some embodiments, determining a respective value of a respective user-normalized metric  308  may use up to a maximum N days worth of the most recently detected physiological and/or environmental data (e.g., at most the last 30 days). User-normalized metrics  308  may include, but not be limited to, a restlessness metric, a long wakes metric and a heart rate metric. 
     In some implementations, a value of a restlessness metric is determined using a value for detected movement of a user during a period of sleep. A degree of movement may be based on a duration of detected movement while a user is asleep. For example, a computing device worn by a user while asleep detects that the user remained nearly motionless for 6 out of 7 hours of sleep, which may indicate a low degree of movement during the period of sleep. A degree of movement may be based on a severity of detected movement while a user is asleep. For example, if a user has a bad dream and moves with great force or great speed, this movement may indicate a high degree of movement during the period of sleep. A value of a restlessness metric may also rely on a value of detected total sleep duration. For example, if 15 minutes of movement were detected during an 8 hour period of sleep, this would indicate less overall restlessness than detecting 15 minutes of movement during a 5 hour period of sleep. A value or weighting of the value of the restlessness metric may also involve comparing a current assessment of restlessness to the user&#39;s historical values of the restlessness metric. For example, if the user typically has a relatively calm night of sleep with little movement, a night with a high restlessness score has a relatively high standard deviation from the normal values and may be weighted more negatively than if the user typically has restless sleep. Similarly, in this same example, if the user typically has a restless night of sleep, a night with a low restlessness score may be weighted more positively than for a user who typically has calm nights of sleep. 
     In some implementations, a value of a long wakes metric is determined using a value representing a detected period of wakefulness or total duration of time between periods of sleep. For example, if a user gets up out of bed multiple times in a given night, the value of the long wakes metric may be low, indicating that the user did not have a long, continuous period of sleep. Similarly to the restlessness metric, determining the value or a weighting of the value of the long wakes metric may involve comparing the value of the determined long wakes metric to historical values of the long wakes metric for a respective user. 
     In some implementations, a value of a heart rate metric is determined using one or more heart rate parameters. Determining a value for the heart rate metric may include using a value representing the average heart rate of a user during a period of sleep (e.g., 45 beats per minute). For example, if the user had an average sleeping heart rate above a particular threshold (e.g., 60 beats per minute), this may result in a low value for the heart rate metric. The average sleeping heart rate of a user may also be compared to historical average sleeping heart rates of the user for a number of days, to assess if the currently determined average sleeping heart rate is abnormally high or low for the user. 
     In some implementations, the value of a heart rate metric includes assessment of a wakeful resting heart rate of the user during a long duration of wakefulness before detecting that the user is attempting to sleep (e.g., during the day before a night&#39;s sleep). For example, a user may have had an average resting heart rate of 55 beats per minute during the day, and an average sleeping heart rate of 53 beats per minute. This unusually small difference between the average resting heart rate of the user during the day and the average sleeping heart rate of the user while asleep may result in a low value for the heart rate metric, indicating that the user may not have had a good night of sleep. In some implementations, detection of a sleeping heart rate (e.g., average, median, or instantaneous) value above a wakeful resting heart rate value is an indication of poor quality sleep. As a result, the heart rate metric may be assigned a low value and/or the weight of the heart rate metric value in determining a population-normalized score and/or the unified sleep score may be increased. 
     The use of a wakeful resting heart rate of the user may also indicate a mental or physical health condition (e.g., a cold, or anxiety), which may affect detected and/or perceived sleep quality. For example, if a user has a relatively high resting heart rate for several days or weeks in a row, this may correspond to a relatively high sleeping heart rate for the same several days or weeks. As a result, weighting of the value of an average sleeping heart rate may be lessened to compensate for this change in physical and/or mental health of the user. While the examples of a detected heart rate have referred to averaged values, in some implementations another statistical basis may be used to assess a respective heart rate of a user. For example, an entire heart rate pattern may be analyzed for a given period of time (e.g., total duration of sleep). A median heart rate value may be used instead of an average heart rate value for a given heart rate parameter. Extreme values for heart rates over a period of time may also be filtered out before performing a statistical operation to determine a representative value for a respective type of heart rate. 
     The third type of sleep quality metric that may be used to determine a unified sleep score, is population-normalized  310 . Population-normalized metrics  308  are sleep quality metrics assessed with respect to a plurality of users. In certain embodiments, determining a respective value for a respective population-normalized metric  310  includes comparing determined values for a user to those of other users of a similar demographic (e.g., same gender, same age range, same occupation). Population-normalized metrics  310  may include, but not be limited to, a deep sleep metric, a rapid-eye movement (REM) metric, a wake after sleep onset (WASO) metric and a breathing disturbances metric. Population-normalized metrics may be associated with a plurality of users of the sleep quality assessment system described herein, and/or with clinical data obtained from sleep therapy resources such as sleep laboratory readings of various patients. 
     In some implementations, a value of a deep sleep metric is determined using a determined value of a duration of time that a user experiences deep sleep during a total duration of sleep. In some embodiments, a total duration of non-rapid eye movement (NREM) sleep is used for determining the value of the deep sleep metric, while in some embodiments, a total duration of deep sleep is a subset of time during a total duration of NREM sleep. For example, a total duration of deep sleep is determined to include time when brain waves of a user have a frequency of less than 1 Hz. Deep sleep is physiologically linked to the consolidation of new memories, and physical and mental recovery. As a result, a relatively low value of duration of deep sleep, as compared to a plurality of users, may result in a low value for the deep sleep metric. 
     In some implementations, a value of a rapid-eye movement (REM) sleep metric is determined using a value of a duration of time that a user experiences REM sleep during a total duration of sleep. In some implementations, duration of REM sleep and/or NREM sleep and/or deep sleep is determined on the basis of detected movement of a user, heart rate, breathing patterns, brain activity and/or body temperature. REM sleep deprivation is linked to mental and physical health issues, therefore a relatively low value of duration of REM sleep, as compared to a plurality of users, may result in a low value for the REM sleep metric. 
     In some implementations, a value of a wake after sleep onset (WASO) metric is determined using a value of total or average time between periods of sleep that a user experiences during a total duration of sleep. For example, a user may wake up three times at night due to nightmares or environmental disturbances, and have a total value of 45 minutes of time between periods of sleep. Continuous sleep is linked to improvements in duration of REM and deep sleep, therefore, a relatively high value of total or average time between periods of sleep as compared to a plurality of users, may result in a low value for a WASO metric. 
     In some implementations, a value of a breathing disturbances metric is determined using a value of a detected duration of halted breathing that a user experiences during a total duration of sleep. For example, a wearable computing device worn by a user while asleep, may detect a pulse oximeter reading below 90%, indicating a low blood oxygen level and suggesting that the user has stopped breathing. In this example, the user may wake up, start breathing again, and have a pulse oximeter reading greater than 90% or another threshold value for some time. While a pulse oximeter is one example of a physiological sensor to determine breathing disturbances, measurement of a duration of halted breathing by a user during sleep, is not limited to this example. Duration of halted breathing may also be detected by brain activity, for example. Threshold levels of blood oxygen levels, brain activity and/or breathing activity for determining a high level of breathing disturbances, may be determined by a plurality of users. 
     While table  300  portrays several sleep quality metrics and several types of sleep quality metrics that may be utilized by a biometric monitoring device to determined a unified sleep quality score, it should be understood that additional sleep quality metrics may be used. Furthermore, in some implementations a subset of the sleep quality metrics shown in table  300  are used to determined a sleep quality score. For example, a wearable computing device that does not have a sensor to determine breathing disturbances does not use the breathing disturbances metric to determine a unified sleep quality score for purposes of sleep quality assessment. 
     Generation of Unified Sleep Quality Score 
       FIG. 4A  is a block diagram  400  of a basis of determining a unified sleep score in accordance with one or more embodiments. In some implementations, one or more goal-driven metrics are used along with one or more user normalized metrics, each as described above with respect to  FIG. 3 , to determine an overall user normalized score. In some implementations, goal-driven metrics are not used to determine an overall user normalized score. In some implementations, population normalized metrics, as described above with respect to  FIG. 3 , are used to determine an overall population normalized score. 
     The determination of the user normalized score and the population normalized score depends on which specific sleep quality metrics are used and the weight of each selected sleep quality metric in the calculation of the each score. For example, a total sleep time, goal-driven sleep quality metric, may be given a relatively high weight in determining the user normalized score, while a restlessness metric may be given a relatively low weight, or determined not to be used. In some implementations, the selection of sleep metrics used to determine the user normalized and/or population normalized score depends on the capability of a biometric monitoring device used to perform sleep assessment of a user. For example, if a user wears a computing device that cannot measure a heart rate, the heart rate metric is not used to determine the user normalized score. 
     The weighting basis for sleep metrics used to determine the user normalized score and/or the population normalized score may change automatically or manually. For example, an atypical value for a respective sleep quality metric may result in a higher than default weighting for that respective sleep quality metric, or may limit the upper or lower bound of an associated score. The weighting basis may also change over time as a user develops better sleep habits, in order to motivate the user to continually achieve better sleep quality. The weighting basis may adjust from a default basis after a period of collecting physiological and/or environmental data while a user sleeps for a minimum threshold number of total sleep periods (e.g., nights). A user may also be able to adjust the weighting basis for one or more sleep quality metrics, in determining an overall unified sleep quality score, which relies on the various goal-driven, user normalized and/or population normalized metrics. For example, a user may be able to indicate a strong link to restlessness and perceived sleep quality, resulting in an increased weighting of that metric. In some implementations, one or more population-normalized metrics are given more weight than one or more user-normalized metrics, while in some implementations one or more user-normalized metrics are given more weight than one or more population-normalized metrics. 
     After determining a respective user normalized score and a respective population normalized score for sleep quality, a unified sleep quality score is determined, using the user normalized score and the population normalized score. In some implementations, there is a weighting component of each of the user normalized score and the population normalized score in the determination of the unified sleep score. This weighting component may be adjusted manually or automatically, as described above with respect to the weighting basis for each of the user normalized and population normalized scores. 
       FIG. 4B  is a table  402  of a basis of determining a unified sleep score in accordance with one or more embodiments. The examples of table  402  are non-limiting, and are merely used to illustrate one particular technique for using a user normalized score and a population normalized score to generate a unified sleep quality score. As described with respect to  FIGS. 3 and 4A , a user normalized score may be based on one or more of a heart rate metric, a total sleep time metric, a sleep consistency metric, a restlessness metric, and a time between sleep periods metric. It should be understood that this is a non-limiting scoring basis for generating the user normalized score, and that a user normalized score may be based on additional user-specific sleep quality metrics not described herein. As described with respect to  FIGS. 3 and 4A , a population normalized score may be based on one or more of a deep sleep metric, a REM metric, a WASO metric and a breathing disturbances metric. It should be understood that this is a non-limiting scoring basis for generating the population normalized score, and that a population normalized score may be based on additional population normalized sleep quality metrics not described herein. 
     A user normalized score may be determined and converted into a numerical value on a preset scale (e.g., −4 to 1, or 0 to 4, or 1 to 100). Similarly, a population normalized score may be determined and converted into a numerical value on a preset scale. The non-limiting examples shown in table  402  illustrate a range of values from −4 at the worst to 1 at the best for a user normalized score, and from −1 at the worst to 1 at the best for a population normalized score. In some embodiments, the user normalized score and the population normalized score use the same scoring value range. The non-limiting example calculation of a unified sleep quality score combines the determined user normalized score and population normalized score, multiplies the sum by 10, and adds the result to 80 to obtain a final value ranging from 30 at the worst to 100 at the best. 
     Sleep Quality Assessment System 
     In certain embodiments described in the present disclosure, a wearable computing device is capable of, and configured to collect physiological sensor readings of a user, and determine values of one or more sleep quality metrics and/or a unified sleep score. However, in some embodiments, a wearable computing device, or another portable electronic device used to detect physiological information of a user, is in communication with another computing device configured to determine these values.  FIG. 5  illustrates a networked relationship  500  between a wearable computing device  502  and an external computing device  504  in accordance with one or more embodiments. 
     The wearable computing device  502  may be configured to collect one or more types of physiological and/or environmental data from embedded sensors and/or external devices, as described throughout the present disclosure, and communicate or relay such information over one or more networks  506  to other devices. This includes, in some implementations, relaying information to devices capable of serving as Internet-accessible data sources, thus permitting the collected data to be viewed, for example, using a web browser or network-based application at an external computing device  504 . For example, while the user is asleep and wearing the wearable computing device  502 , the wearable computing device  502  may calculate and optionally store the user&#39;s sleeping heart rate using one or more biometric sensors. The wearable computing device  502  may then transmit data representative of the user&#39;s sleeping heart rate over network  506  to an account on a web service, computer, mobile phone, or health station where the data may be stored, processed, and visualized by the user. 
     While the example wearable computing device  502  is shown to have a touchscreen display, it should be understood that the wearable computing device  502  may not have any type of display unit, or may have any variety of audio and/or visual feedback components, such as light-emitting diodes (LEDs) (of various colors), buzzers, speakers, or a display with limited functionality. The wearable computing device  502  may be configured to be attached to the user&#39;s body, or clothing, such as a wrist bracelet, watch, ring, electrode, finger-clip, toe-clip, chest-strap, ankle strap or a device placed in a pocket. Additionally, the wearable computing device  502  may alternatively be embedded in something in contact with the user such as clothing, a mat under the user, a blanket, a pillow or another accessory involved in the activity of engaging in sleep. 
     The communication between wearable computing device  502  and external device  504  may be facilitated by one or more networks  506 . In some implementations, one or more networks  506  include one or more of an ad hoc network, a peer to peer communication link, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, or any other type of network. The communication between the wearable computing device  502  and external device  504  may also be performed through a direct wired connection. This direct-wired connection may be associated with any suitable or desirable communication protocol and/or physical connector, such as Universal Serial Bus (USB), Micro-USB, WiFi, Bluetooth, FireWire, PCIe, or the like. 
     A variety of computing devices may be in communication with the wearable computing device  502 , to facilitate sleep quality assessment. As depicted in  FIG. 5 , an example external computing device  504  is a smart phone with a display  508 . An external computing device  504  may be any computing device capable of assessing values for one or more sleep quality metrics and/or a unified sleep score, such as, but not limited to, a smart phone, personal digital assistant (PDA), a mobile phone, a tablet, a personal computer, a laptop computer, a smart television, and/or a video game console. This example external computing device  504  illustrates that in some embodiments, through this networked relationship shown in  FIG. 5 , an external computing device  504  may be implemented to determine one or more sleep quality metrics, and/or a unified sleep score. For example, a user wears a wearable computing device  502  equipped as a bracelet with one or more physiological sensors but without a display. In this example, over the course of the night, as the user sleeps, the wearable computing device  502  records the user&#39;s heart beat, movement, body temperature and blood oxygen level as well as room temperature and ambient light levels, and periodically transmits this information to the external computing device  504 . Alternatively, the wearable computing device  502  may store and transmit the collected physiological and/or environmental data and transmit this data to the external computing device  504  in response to a trigger, such as detection of the user being awake after a period of being asleep. In some implementations, the user must be awake for a threshold period of time to set this trigger (e.g., awake for at least 10 minutes), and/or awake for a threshold period of time after a threshold period of sleep has been experienced (e.g., awake for at least 5 minutes after having at least 6 hours of sleep). In some implementations, a trigger for determining one or more values of sleep quality metrics and/or a unified sleep score, is detection of a command performed at the external computing device  504 , such as manual or automatic execution of an instruction to synchronize collected physiological and/or environmental data and determine the unified sleep score. 
     The example external computing device  504  illustrates that in some implementations, the external computing device  504  may present the values of one or more sleep quality metrics and/or a unified sleep score. This presentation may be made to the user (e.g., wearer) of the wearable computing device  502 , or may, for example, be made to a sleep therapy provider for the user, such as a doctor or caregiver. In some implementations, the external computing device  504  determines one or more values for one or more sleep quality metrics and/or a unified sleep score and sends this information back to the wearable computing device  502  via the one or more networks  506 , for presentation of such determined value or values to the user (e.g., wearer) of the wearable computing device. Additional information regarding presentation of a unified sleep score and/or values of one or more sleep quality metrics are discussed below, with respect to  FIGS. 7A and 7B . 
       FIG. 6  illustrates a system  600  of client devices and a server system  608  for performing sleep quality assessment in accordance with one or more embodiments. In certain implementations, a sleep quality assessment system or platform, is implemented across a plurality of electronic devices. The wearable computing devices  502   a ,  502   b  and  502   c  may have characteristics similar to those described above with respect to wearable computing device  502  in  FIG. 5 , where each wearable computing device is coupled to a respective user  602   a ,  602   b  and  602   c . The one or more networks  506  may have characteristics similar to those described above with respect to one or more networks  506  in  FIG. 5 . The example external computing devices  504   a  (e.g., a laptop computer),  504   b  (e.g., a smart phone),  504   c  (e.g., a personal computer) may have characteristics similar to those described above with respect to external computing device  504  in  FIG. 5 . 
     In some implementations, of a sleep quality assessment system  600 , one or more wearable computing devices such as wearable computing device  502   a  are directly connected to one or more networks  506 , connecting server system  608 , and optionally connecting one or more external computing devices such as external computing devices  504   a ,  504   b , and/or  504   c . In some implementations, one or more external computing devices  504   a ,  504   b  and/or  504   c  are interconnected in a local area network (LAN)  604  (or another type of communication interconnection), which is connected to the one or more networks  506 . The example LAN  604  may interconnect one or more external computing devices such as devices  504   a ,  504   b , or  504   c , as well as one or more wearable computing devices such as  502   c . In some implementations, one or more wearable computing devices such as wearable computing device  502   b  are connected to one or more networks  506 , indirectly, through LAN  604  to one or more external computing devices  504 , which is/are connected to the one or more networks  506 . In some implementations, one or more wearable computing devices such as wearable computing device  502   b  are connected to one or more networks  506  directly, and indirectly through LAN  604 , as described above. For example, wearable computing device  502   b  is connected to smart phone  504   b  through a Bluetooth connection, smart phone  504   b  is connected to server system  608  through network  506 , and wearable computing device  502   b  is also connected to server system  608  through network  506 . 
     Sleep Quality Assessment Server 
     A sleep quality assessment system  600  may implement a server system  608  to collect detected physiological and/or environmental sensor readings from one or more wearable computing devices such as devices  502   a ,  502   b  and  502   c  as shown. In some implementations, server system  608  may also collect values of sleep quality assessment metrics and/or a unified sleep score from one or more wearable computing devices such as devices  502   a ,  502   b  and  502   c  and/or from one or more external computing devices such as devices  504   a ,  504   b  and  504   c  as shown. For example, wearable computing device  502   a  is not associated with an external computing device, therefore it transmits collected physiological data while user  602   a  sleeps to server system  608 , which analyzes the received data to determine values of one or more sleep quality metrics and a unified sleep score, to transmit back to wearable computing device  502   a . In another example, wearable computing device  502   b  transmits collected physiological data while user  602   b  sleeps (or has finished sleeping) to both server system  608  and external computing device  504   a . In this example, external computing device  504   a  determines values for one or more sleep quality metrics and a unified sleep score, while server system  608  uses the received physiological data to update a user profile for user  602   b , stored in profiles database  614 . 
     In some implementations, server system  608  is implemented on one or more standalone data processing apparatuses or a distributed network of computers. In some embodiments, server system  108  also employs various virtual devices and/or services of third party service providers (e.g., third-party cloud service providers) to provide the underlying computing resources and/or infrastructure resources of server system  108 . In some embodiments, server system  108  includes, but is not limited to, a handheld computer, a tablet computer, a laptop computer, a desktop computer, or a combination of any two or more of these data processing devices or other data processing devices. 
     Server system  608  may include one or more processors or processing units  610  (e.g., CPUs) and one or more network interfaces  618  including an I/O interface to external computing devices and wearable computing devices. In some implementations, server system  608  includes memory  612 , and one or more communication buses for interconnecting these components. Memory  612  includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid state memory devices; and, optionally, includes non-volatile memory, such as one or more magnetic disk storage devices, one or more optical disk storage devices, one or more flash memory devices, or one or more other non-volatile solid state storage devices. Memory  612 , optionally, includes one or more storage devices remotely located from one or more processing units  610 . Memory  612 , or alternatively the non-volatile memory within memory  612 , includes a non-transitory computer readable storage medium. In some implementations, memory  612 , or the non-transitory computer readable storage medium of memory  612 , stores one or more programs, modules, and data structures. These programs, modules and data structures may include, but not be limited to one or more of an operating system including procedures for handling various basic system services and for performing hardware dependent tasks, a network communication module for connecting server system  608  to other computing devices (e.g., wearable computing devices  502   a ,  502   b  and  502   c  and/or external computing devices  504   a ,  504   b  and  504   c ) connected to one or more networks  506  via one or more network interfaces  618  (wired or wireless). 
     Memory  612  may also include a unified score determination module  614  for using collected physiological and/or environmental data of one or more users (e.g., received from one or more wearable computing devices or external computing devices), to determine values of one or more sleep quality metrics and/or a unified sleep score, as described earlier in the present disclosure, with respect to  FIGS. 3, 4A and 4B . Memory  612  may also include a profiles database  616  storing user profiles for users of the sleep quality assessment system  600 , where a respective user profile for a user may include a user identifier (e.g., an account name or handle), login credentials to the sleep quality assessment system, email address or preferred contact information, wearable computing device information (e.g., model number), demographic parameters for the user (e.g., age, gender, occupation, etc.), historical sleep quality information and identified sleep quality trends of the user (e.g., particularly restless sleeper). In some implementations, collected physiological information of a plurality of users (such as users  602   a ,  602   b  and  602   c ) of the sleep quality assessment system  600  provides for more robust population-normalized sleep metrics, as described above with respect to  FIGS. 3, 4A and 4B . For example, user  602   a  is a 35 year old female veterinarian and user  602   b  is a 34 year old female veterinarian, and each of their respective historical sleep quality physiological data and/or metrics are used in the determination of one or more population-normalized sleep quality metrics for each other, due to their closely aligned demographic characteristics. In some implementations, a user may opt in or opt out of providing sleep quality assessment information to a population-normalization determination for other users. In some implementations, a user&#39;s sleep quality information may be incorporated into population-normalized sleep quality metric information used to determine that user&#39;s own values for one or more sleep quality metrics. 
     Presentation of Unified Sleep Quality Score 
       FIG. 7A  illustrates embodiments of wearable computing devices  700   a  and  700   b , each having displays for presenting a representation of a unified sleep score in accordance with one or more embodiments. The computing devices  700   a  and  700   b  of  FIG. 7A  each illustrate a generally rectangular display. Computing device  700   a  illustrates that some wearable computing devices may have a set of light-emitting diodes (LEDs), to indicate a status, or in this context, a representation of a unified sleep quality score. For example, 3 out of 4 illuminated LEDs, as shown in display  702   a , may indicate a moderately good unified sleep quality score. 
     In certain embodiments, computing device  700   b  may be configured to display text and/or other visual elements on the display  702   b . The display  702   b  of the computing device  700   b  may represent a relatively small display, wherein portrayal of extensive information may be undesirable and/or impractical. As a result, display  702   b  of a wearable computing device  700   b  with limited display capability, may simply portray a numerical (e.g., 45, or 8/10) and/or subjective representation (e.g., good, bad, neutral) of the unified sleep quality score. 
       FIG. 7B  illustrates embodiments of wearable computing devices having touchscreen displays for presenting a representation of a unified sleep score in accordance with one or more embodiments.  FIG. 7B  illustrates wearable computing devices  704   a  and  704   b  having a generally rectangular, horizontally-arranged, display  706   a  and  706   b , respectively. Display  706   a  illustrates that in some implementations, a unified sleep quality score may be portrayed along with more detailed information about the sleep quality metrics contributing to the unified sleep quality score. In some implementations, a user may be able to scroll through this detailed information on display  706   a . Display  706   b  illustrates that a representation of a unified sleep quality score may be in the form of a graphic, emoji, background color or another audio-visual representation other than numbers or text. 
     Methods for Assessing Sleep Quality 
       FIG. 8  illustrates a flow diagram for a process  800  for determining a sleep quality assessment in accordance with the present disclosure. In certain embodiments, the process  800  may be performed at least in part by a computing device having one or more physiological and/or environmental sensors and/or control circuitry of the computing device. For example, a user may be wearing a computing device on a wrist or other body part, or otherwise have the computing device attached to him or her, when at least part of the process  800  is performed. The process  800  may be performed in order to assess the quality of sleep experienced by a user, using one or more sleep quality metrics based off of detected physiological and/or environmental information. In some implementations, process  800  is performed at least in part by a plurality of electronic devices (e.g., a wearable computing device and an external computing device), and in some implementations, process  800  is performed at least in part by a sleep quality assessment server (e.g., server system  608  as described with respect to  FIG. 6  and a wearable computing device). 
     In certain embodiments, prior to execution of the first step  802  of the process  800 , a user may have been detected to be in a wakeful state. For example, the user is detected to be awake and not attempting to sleep from 7:01 AM until 10:59 PM. When the user attempts to fall asleep, one or more measurable physiological parameters relating to sleep quality metrics may be triggered to be detected. In certain embodiments, the process  800  may be implemented in connection with a wearable computing device that may be worn, for example, about a wrist or other member of the user. In certain embodiments, the sleep quality assessment process  800  may be initiated when the user puts on the device and/or contacts a device with the user&#39;s skin and/or lays down in a reclined position. Alternatively, sleep quality assessment processes in accordance with the present disclosure may be initiated after a predetermined period of time in an idle state, or in connection with a request to initiate a sleep assessment program. 
     At block  802 , the process  800  involves detecting an attempt by a user to fall asleep, or receiving one or more signals indicating an attempt by the user to fall asleep. For example, a user is wearing a computing device that detects that the user has entered a reclined position, that the ambient light is low and/or that the device is in contact with the user but is relatively motionless. In some implementations, process  800  includes detecting an onset of sleep by the user, or receiving one or more signals indicating an onset of sleep by the user and determining a duration of time from the attempt to fall asleep to the onset of sleep. In some implementations, sleep attempt detection is based automatically off of one or more physiological and/or environmental sensor readings, while in some implementations it is based off of manual input from the user (e.g., pressing a button on a wearable computing device or indicating attempt to sleep in an application on a wearable or external computing device). 
     At block  804 , the process  800  involves collecting (or receiving) physiological information associated with the user, including at least one sleeping heart rate. For example, the sleeping heart rate may be an average of periodically measured heart rate values of a user during a total duration of sleep (e.g., during a night of sleep). In some implementations, the physiological information is collected periodically and/or upon detection of a change in some condition (e.g., movement). In some implementations, physiological information is still collected while the user is awake, such as in a period of time between an attempt to fall sleep and the onset of sleep, or during a period of wakefulness before the user awakens for a long period of time (e.g., waking up in the morning). In some implementations, collecting physiological information about the user includes collecting one or more sets of values associated with: movement of the user, total sleep duration, total deep sleep duration, duration of wake time after sleep onset (WASO), total rapid-eye-movement (REM) sleep duration, total light sleep duration, breathing patterns of the user, breathing disturbances of the user and temperature of the user. 
     At block  806 , the process  800  involves determining respective values for one or more sleep quality metrics, based at least in part on the collected physiological information. In some implementations, determining respective values for one or more sleep quality metrics includes using at least one wakeful resting heart rate of the user. For example, determining a value for a heart rate metric includes comparing an average sleeping heart rate of the collected physiological information, and an average wakeful resting heart rate detected and calculated before the user attempts to fall asleep. In some implementations, the wakeful resting heart rate is determined periodically during a period of extended wakefulness before the user enters a state of sleep. In some implementations, the wakeful resting heart rate is the lowest resting heart rate value detected while the user is awake. In some implementations, determining respective values for the one or more sleep quality metrics includes comparing the at least one sleeping heart rate of the user and the at least one wakeful resting heart rate of the user. In some implementations, determining respective values for the one or more sleep quality metrics includes comparing the at least one sleeping heart rate of the user to a threshold value. 
     In certain embodiments, the one or more sleep quality metrics includes a first set of sleep quality metrics associated with sleep quality of a plurality of users, and a second set of sleep quality metrics associated with historical sleep quality of the user. In certain embodiments, process  800  further includes detecting that the user is awake after the detected onset of sleep, and determining the respective values for one or more sleep quality metrics, in response to detecting that the user is awake. 
     At block  808 , the process  800  includes determining a unified score for sleep quality of the user, based at least in part on the respective values of the one or more sleep quality metrics. In certain embodiments, determining the unified score for sleep quality includes determining a respective metric-score for each of the one or more sleep quality metrics and applying a respective weighting for each of the one or more sleep quality metrics. In some embodiments, process  800  includes collecting sleep quality feedback information from the user and determining the unified score for sleep quality of the user based at least in part on the collected sleep quality feedback information. 
     At block  810 , the process  800  involves presenting a representation of the unified score to the user, or generating instructions to provide a representation of the unified score to the user. For example, as shown in  FIGS. 7A and 7B , a representation of the unified score may vary depending on the audio.visual feedback capability of a wearable and/or biometric monitoring device. A representation of the unified score may include any of alphanumeric characters, graphics, sounds, lights, patterns and animations. In another example, server system  608  of  FIG. 6  sends instructions to a wearable computing device to present a unified sleep quality assessment score of  85 , on a display of the wearable computing device. 
       FIG. 9  illustrates a process  900  for determining a sleep quality assessment in accordance with one or more embodiments disclosed herein. The process  900  may be performed at least in part by a computing device having one or more physiological and/or environmental sensors and/or control circuitry of the computing device. For example, the user may be wearing a computing device on a wrist or other body part, or otherwise have the computing device attached to him or her, when performing at least part of the process  900 . The process  900  may be performed in order to assess the quality of sleep experienced by a user, using one or more sleep quality metrics based off of detected physiological and/or environmental information. In some implementations, process  900  is performed at least in part by a plurality of electronic devices (e.g., a wearable computing device and an external computing device), and in some implementations, process  900  is performed at least in part by a sleep quality assessment server (e.g., server system  608  as described with respect to  FIG. 6  and a wearable computing device). 
     At block  902 , the process  900  involves detecting an attempt by a user to fall asleep or receiving one or more signals indicating an attempt by the user to fall asleep. For example, a user is wearing a computing device that detects that the user has entered a reclined position, that the ambient light is low and/or that the device is in contact with the user but is relatively motionless. In some implementations, process  900  includes detecting an onset of sleep by the user, or receiving one or more signals indicating an onset of sleep by the user and determining a duration of time from the attempt to fall asleep to the onset of sleep. 
     At block  904 , the process  900  involves collecting physiological information associated with the user. In some implementations, collecting physiological information associated with the user includes collecting one or more sets of values associated with: movement of the user, total sleep duration, total deep sleep duration, duration of wake time after sleep onset (WASO), total rapid-eye-movement (REM) sleep duration, total light sleep duration, breathing patterns of the user, breathing disturbances of the user and temperature of the user. 
     At block  906 , the process  900  involves determining respective values for one or more sleep quality metrics, based at least in part on the collected physiological information, wherein the one or more sleep quality metrics includes a first set of sleep quality metrics associated with sleep quality of a plurality of users, and a second set of sleep quality metrics associated with historical sleep quality of the user. In some implementations, determining respective values for one or more sleep quality metrics includes using at least one wakeful resting heart rate of the user. For example, determining a value for a heart rate metric includes using an average sleeping heart rate of the collected physiological information, and an average wakeful resting heart rate detected and calculated before the user attempts to fall asleep. In some implementations, determining respective values for the one or more sleep quality metrics includes comparing the at least one sleeping heart rate of the user and the at least one wakeful resting heart rate of the user. In certain embodiments, process  900  further includes detecting that the user is awake after the detected onset of sleep, or receiving one or more signals indicating that the user is awake, and determining the respective values for one or more sleep quality metrics, in response to detecting that the user is awake. 
     At block  908 , the process  900  includes determining a unified score for sleep quality of the user, based at least in part on the respective values of the one or more sleep quality metrics. In certain embodiments, determining the unified score for sleep quality includes determining a respective metric-score for each of the one or more sleep quality metrics and based at least in part on a respective weighting for each of the one or more sleep quality metrics. In some embodiments, process  900  includes collecting sleep quality feedback information from the user or receiving collected sleep quality information and determining the unified score for sleep quality of the user based at least in part on the collected sleep quality feedback information 
     At block  910 , the process  900  involves presenting a representation of the unified score to the user or generating a representation of the unified score to present to the user. For example, as shown in  FIGS. 7A and 7B , a representation of the unified score may vary depending on the audio.visual feedback capability of a wearable and/or biometric monitoring device. A representation of the unified score may include any of alphanumeric characters, graphics, sounds, lights, patterns and animations. In another example, server system  608  of  FIG. 6  sends instructions to a wearable computing device to present a unified sleep quality assessment score of  85 , on a display of the wearable computing device. 
     Additional Embodiments 
     Depending on the embodiment, certain acts, events, or functions of any of the processes or algorithms described herein can be performed in a different sequence, may be added, merged, or left out altogether. Thus, in certain embodiments, not all described acts or events are necessary for the practice of the processes. Moreover, in certain embodiments, acts or events may be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or via multiple processors or processor cores, rather than sequentially. 
     Certain methods and/or processes described herein may be embodied in, and partially or fully automated via, software code modules executed by one or more general and/or special purpose computers. The word “module” refers to logic embodied in hardware and/or firmware, or to a collection of software instructions, possibly having entry and exit points, written in a programming language, such as, for example, C or C++. A software module may be compiled and linked into an executable program, installed in a dynamically linked library, or may be written in an interpreted programming language such as, for example, BASIC, Perl, or Python. It will be appreciated that software modules may be callable from other modules or from themselves, and/or may be invoked in response to detected events or interrupts. Software instructions may be embedded in firmware, such as an erasable programmable read-only memory (EPROM). It will be further appreciated that hardware modules may be comprised of connected logic units, such as gates and flip-flops, and/or may be comprised of programmable units, such as programmable gate arrays, application specific integrated circuits, and/or processors. The modules described herein are preferably implemented as software modules, but may be represented in hardware and/or firmware. Moreover, although in some embodiments a module may be separately compiled, in other embodiments a module may represent a subset of instructions of a separately compiled program, and may not have an interface available to other logical program units. 
     In certain embodiments, code modules may be implemented and/or stored in any type of computer-readable medium or other computer storage device. In some systems, data (and/or metadata) input to the system, data generated by the system, and/or data used by the system can be stored in any type of computer data repository, such as a relational database and/or flat file system. Any of the systems, methods, and processes described herein may include an interface configured to permit interaction with patients, health care practitioners, administrators, other systems, components, programs, and so forth. 
     Conditional language used herein, such as, among others, “can,” “could,” “might,” “may,” “e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is intended in its ordinary sense and is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without author input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment. The terms “comprising,” “including,” “having,” and the like are synonymous, are used in their ordinary sense, and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations, and so forth. Also, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list. Conjunctive language such as the phrase “at least one of X, Y and Z,” unless specifically stated otherwise, is understood with the context as used in general to convey that an item, term, element, etc. may be either X, Y or Z. Thus, such conjunctive language is not generally intended to imply that certain embodiments require at least one of X, at least one of Y and at least one of Z to each be present. 
     Reference throughout this specification to “certain embodiments” or “an embodiment” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least some embodiments. Thus, appearances of the phrases “in some embodiments” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment and may refer to one or more of the same or different embodiments. Furthermore, the particular features, structures or characteristics can be combined in any suitable manner, as would be apparent to one of ordinary skill in the art from this disclosure, in one or more embodiments. 
     It should be appreciated that in the above description of embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that any claim require more features than are expressly recited in that claim. Moreover, any components, features, or steps illustrated and/or described in a particular embodiment herein can be applied to or used with any other embodiment(s). Further, no component, feature, step, or group of components, features, or steps are necessary or indispensable for each embodiment. Thus, it is intended that the scope of the inventions herein disclosed and claimed below should not be limited by the particular embodiments described above, but should be determined only by a fair reading of the claims that follow.