Patent Abstract:
Sleep management techniques and devices are configured for use with a data-capable personal worn or carried device. In one embodiment, a method includes receiving data representing a sleep profile defining parameters upon which a target score is established, and acquiring data representing acquired parameters associated with sleep activity. The method also includes determining a first score for a first acquired parameter, determining a second score for a second acquired parameter, and calculating at a processor a sleep score based on data in a memory including the first score and the second score. Further, the method includes causing presentation of a representation of the sleep score to indicate either an attainment of the target score or a deviation therefrom.

Full Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application is a continuation-in-part U.S. non-provisional patent application of U.S. patent application Ser. No. 13/181,495, filed Jul. 12, 2011, which is a continuation-in-part of prior U.S. patent application Ser. No. 13/180,000, filed Jul. 11, 2011, which claims the benefit of U.S. Provisional Patent Application No. 61/495,995, filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,994, filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,997, filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,996, filed Jun. 11, 2011, and is a continuation-in-part of prior U.S. patent application Ser. No. 13/158,416, filed Jun. 11, 2011, which is a continuation-in-part of prior U.S. patent application Ser. No. 13/158,372, filed Jun. 10, 2011, and U.S. patent application Ser. No. 13/181,495 claims the benefit of U.S. Provisional Patent Application No. 61/495,995, filed Jun. 11, 2011, U.S. Provisional Patent Application. No. 61/495,994, filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,997, filed Jun. 11, 2011, and U.S. Provisional Patent Application No. 61/495,996, filed Jun. 11, 2011; U.S. patent application Ser. No. 13/181,495 is also a continuation-in-part of prior U.S. patent application Ser. No. 13/180,320, filed Jul. 11, 2011, which claims the benefit of U.S. Provisional Patent Application No. 61/495,995, filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,994, filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,997, filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,996, filed Jun. 11, 2011, and is a continuation-in-part of prior U.S. patent application Ser. No. 13/158,416, filed Jun. 11, 2011, which is a continuation-in-part of prior U.S. patent application Ser. No. 13/158,372, filed Jun. 10, 2011; U.S. patent application Ser. No. 13/181,495 is also a continuation-in-part of prior U.S. patent application Ser. No. 13/158,416, filed Jun. 11, 2011, which is a continuation-in-part of prior U.S. patent application Ser. No. 13/158,372, filed Jun. 10, 2011; U.S. patent application Ser. No. 13/181,495 is also a continuation-in-part of prior U.S. patent application Ser. No. 13/158,372, filed Jun. 10, 2011; this application claims the benefit of U.S. Provisional Patent Application No. 61/495,995, filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,994, filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,997, filed Jun. 11, 2011, and U.S. Provisional Patent Application No. 61/495,996, filed Jun. 11, 2011, and is a continuation-in-part of prior U.S. patent application Ser. No. 13/180,320, filed Jul. 11, 2011, which claims the benefit of U.S. Provisional Patent Application No. 61/495,995, filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,994, filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,997, filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,996, filed Jun. 11, 2011, and is a continuation-in-part of prior U.S. patent application Ser. No. 13/158,416, filed Jun. 11, 2011, which is a continuation-in-part of prior U.S. patent application Ser. No. 13/158,372, filed Jun. 10, 2011, and also is a continuation-in-part of prior U.S. patent application Ser. No. 13/158,416, filed Jun. 11, 2011, which is a continuation-in-part of prior U.S. patent application Ser. No. 13/158,372, filed Jun. 10, 2011, and is also a continuation-in-part of prior U.S. patent application Ser. No. 13/158,372, filed Jun. 10, 2011; this application is also a continuation-in-part of U.S. Nonprovisional patent application Ser. No. 13/361,919, filed Jan. 30, 2012, which is a continuation of U.S. Nonprovisional patent application Ser. No. 13/181,495 filed Jul. 12, 2011, which claims the benefit of U.S. Provisional Patent Application No. 61/495,995 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,994 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,997 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,996 filed Jun. 11, 2011 and, is a continuation-in-part of U.S. patent application Ser. No. 13/180,000 filed Jul. 11, 2011, which claims the benefit of U.S. Provisional Patent Application No. 61/495,995 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,994 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,997 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,996 filed Jun. 11, 2011 and is a continuation-in-part of U.S. patent application Ser. No. 13/158,416 filed Jun. 11, 2011, which is a continuation-in-part of U.S. patent application Ser. No. 13/158,372 filed Jun. 10, 2011; U.S. Nonprovisional patent application Ser. No. 13/181,495 filed Jul. 12, 2011 is also a continuation-in-part of U.S. patent application Ser. No. 13/180,320 filed Jul. 11, 2011, which claims the benefit of U.S. Provisional Patent Application No. 61/495,995 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,994 filed Jun. 11, 2011, U.S. Provisional Patent. Application No. 61/495,997 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,996 filed Jun. 11, 2011 and is a continuation-in-part of U.S. patent application Ser. No. 13/158,416 filed Jun. 11, 2011, which is a continuation-in-part of U.S. patent application Ser. No. 13/158,372 filed Jun. 10, 2011; U.S. Nonprovisional patent application Ser. No. 13/361,919 is also a continuation of U.S. patent application Ser. No. 13/181,511 filed Jul. 12, 2011, which claims the benefit of U.S. Provisional Patent Application No. 61/495,995 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,994 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,997 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,996 filed Jun. 11, 2011 and is a continuation-in-part of U.S. patent application Ser. No. 13/180,000 filed Jul. 11, 2011, which claims the benefit of U.S. Provisional Patent Application No. 61/495,995 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,994 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,997 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,996 filed Jun. 11, 2011 and is a continuation-in-part of U.S. patent application Ser. No. 13/158,416 filed Jun. 11, 2011, which is a continuation-in-part of U.S. patent application Ser. No. 13/158,372 filed Jun. 10, 2011; U.S. patent application Ser. No. 13/181,511 filed Jul. 12, 2011 is also a continuation-in-part of U.S. patent application Ser. No. 13/180,320 filed Jul. 11, 2011, which claims the benefit of U.S. Provisional Patent Application No. 61/495,995 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,994 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,997 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,996 filed Jun. 11, 2011 and is a continuation-in-part of U.S. patent application Ser. No. 13/158,416 filed Jun. 11, 2011, which is a continuation-in-part of U.S. patent application Ser. No. 13/158,372 filed Jun. 10, 2011; this application is also a continuation-in-part of U.S. patent application Ser. No. 13/181,511 filed Jul. 12, 2011, which claims the benefit of U.S. Provisional Patent Application No. 61/495,995 filed Jun. 11, 2011, U.S. Provisional. Patent Application No. 61/495,994 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,997 filed Jun. 11, 2011, U.S. Provisional Patent. Application No. 61/495,996 filed Jun. 11, 2011 and is a continuation-in-part of U.S. patent application Ser. No. 13/180,000 filed Jul. 11, 2011, which claims the benefit of U.S. Provisional Patent Application No. 61/495,995 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,994 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,997 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,996 filed Jun. 11, 2011 and is a continuation-in-part of U.S. patent application Ser. No. 13/158,416 filed Jun. 11, 2011, which is a continuation-in-part of U.S. patent application Ser. No. 13/158,372 filed Jun. 10, 2011; U.S. patent application Ser. No. 13/181,511 filed Jul. 12, 2011 is also a continuation-in-part of U.S. patent application Ser. No. 13/180,320 filed Jul. 11, 2011, which claims the benefit of U.S. Provisional Patent Application No. 61/495,995 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,994 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,997 filed Jun. 11, 2011, U.S. Provisional Patent Application No. 61/495,996 filed Jun. 11, 2011 and is a continuation-in-part of U.S. patent application Ser. No. 13/158,416 filed Jun. 11, 2011, which is a continuation-in-part of U.S. patent application Ser. No. 13/158,372 filed Jun. 10, 2011; this application is also related to copending U.S. Nonprovisional patent application Ser. No. 13/______,______, filed Mar. 28, 2012, entitled “Activity Attainment Method and Apparatus for a Wellness Application Using Data from a Data-Capable Band,” U.S. Nonprovisional patent application Ser. No. 13/______,______, filed Mar. 28, 2012, entitled “Nutrition Management Method and Apparatus for a Wellness Application Using Data from a Data-Capable Band,” and U.S. Nonprovisional patent application Ser. No. 13/______,______, filed Mar. 28, 2012, entitled “General Health and Wellness Management Method and Apparatus for a Wellness Application Using Data from a Data-Capable Band,” all of which are herein incorporated by reference for all purposes. 
     
    
     FIELD 
       [0002]    The present invention relates generally to electrical and electronic hardware, computer software, wired and wireless network communications, and computing devices. More specifically, sleep management techniques and devices for use with a data-capable personal worn or carried device are described. 
       BACKGROUND 
       [0003]    With the advent of greater computing capabilities in smaller personal and/or portable form factors and an increasing number of applications (i.e., computer and Internet software or programs) for different uses, consumers (i.e., users) have access to large amounts of personal data. Information and data are often readily available, but poorly captured using conventional data capture devices. Conventional devices typically lack capabilities that can capture, analyze, communicate, or use data in a contextually-meaningful, comprehensive, and efficient manner. Further, conventional solutions are often limited to specific individual purposes or uses, demanding that users invest in multiple devices in order to perform different activities (e.g., a sports watch for tracking time and distance, a GPS receiver for monitoring a hike or run, a cyclometer for gathering cycling data, and others). Although a wide range of data and information is available, conventional devices and applications fail to provide effective solutions that comprehensively capture data for a given user across numerous disparate activities. 
         [0004]    Some conventional solutions combine a small number of discrete functions. Functionality for data capture, processing, storage, or communication in conventional devices such as a watch or timer with a heart rate monitor or global positioning system (“GPS”) receiver are available conventionally, but are expensive to manufacture and purchase. Other conventional solutions for combining personal data capture facilities often present numerous design and manufacturing problems such as size restrictions, specialized materials requirements, lowered tolerances for defects such as pits or holes in coverings for water-resistant or waterproof devices, unreliability, higher failure rates, increased manufacturing time, and expense. Subsequently, conventional devices such as fitness watches, heart rate monitors, GPS-enabled fitness monitors, health monitors (e.g., diabetic blood sugar testing units), digital voice recorders, pedometers, altimeters, and other conventional personal data capture devices are generally manufactured for conditions that occur in a single or small groupings of activities. Problematically, though, conventional devices do not provide effective solutions to users in terms of providing a comprehensive view of one&#39;s overall health or wellness as a result of a combined analysis of data gathered. This is a limiting aspect of the commercial attraction of the various types of conventional devices listed above. 
         [0005]    Generally, if the number of activities performed by conventional personal data capture devices increases, there is a corresponding rise in design and manufacturing requirements that results in significant consumer expense, which eventually becomes prohibitive to both investment and commercialization. Further, conventional manufacturing techniques are often limited and ineffective at meeting increased requirements to protect sensitive hardware, circuitry, and other components that are susceptible to damage, but which are required to perform various personal data capture activities. As a conventional example, sensitive electronic components such as printed circuit board assemblies (“PCBA”), sensors, and computer memory (hereafter “memory”) can be significantly damaged or destroyed during manufacturing processes where overmoldings or layering of protective material occurs using techniques such as injection molding, cold molding, and others. Damaged or destroyed items subsequently raises the cost of goods sold and can deter not only investment and commercialization, but also innovation in data capture and analysis technologies, which are highly compelling fields of opportunity. 
         [0006]    Thus, what is needed is a solution for data capture devices without the limitations of conventional techniques. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0007]    Various embodiments or examples (“examples”) of the invention are disclosed in the following detailed description and the accompanying drawings: 
           [0008]      FIG. 1  illustrates an exemplary data-capable band system; 
           [0009]      FIG. 2  illustrates a block diagram of an exemplary data-capable band; 
           [0010]      FIG. 3  illustrates sensors for use with an exemplary data-capable band; 
           [0011]      FIG. 4  illustrates an application architecture for an exemplary data-capable band; 
           [0012]      FIG. 5A  illustrates representative data types for use with an exemplary data-capable band; 
           [0013]      FIG. 5B  illustrates representative data types for use with an exemplary data-capable band in fitness-related activities; 
           [0014]      FIG. 5C  illustrates representative data types for use with an exemplary data-capable band in sleep management activities; 
           [0015]      FIG. 5D  illustrates representative data types for use with an exemplary data-capable band in medical-related activities; 
           [0016]      FIG. 5E  illustrates representative data types for use with an exemplary data-capable band in social media/networking-related activities; 
           [0017]      FIG. 6  illustrates an exemplary communications device system implemented with multiple exemplary data-capable bands; 
           [0018]      FIG. 7  illustrates an exemplary wellness tracking system for use with or within a distributed wellness application; 
           [0019]      FIG. 8  illustrates representative calculations executed by an exemplary conversion module to determine an aggregate value for producing a graphical representation of a user&#39;s wellness; 
           [0020]      FIG. 9  illustrates an exemplary process for generating and displaying a graphical representation of a user&#39;s wellness based upon the user&#39;s activities; 
           [0021]      FIG. 10  illustrates an exemplary graphical representation of a user&#39;s wellness over a time period; 
           [0022]      FIG. 11  illustrates another exemplary graphical representation of a user&#39;s wellness over a time period; 
           [0023]      FIGS. 12A-12F  illustrate exemplary wireframes of exemplary webpages associated with a wellness marketplace portal; 
           [0024]      FIG. 13  illustrates an exemplary computer system suitable for implementation of a wellness application and use with a data-capable band; 
           [0025]      FIG. 14  depicts an example of an aggregation engine, according to some examples; 
           [0026]      FIG. 15  depicts an example of a sleep manager, according to some examples; 
           [0027]      FIG. 16  is an example flow diagram for a technique of managing sleep using wearable devices, including sensors, according to some examples; 
           [0028]      FIG. 17  is another example flow diagram for another technique of managing sleep using wearable devices, including sensors, according to some examples; and 
           [0029]      FIG. 18  depicts a functional interaction between a sleep evaluator and score generator, according to some examples. 
       
    
    
     DETAILED DESCRIPTION 
       [0030]    Various embodiments or examples may be implemented in numerous ways, including as a system, a process, an apparatus, a user interface, or a series of program instructions on a computer readable medium such as a computer readable storage medium or a computer network where the program instructions are sent over optical, electronic, or wireless communication links. In general, operations of disclosed processes may be performed in an arbitrary order, unless otherwise provided in the claims. 
         [0031]    A detailed description of one or more examples is provided below along with accompanying figures. The detailed description is provided in connection with such examples, but is not limited to any particular example. The scope is limited only by the claims and numerous alternatives, modifications, and equivalents are encompassed. Numerous specific details are set forth in the following description in order to provide a thorough understanding. These details are provided for the purpose of example and the described techniques may be practiced according to the claims without some or all of these specific details. For clarity, technical material that is known in the technical fields related to the examples has not been described in detail to avoid unnecessarily obscuring the description. 
         [0032]      FIG. 1  illustrates an exemplary data-capable band system. Here, system  100  includes network  102 , bands  104 - 112 , server  114 , mobile computing device  116 , mobile communications device  118 , computer  120 , laptop  122 , and distributed sensor  124 . Bands  104 - 112  may be implemented as data-capable device that may be worn as a strap or band around an arm, leg, ankle, or other bodily appendage or feature. In other examples, bands  104 - 112  may be attached directly or indirectly to other items, organic or inorganic, animate, or static. In still other examples, bands  104 - 112  may be used differently. 
         [0033]    As described above, bands  104 - 112  may be implemented as wearable personal data or data capture devices (e.g., data-capable devices) that are worn by a user around a wrist, ankle, arm, ear, or other appendage, or attached to the body or affixed to clothing. One or more facilities, sensing elements, or sensors, both active and passive, may be implemented as part of bands  104 - 112  in order to capture various types of data from different sources. Temperature, environmental, temporal, motion, electronic, electrical, chemical, or other types of sensors (including those described below in connection with  FIG. 3 ) may be used in order to gather varying amounts of data, which may be configurable by a user, locally (e.g., using user interface facilities such as buttons, switches, motion-activated/detected command structures (e.g., accelerometer-gathered data from user-initiated motion of bands  104 - 112 ), and others) or remotely (e.g., entering rules or parameters in a website or graphical user interface (“GUI”) that may be used to modify control systems or signals in firmware, circuitry, hardware, and software implemented (i.e., installed) on bands  104 - 112 ). Bands  104 - 112  may also be implemented as data-capable devices that are configured for data communication using various types of communications infrastructure and media, as described in greater detail below. Bands  104 - 112  may also be wearable, personal, non-intrusive, lightweight devices that are configured to gather large amounts of personally relevant data that can be used to improve user health, fitness levels, medical conditions, athletic performance, sleeping physiology, and physiological conditions, or used as a sensory-based user interface (“UI”) to signal social-related notifications specifying the state of the user through vibration, heat, lights or other sensory based notifications. For example, a social-related notification signal indicating a user is on-line can be transmitted to a recipient, who in turn, receives the notification as, for instance, a vibration. 
         [0034]    Using data gathered by bands  104 - 112 , applications may be used to perform various analyses and evaluations that can generate information as to a person&#39;s physical (e.g., healthy, sick, weakened, or other states, or activity level), emotional, or mental state (e.g., an elevated body temperature or heart rate may indicate stress, a lowered heart rate and skin temperature, or reduced movement (e.g., excessive sleeping), may indicate physiological depression caused by exertion or other factors, chemical data gathered from evaluating outgassing from the skin&#39;s surface may be analyzed to determine whether a person&#39;s diet is balanced or if various nutrients are lacking, salinity detectors may be evaluated to determine if high, lower, or proper blood sugar levels are present for diabetes management, and others). Generally, bands  104 - 112  may be configured to gather from sensors locally and remotely. 
         [0035]    As an example, band  104  may capture (i.e., record, store, communicate (i.e., send or receive), process, or the like) data from various sources (i.e., sensors that are organic (i.e., installed, integrated, or otherwise implemented with band  104 ) or distributed (e.g., microphones on mobile computing device  116 , mobile communications device  118 , computer  120 , laptop  122 , distributed sensor  124 , global positioning system (“GPS”) satellites, or others, without limitation)) and exchange data with one or more of bands  106 - 112 , server  114 , mobile computing device  116 , mobile communications device  118 , computer  120 , laptop  122 , and distributed sensor  124 . As shown here, a local sensor may be one that is incorporated, integrated, or otherwise implemented with bands  104 - 112 . A remote or distributed sensor (e.g., mobile computing device  116 , mobile communications device  118 , computer  120 , laptop  122 , or, generally, distributed sensor  124 ) may be sensors that can be accessed, controlled, or otherwise used by bands  104 - 112 . For example, band  112  may be configured to control devices that are also controlled by a given user (e.g., mobile computing device  116 , mobile communications device  118 , computer  120 , laptop  122 , and distributed sensor  124 ). For example, a microphone in mobile communications device  118  may be used to detect, for example, ambient audio data that is used to help identify a person&#39;s location, or an ear clip (e.g., a headset as described below) affixed to an ear may be used to record pulse or blood oxygen saturation levels. Additionally, a sensor implemented with a screen on mobile computing device  116  may be used to read a user&#39;s temperature or obtain a biometric signature while a user is interacting with data. A further example may include using data that is observed on computer  120  or laptop  122  that provides information as to a user&#39;s online behavior and the type of content that she is viewing, which may be used by bands  104 - 112 . Regardless of the type or location of sensor used, data may be transferred to bands  104 - 112  by using, for example, an analog audio jack, digital adapter (e.g., USB, mini-USB), or other, without limitation, plug, or other type of connector that may be used to physically couple bands  104 - 112  to another device or system for transferring data and, in some examples, to provide power to recharge a battery (not shown). Alternatively, a wireless data communication interface or facility (e.g., a wireless radio that is configured to communicate data from bands  104 - 112  using one or more data communication protocols (e.g., IEEE 802.11a/b/g/n (WiFi), WiMax, ANT™, ZigBee®, Bluetooth®, Near Field Communications (“NFC”), and others)) may be used to receive or transfer data. Further, bands  104 - 112  may be configured to analyze, evaluate, modify, or otherwise use data gathered, either directly or indirectly. 
         [0036]    In some examples, bands  104 - 112  may be configured to share data with each other or with an intermediary facility, such as a database, website, web service, or the like, which may be implemented by server  114 . In some embodiments, server  114  can be operated by a third party providing, for example, social media-related services. Bands  104 - 112  and other related devices may exchange data with each other directly, or bands  104 - 112  may exchange data via a third party server, such as a third party like Facebook®, to provide social-media related services. Examples of other third party servers include those implemented by social networking services, including, but not limited to, services such as Yahoo! IM™, GTalk™, MSN Messenger™, Twitter® and other private or public social networks. The exchanged data may include personal physiological data and data derived from sensory-based user interfaces (“UI”). Server  114 , in some examples, may be implemented using one or more processor-based computing devices or networks, including computing clouds, storage area networks (“SAN”), or the like. As shown, bands  104 - 112  may be used as a personal data or area network (e.g., “PDN” or “PAN”) in which data relevant to a given user or band (e.g., one or more of bands  104 - 112 ) may be shared. As shown here, bands  104  and  112  may be configured to exchange data with each other over network  102  or indirectly using server  114 . Users of bands  104  and  112  may direct a web browser hosted on a computer (e.g., computer  120 , laptop  122 , or the like) in order to access, view, modify, or perform other operations with data captured by bands  104  and  112 . For example, two runners using bands  104  and  112  may be geographically remote (e.g., users are not geographically in close proximity locally such that bands being used by each user are in direct data communication), but wish to share data regarding their race times (pre, post, or in-race), personal records (i.e., “PR”), target split times, results, performance characteristics (e.g., target heart rate, target VO2 max, and others), and other information. If both runners (i.e., bands  104  and  112 ) are engaged in a race on the same day, data can be gathered for comparative analysis and other uses. Further, data can be shared in substantially real-time (taking into account any latencies incurred by data transfer rates, network topologies, or other data network factors) as well as uploaded after a given activity or event has been performed. In other words, data can be captured by the user as it is worn and configured to transfer data using, for example, a wireless network connection (e.g., a wireless network interface card, wireless local area network (“LAN”) card, cell phone, or the like). Data may also be shared in a temporally asynchronous manner in which a wired data connection (e.g., an analog audio plug (and associated software or firmware) configured to transfer digitally encoded data to encoded audio data that may be transferred between bands  104 - 112  and a plug configured to receive, encode/decode, and process data exchanged) may be used to transfer data from one or more bands  104 - 112  to various destinations (e.g., another of bands  104 - 112 , server  114 ; mobile computing device  116 , mobile communications device  118 , computer  120 , laptop  122 , and distributed sensor  124 ). Bands  104 - 112  may be implemented with various types of wired and/or wireless communication facilities and are not intended to be limited to any specific technology. For example, data may be transferred from bands  104 - 112  using an analog audio plug (e.g., TRRS, TRS, or others). In other examples, wireless communication facilities using various types of data communication protocols (e.g., WiFi, Bluetooth®, ZigBee®, ANT™, and others) may be implemented as part of bands  104 - 112 , which may include circuitry, firmware, hardware, radios, antennas, processors, microprocessors, memories, or other electrical, electronic, mechanical, or physical elements configured to enable data communication capabilities of various types and characteristics. 
         [0037]    As data-capable devices, bands  104 - 112  may be configured to collect data from a wide range of sources, including onboard (not shown) and distributed sensors (e.g., server  114 , mobile computing device  116 , mobile communications device  118 , computer  120 , laptop  122 , and distributed sensor  124 ) or other bands. Some or all data captured may be personal, sensitive, or confidential and various techniques for providing secure storage and access may be implemented. For example, various types of security protocols and algorithms may be used to encode data stored or accessed by bands  104 - 112 . Examples of security protocols and algorithms include authentication, encryption, encoding, private and public key infrastructure, passwords, checksums, hash codes and hash functions (e.g., SHA, SHA-1, MD-5, and the like), or others may be used to prevent undesired access to data captured by bands  104 - 112 . In other examples, data security for bands  104 - 112  may be implemented differently. 
         [0038]    Bands  104 - 112  may be used as personal wearable, data capture devices that, when worn, are configured to identify a specific, individual user. By evaluating captured data such as motion data from an accelerometer, biometric data such as heart rate, skin galvanic response, and other biometric data, and using long-term analysis techniques (e.g., software packages or modules of any type, without limitation), a user may have a unique pattern of behavior or motion and/or biometric responses that can be used as a signature for identification. For example, bands  104 - 112  may gather data regarding an individual person&#39;s gait or other unique biometric, physiological or behavioral characteristics. Using, for example, distributed sensor  124 , a biometric signature (e.g., fingerprint, retinal or iris vascular pattern, or others) may be gathered and transmitted to bands  104 - 112  that, when combined with other data, determines that a given user has been properly identified and, as such, authenticated. When bands  104 - 112  are worn, a user may be identified and authenticated to enable a variety of other functions such as accessing or modifying data, enabling wired or wireless data transmission facilities (i.e., allowing the transfer of data from bands  104 - 112 ), modifying functionality or functions of bands  104 - 112 , authenticating financial transactions using stored data and information (e.g., credit card, PIN, card security numbers, and the like), running applications that allow for various operations to be performed (e.g., controlling physical security and access by transmitting a security code to a reader that, when authenticated, unlocks a door by turning off current to an electromagnetic lock, and others), and others. Different functions and operations beyond those described may be performed using bands  104 - 112 , which can act as secure, personal, wearable, data-capable devices. The number, type, function, configuration, specifications, structure, or other features of system  100  and the above-described elements may be varied and are not limited to the examples provided. 
         [0039]      FIG. 2  illustrates a block diagram of an exemplary data-capable band. Here, band  200  includes bus  202 , processor  204 , memory  206 , notification facility  208 , accelerometer  210 , sensor  212 , battery  214 , and communications facility  216 . In some examples, the quantity, type, function, structure, and configuration of band  200  and the elements (e.g., bus  202 , processor  204 , memory  206 , notification facility  208 , accelerometer  210 , sensor  212 , battery  214 , and communications facility  216 ) shown may be varied and are not limited to the examples provided. As shown, processor  204  may be implemented as logic to provide control functions and signals to memory  206 , notification facility  208 , accelerometer  210 , sensor  212 , battery  214 , and communications facility  216 . Processor  204  may be implemented using any type of processor or microprocessor suitable for packaging within bands  104 - 112  ( FIG. 1 ). Various types of microprocessors may be used to provide data processing capabilities for band  200  and are not limited to any specific type or capability. For example, a MSP430F5528-type microprocessor manufactured by Texas Instruments of Dallas, Tex. may be configured for data communication using audio tones and enabling the use of an audio plug-and-jack system (e.g., TRRS, TRS, or others) for transferring data captured by band  200 . Further, different processors may be desired if other functionality (e.g., the type and number of sensors (e.g., sensor  212 )) are varied. Data processed by processor  204  may be stored using, for example, memory  206 . 
         [0040]    In some examples, memory  206  may be implemented using various types of data storage technologies and standards, including, without limitation, read-only memory (“ROM”), random access memory (“RAM”), dynamic random access memory (“DRAM”), static random access memory (“SRAM”), static/dynamic random access memory (“SDRAM”), magnetic random access memory (“MRAM”), solid state, two and three-dimensional memories, Flash®, and others. Memory  206  may also be implemented using one or more partitions that are configured for multiple types of data storage technologies to allow for non-modifiable (i.e., by a user) software to be installed (e.g., firmware installed on ROM) while also providing for storage of captured data and applications using, for example, RAM. Once captured and/or stored in memory  206 , data may be subjected to various operations performed by other elements of band  200 . 
         [0041]    Notification facility  208 , in some examples, may be implemented to provide vibratory energy, audio or visual signals, communicated through band  200 . As used herein, “facility” refers to any, some, or all of the features and structures that are used to implement a given set of functions. In some examples, the vibratory energy may be implemented using a motor or other mechanical structure. In some examples, the audio signal may be a tone or other audio cue, or it may be implemented using different sounds for different purposes. The audio signals may be emitted directly using notification facility  208 , or indirectly by transmission via communications facility  216  to other audio-capable devices (e.g., headphones (not shown), a headset (as described below with regard to  FIG. 12 ), mobile computing device  116 , mobile communications device  118 , computer  120 , laptop  122 , distributed sensor  124 , etc.). In some examples, the visual signal may be implemented using any available display technology, such as lights, light-emitting diodes (LEDs), interferometric modulator display (IMOD), electrophoretic ink (E Ink), organic light-emitting diode (OLED), or other display technologies. As an example, an application stored on memory  206  may be configured to monitor a clock signal from processor  204  in order to provide timekeeping functions to band  200 . For example, if an alarm is set for a desired time, notification facility  208  may be used to provide a vibration or an audio tone, or a series of vibrations or audio tones, when the desired time occurs. As another example, notification facility  208  may be coupled to a framework (not shown) or other structure that is used to translate or communicate vibratory energy throughout the physical structure of band  200 . In other examples, notification facility  208  may be implemented differently. 
         [0042]    Power may be stored in battery  214 , which may be implemented as a battery, battery module, power management module, or the like. Power may also be gathered from local power sources such as solar panels, thermo-electric generators, and kinetic energy generators, among others that are alternatives power sources to external power for a battery. These additional sources can either power the system directly or can charge a battery, which, in turn, is used to power the system (e.g., of a band). In other words, battery  214  may include a rechargeable, expendable, replaceable, or other type of battery, but also circuitry, hardware, or software that may be used in connection with in lieu of processor  204  in order to provide power management, charge/recharging, sleep, or other functions. Further, battery  214  may be implemented using various types of battery technologies, including Lithium Ion (“LI”), Nickel Metal Hydride (“NiMH”), or others, without limitation. Power drawn as electrical current may be distributed from battery via bus  202 , the latter of which may be implemented as deposited or formed circuitry or using other forms of circuits or cabling, including flexible circuitry. Electrical current distributed from battery  204  and managed by processor  204  may be used by one or more of memory  206 , notification facility  208 , accelerometer  210 , sensor  212 , or communications facility  216 . 
         [0043]    As shown, various sensors may be used as input sources for data captured by band  200 . For example, accelerometer  210  may be used to gather data measured across one, two, or three axes of motion. In addition to accelerometer  210 , other sensors (i.e., sensor  212 ) may be implemented to provide temperature, environmental, physical, chemical, electrical, or other types of sensed inputs. As presented here, sensor  212  may include one or multiple sensors and is not intended to be limiting as to the quantity or type of sensor implemented. Data captured by band  200  using accelerometer  210  and sensor  212  or data requested from another source (i.e., outside of band  200 ) may also be exchanged, transferred, or otherwise communicated using communications facility  216 . For example, communications facility  216  may include a wireless radio, control circuit or logic, antenna, transceiver, receiver, transmitter, resistors, diodes, transistors, or other elements that are used to transmit and receive data from band  200 . In some examples, communications facility  216  may be implemented to provide a “wired” data communication capability such as an analog or digital attachment, plug, jack, or the like to allow for data to be transferred. In other examples, communications facility  216  may be implemented to provide a wireless data communication capability to transmit digitally encoded data across one or more frequencies using various types of data communication protocols, without limitation. In still other examples, band  200  and the above-described elements may be varied in function, structure, configuration, or implementation and are not limited to those shown and described. 
         [0044]      FIG. 3  illustrates sensors for use with an exemplary data-capable band. Sensor  212  may be implemented using various types of sensors, some of which are shown. Like-numbered and named elements may describe the same or substantially similar element as those shown in other descriptions. Here, sensor  212  ( FIG. 2 ) may be implemented as accelerometer  302 , altimeter/barometer  304 , light/infrared (“IR”) sensor  306 , pulse/heart rate (“HR”) monitor  308 , audio sensor (e.g., microphone, transducer, or others)  310 , pedometer  312 , velocimeter  314 , GPS receiver  316 , location-based service sensor (e.g., sensor for determining location within a cellular or micro-cellular network, which may or may not use GPS or other satellite constellations for fixing a position)  318 , motion detection sensor  320 , environmental sensor  322 , chemical sensor  324 , electrical sensor  326 , or mechanical sensor  328 . 
         [0045]    As shown, accelerometer  302  may be used to capture data associated with motion detection along 1, 2, or 3-axes of measurement, without limitation to any specific type of specification of sensor. Accelerometer  302  may also be implemented to measure various types of user motion and may be configured based on the type of sensor, firmware, software, hardware, or circuitry used. As another example, altimeter/barometer  304  may be used to measure environment pressure, atmospheric or otherwise, and is not limited to any specification or type of pressure-reading device. In some examples, altimeter/barometer  304  may be an altimeter, a barometer, or a combination thereof. For example, altimeter/barometer  304  may be implemented as an altimeter for measuring above ground level (“AGL”) pressure in band  200 , which has been configured for use by naval or military aviators. As another example, altimeter/barometer  304  may be implemented as a barometer for reading atmospheric pressure for marine-based applications. In other examples, altimeter/barometer  304  may be implemented differently. 
         [0046]    Other types of sensors that may be used to measure light or photonic conditions include light/IR sensor  306 , motion detection sensor  320 , and environmental sensor  322 , the latter of which may include any type of sensor for capturing data associated with environmental conditions beyond light. Further, motion detection sensor  320  may be configured to detect motion using a variety of techniques and technologies, including, but not limited to comparative or differential light analysis (e.g., comparing foreground and background lighting), sound monitoring, or others. Audio sensor  310  may be implemented using any type of device configured to record or capture sound. 
         [0047]    In some examples, pedometer  312  may be implemented using devices to measure various types of data associated with pedestrian-oriented activities such as running or walking. Footstrikes, stride length, stride length or interval, time, and other data may be measured. Velocimeter  314  may be implemented, in some examples, to measure velocity (e.g., speed and directional vectors) without limitation to any particular activity. Further, additional sensors that may be used as sensor  212  include those configured to identify or obtain location-based data. For example, GPS receiver  316  may be used to obtain coordinates of the geographic location of band  200  using, for example, various types of signals transmitted by civilian and/or military satellite constellations in low, medium, or high earth orbit (e.g., “LEO,” “MEO,” or “GEO”). In other examples, differential GPS algorithms may also be implemented with GPS receiver  316 , which may be used to generate more precise or accurate coordinates. Still further, location-based services sensor  318  may be implemented to obtain location-based data including, but not limited to location, nearby services or items of interest, and the like. As an example, location-based services sensor  318  may be configured to detect an electronic signal, encoded or otherwise, that provides information regarding a physical locale as band  200  passes. The electronic signal may include, in some examples, encoded data regarding the location and information associated therewith. Electrical sensor  326  and mechanical sensor  328  may be configured to include other types (e.g., haptic, kinetic, piezoelectric, piezomechanical, pressure, touch, thermal, and others) of sensors for data input to band  200 , without limitation. Other types of sensors apart from those shown may also be used, including magnetic flux sensors such as solid-state compasses and the like, including gyroscopic sensors. While the present illustration provides numerous examples of types of sensors that may be used with band  200  ( FIG. 2 ), others not shown or described may be implemented with or as a substitute for any sensor shown or described. 
         [0048]      FIG. 4  illustrates an application architecture for an exemplary data-capable band. Here, application architecture  400  includes bus  402 , logic module  404 , communications module  406 , security module  408 , interface module  410 , data management  412 , audio module  414 , motor controller  416 , service management module  418 , sensor input evaluation module  420 , and power management module  422 . In some examples, application architecture  400  and the above-listed elements (e.g., bus  402 , logic module  404 , communications module  406 , security module  408 , interface module  410 , data management  412 , audio module  414 , motor controller  416 , service management module  418 , sensor input evaluation module  420 , and power management module  422 ) may be implemented as software using various computer programming and formatting languages such as Java, C++, C, and others. As shown here, logic module  404  may be firmware or application software that is installed in memory  206  ( FIG. 2 ) and executed by processor  204  ( FIG. 2 ). Included with logic module  404  may be program instructions or code (e.g., source, object, binary executables, or others) that, when initiated, called, or instantiated, perform various functions. 
         [0049]    For example, logic module  404  may be configured to send control signals to communications module  406  in order to transfer, transmit, or receive data stored in memory  206 , the latter of which may be managed by a database management system (“DBMS”) or utility in data management module  412 . As another example, security module  408  may be controlled by logic module  404  to provide encoding, decoding, encryption, authentication, or other functions to band  200  ( FIG. 2 ). Alternatively, security module  408  may also be implemented as an application that, using data captured from various sensors and stored in memory  206  (and accessed by data management module  412 ) may be used to provide identification functions that enable band  200  to passively identify a user or wearer of band  200 . Still further, various types of security software and applications may be used and are not limited to those shown and described. 
         [0050]    interface module  410 , in some examples, may be used to manage user interface controls such as switches, buttons, or other types of controls that enable a user to manage various functions of band  200 . For example, a 4-position switch may be turned to a given position that is interpreted by interface module  410  to determine the proper signal or feedback to send to logic module  404  in order to generate a particular result. In other examples, a button (not shown) may be depressed that allows a user to trigger or initiate certain actions by sending another signal to logic module  404 . Still further, interface module  410  may be used to interpret data from, for example, accelerometer  210  ( FIG. 2 ) to identify specific movement or motion that initiates or triggers a given response. In other examples, interface module  410  may be used to manage different types of displays (e.g., LED, IMOD, E Ink, OLED, etc.). In other examples, interface module  410  may be implemented differently in function, structure, or configuration and is not limited to those shown and described. 
         [0051]    As shown, audio module  414  may be configured to manage encoded or unencoded data gathered from various types of audio sensors. In some examples, audio module  414  may include one or more codecs that are used to encode or decode various types of audio waveforms. For example, analog audio input may be encoded by audio module  414  and, once encoded, sent as a signal or collection of data packets, messages, segments, frames, or the like to logic module  404  for transmission via communications module  406 . In other examples, audio module  414  may be implemented differently in function, structure, configuration, or implementation and is not limited to those shown and described. Other elements that may be used by band  200  include motor controller  416 , which may be firmware or an application to control a motor or other vibratory energy source (e.g., notification facility  208  ( FIG. 2 )). Power used for band  200  may be drawn from battery  214  ( FIG. 2 ) and managed by power management module  422 , which may be firmware or an application used to manage, with or without user input, how power is consumer, conserved, or otherwise used by band  200  and the above-described elements, including one or more sensors (e.g., sensor  212  ( FIG. 2 ), sensors  302 - 328  ( FIG. 3 )). With regard to data captured, sensor input evaluation module  420  may be a software engine or module that is used to evaluate and analyze data received from one or more inputs (e.g., sensors  302 - 328 ) to band  200 . When received, data may be analyzed by sensor input evaluation module  420 , which may include custom or “off-the-shelf” analytics packages that are configured to provide application-specific analysis of data to determine trends, patterns, and other useful information. In other examples, sensor input module  420  may also include firmware or software that enables the generation of various types and formats of reports for presenting data and any analysis performed thereupon. 
         [0052]    Another element of application architecture  400  that may be included is service management module  418 . In some examples, service management module  418  may be firmware, software, or an application that is configured to manage various aspects and operations associated with executing software-related instructions for band  200 . For example, libraries or classes that are used by software or applications on band  200  may be served from an online or networked source. Service management module  418  may be implemented to manage how and when these services are invoked in order to ensure that desired applications are executed properly within application architecture  400 . As discrete sets, collections, or groupings of functions, services used by band  200  for various purposes ranging from communications to operating systems to call or document libraries may be managed by service management module  418 . Alternatively, service management module  418  may be implemented differently and is not limited to the examples provided herein. Further, application architecture  400  is an example of a software/system/application-level architecture that may be used to implement various software-related aspects of band  200  and may be varied in the quantity, type, configuration, function, structure, or type of programming or formatting languages used, without limitation to any given example. 
         [0053]      FIG. 5A  illustrates representative data types for use with an exemplary data-capable band. Here, wearable device  502  may capture various types of data, including, but not limited to sensor data  504 , manually-entered data  506 , application data  508 , location data  510 , network data  512 , system/operating data  514 , and user data  516 . Various types of data may be captured from sensors, such as those described above in connection with  FIG. 3 . Manually-entered data, in some examples, may be data or inputs received directly and locally by band  200  ( FIG. 2 ). In other examples, manually-entered data may also be provided through a third-party website that stores the data in a database and may be synchronized from server  114  ( FIG. 1 ) with one or more of bands  104 - 112 . Other types of data that may be captured including application data  508  and system/operating data  514 , which may be associated with firmware, software, or hardware installed or implemented on band  200 . Further, location data  510  may be used by wearable device  502 , as described above. User data  516 , in some examples, may be data that include profile data, preferences, rules, or other information that has been previously entered by a given user of wearable device  502 . Further, network data  512  may be data is captured by wearable device with regard to routing tables, data paths, network or access availability (e.g., wireless network access availability), and the like. Other types of data may be captured by wearable device  502  and are not limited to the examples shown and described. Additional context-specific examples of types of data captured by bands  104 - 112  ( FIG. 1 ) are provided below. 
         [0054]      FIG. 5B  illustrates representative data types for use with an exemplary data-capable band in fitness-related activities. Here, band  519  may be configured to capture types (i.e., categories) of data such as heart rate/pulse monitoring data  520 , blood oxygen saturation data  522 , skin temperature data  524 , salinity/emission/outgassing data  526 , location/GPS data  528 , environmental data  530 , and accelerometer data  532 . As an example, a runner may use or wear band  519  to obtain data associated with his physiological condition (i.e., heart rate/pulse monitoring data  520 , skin temperature, salinity/emission/outgassing data  526 , among others), athletic efficiency (i.e., blood oxygen saturation data  522 ), and performance (i.e., location/GPS data  528  (e.g., distance or laps run), environmental data  530  (e.g., ambient temperature, humidity, pressure, and the like), accelerometer  532  (e.g., biomechanical information, including gait, stride, stride length, among others)). Other or different types of data may be captured by band  519 , but the above-described examples are illustrative of some types of data that may be captured by band  519 . Further, data captured may be uploaded to a website or online/networked destination for storage and other uses. For example, fitness-related data may be used by applications that are downloaded from a “fitness marketplace” or “wellness marketplace,” where athletes, or other users, may find, purchase, or download applications, products, information, etc., for various uses, as well as share information with other users. Some applications may be activity-specific and thus may be used to modify or alter the data capture capabilities of band  519  accordingly. For example, a fitness marketplace may be a website accessible by various types of mobile and non-mobile clients to locate applications for different exercise or fitness categories such as running, swimming, tennis, golf, baseball, football, fencing, and many others. When downloaded, applications from a fitness marketplace may also be used with user-specific accounts to manage the retrieved applications as well as usage with band  519 , or to use the data to provide services such as online personal coaching or targeted advertisements. More, fewer, or different types of data may be captured for fitness-related activities. 
         [0055]    In some examples, applications may be developed using various types of schema, including using a software development kit or providing requirements in a proprietary or open source software development regime. Applications may also be developed by using an application programming interface to an application marketplace in order for developers to design and build applications that can be downloaded on wearable devices (e.g., bands  104 - 106  ( FIG. 1 )). Alternatively, application can be developed for download and installation on devices that may be in data communication over a shared data link or network connection, wired or wireless. For example, an application may be downloaded onto mobile computing device  116  ( FIG. 1 ) from server  114  ( FIG. 1 ), which may then be installed and executed using data gathered from one or more sensors on band  104 . Analysis, evaluation, or other operations performed on data gathered by an application downloaded from server  114  may be presented (i.e., displayed) on a graphical user interface (e.g., a micro web browser, WAP web browser, Java/Java-script-based web browser, and others, without limitation) on mobile computing device  116  or any other type of client. Users may, in some examples, search, find, retrieve, download, purchase, or otherwise obtain applications for various types of purposes from an application marketplace. Applications may be configured for various types of purposes and categories, without limitation. Examples of types of purposes include running, swimming, trail running, diabetic management, dietary, weight management, sleep management, caloric burn rate tracking, activity tracking, and others, without limitation. Examples of categories of applications may include fitness, wellness, health, medical, and others, without limitation. In other examples, applications for distribution via a marketplace or other download website or source may be implemented differently and is not limited to those described. 
         [0056]      FIG. 5C  illustrates representative data types for use with an exemplary data-capable band in sleep management activities. Here, band  539  may be used for sleep management purposes to track various types of data, including heart rate monitoring data  540 , motion sensor data  542 , accelerometer data  544 , skin resistivity data  546 , user input data  548 , clock data  550 , and audio data  552 . In some examples, heart rate monitor data  540  may be captured to evaluate rest, waking, or various states of sleep. Motion sensor data  542  and accelerometer data  544  may be used to determine whether a user of band  539  is experiencing a restful or fitful sleep. For example, some motion sensor data  542  may be captured by a light sensor that measures ambient or differential light patterns in order to determine whether a user is sleeping on her front, side, or back. Accelerometer data  544  may also be captured to determine whether a user is experiencing gentle or violent disruptions when sleeping, such as those often found in afflictions of sleep apnea or other sleep disorders. Further, skin resistivity data  546  may be captured to determine whether a user is ill (e.g., running a temperature, sweating, experiencing chills, clammy skin, and others). Still further, user input data may include data input by a user as to how and whether band  539  should trigger notification facility  208  ( FIG. 2 ) to wake a user at a given time or whether to use a series of increasing or decreasing vibrations or audio tones to trigger a waking state. Clock data ( 550 ) may be used to measure the duration of sleep or a finite period of time in which a user is at rest. Audio data may also be captured to determine whether a user is snoring and, if so, the frequencies and amplitude therein may suggest physical conditions that a user may be interested in knowing (e.g., snoring, breathing interruptions, talking in one&#39;s sleep, and the like). More, fewer, or different types of data may be captured for sleep management-related activities. 
         [0057]      FIG. 5D  illustrates representative data types for use with an exemplary data-capable band in medical-related activities. Here, band  539  may also be configured for medical purposes and related-types of data such as heart rate monitoring data  560 , respiratory monitoring data  562 , body temperature data  564 , blood sugar data  566 , chemical protein/analysis data  568 , patient medical records data  570 , and healthcare professional (e.g., doctor, physician, registered nurse, physician&#39;s assistant, dentist, orthopedist, surgeon, and others) data  572 . In some examples, data may be captured by band  539  directly from wear by a user. For example, band  539  may be able to sample and analyze sweat through a salinity or moisture detector to identify whether any particular chemicals, proteins, hormones, or other organic or inorganic compounds are present, which can be analyzed by band  539  or communicated to server  114  to perform further analysis. If sent to server  114 , further analyses may be performed by a hospital or other medical facility using data captured by band  539 . In other examples, more, fewer, or different types of data may be captured for medical-related activities. 
         [0058]      FIG. 5E  illustrates representative data types for use with an exemplary data-capable band in social media/networking-related activities. Examples of social media/networking-related activities include activities related to Internet-based Social Networking Services (“SNS”), such as Facebook®, Twitter®, etc. Here, band  519 , shown with an audio data plug, may be configured to capture data for use with various types of social media and networking-related services, websites, and activities. Accelerometer data  580 , manual data  582 , other user/friends data  584 , location data  586 , network data  588 , clock/timer data  590 , and environmental data  592  are examples of data that may be gathered and shared by, for example, uploading data from band  519  using, for example, an audio plug such as those described herein. As another example, accelerometer data  580  may be captured and shared with other users to share motion, activity, or other movement-oriented data. Manual data  582  may be data that a given user also wishes to share with other users. Likewise, other user/friends data  584  may be from other bands (not shown) that can be shared or aggregated with data captured by band  519 . Location data  586  for band  519  may also be shared with other users. In other examples, a user may also enter manual data  582  to prevent other users or friends from receiving updated location data from band  519 . Additionally, network data  588  and clock/timer data may be captured and shared with other users to indicate, for example, activities or events that a given user (i.e., wearing band  519 ) was engaged at certain locations. Further, if a user of band  519  has friends who are not geographically located in close or near proximity (e.g., the user of band  519  is located in San Francisco and her friend is located in Rome), environmental data can be captured by band  519  (e.g., weather, temperature, humidity, sunny or overcast (as interpreted from data captured by a light sensor and combined with captured data for humidity and temperature), among others). In other examples, more, fewer, or different types of data may be captured for medical-related activities. 
         [0059]      FIG. 6  illustrates an exemplary communications device system implemented with multiple exemplary data-capable bands. The exemplary system  600  shows exemplary lines of communication between some of the devices shown in  FIG. 1 , including network  102 , bands  104 - 110 , mobile communications device  118 , and laptop  122 . In  FIG. 6 , examples of both peer-to-peer communication and peer-to-hub communication using bands  104 - 110  are shown. Using these avenues of communication, bands worn by multiple users or wearers (the term “wearer” is used herein to describe a user that is wearing one or more bands) may monitor and compare physical, emotional, mental states among wearers (e.g., physical competitions, sleep pattern comparisons, resting physical states, etc.). 
         [0060]    Peer-to-hub communication may be exemplified by bands  104  and  108 , each respectively communicating with mobile communications device  118  or laptop  122 , exemplary hub devices. Bands  104  and  108  may communicate with mobile communications device  118  or laptop  122  using any number of known wired communication technologies (e.g., Universal Service Bus (USB) connections, TRS/TRRS connections, telephone networks, fiber-optic networks, cable networks, etc.). In some examples, bands  104  and  108  may be implemented as lower power or lower energy devices, in which case mobile communications device  118 , laptop  122  or other hub devices may act as a gateway to route the data from bands  104  and  108  to software applications on the hub device, or to other devices. For example, mobile communications device  118  may comprise both wired and wireless communication capabilities, and thereby act as a hub to further communicate data received from band  104  to band  110 , network  102  or laptop  122 , among other devices. Mobile communications device  118  also may comprise software applications that interact with social or professional networking services (“SNS”) (e.g., Facebook®, Twitter®, LinkedIn®, etc.), for example via network  102 , and thereby act also as a hub to further share data received from band  104  with other users of the SNS. Band  104  may communicate with laptop  122 , which also may comprise both wired and wireless communication capabilities, and thereby act as a hub to further communicate data received from band  104  to, for example, network  102  or laptop  122 , among other devices. Laptop  122  also may comprise software applications that interact with SNS, for example via network  102 , and thereby act also as a hub to further share data received from band  104  with other users of the SNS. The software applications on mobile communications device  118  or laptop  122  or other hub devices may further process or analyze the data they receive from bands  104  and  108  in order to present to the wearer, or to other wearers or users of the SNS, useful information associated with the wearer&#39;s activities. 
         [0061]    In other examples, bands  106  and  110  may also participate in peer-to-hub communications with exemplary hub devices such as mobile communications device  118  and laptop  122 . Bands  106  and  110  may communicate with mobile communications device  118  and laptop  122  using any number of wireless communication technologies (e.g., local wireless network, near field communication, Bluetooth®, Bluetooth® low energy, ANT, etc.). Using wireless communication technologies, mobile communications device  118  and laptop  122  may be used as a hub or gateway device to communicate data captured by bands  106  and  110  with other devices, in the same way as described above with respect to bands  104  and  108 . Mobile communications device  118  and laptop  122  also may be used as a hub or gateway device to further share data captured by bands  106  and  110  with SNS, in the same way as described above with respect to bands  104  and  108 . 
         [0062]    Peer-to-peer communication may be exemplified by bands  106  and  110 , exemplary peer devices, communicating directly. Band  106  may communicate directly with band  110 , and vice versa, using known wireless communication technologies, as described above. Peer-to-peer communication may also be exemplified by communications between bands  104  and  108  and bands  106  and  110  through a hub device, such as mobile communications device  118  or laptop  122 . 
         [0063]    Alternatively, exemplary system  600  may be implemented with any combination of communication capable devices, such as any of the devices depicted in  FIG. 1 , communicating with each other using any communication platform, including any of the platforms described above. Persons of ordinary skill in the art will appreciate that the examples of peer-to-hub communication provided herein, and shown in  FIG. 6 , are only a small subset of the possible implementations of peer-to-hub communications involving the bands described herein. 
         [0064]      FIG. 7  illustrates an exemplary wellness tracking system for use with or within a distributed wellness application. System  700  comprises aggregation engine  710 , conversion module  720 , band  730 , band  732 , textual input  734 , other input  736 , and graphical representation  740 . Bands  730  and  732  may be implemented as described above. In some examples, aggregation engine  710  may receive input from various sources. For example, aggregation engine  710  may receive sensory input from band  730 , band  732 , and/or other data-capable bands. This sensory input may include any of the above-described sensory data that may be gathered by data-capable bands. In other examples, aggregation engine  710  may receive other (e.g., manual) input from textual input  734  or other input  736 . Textual input  734  and other input  736  may include information that a user types, uploads, or otherwise inputs into an application (e.g., a web application, an iPhone® application, etc.) implemented on any of the data and communications capable devices referenced herein (e.g., computer, laptop, computer, mobile communications device, mobile computing device, etc.). In some examples, aggregation engine  720  may be configured to process (e.g., interpret) the data and information received from band  730 , band  732 , textual input  734  and other input  736 , to determine an aggregate value from which graphical representation  740  may be generated. In an example, system  700  may comprise a conversion module  720 , which may be configured to perform calculations to convert the data received from band  730 , band  732 , textual input  734  and other input  736  into values (e.g., numeric values). Those values may then be aggregated by aggregation engine  710  to generate graphical representation  740 . Conversion module  720  may be implemented as part of aggregation engine  710  (as shown), or it may be implemented separately (not shown). In some examples, aggregation engine  710  may be implemented with more or different modules. In other examples, aggregation engine  710  may be implemented with fewer or more input sources. In some examples, graphical representation  740  may be implemented differently, using different facial expressions, or any image or graphic according to any intuitive or predetermined set of graphics indicating various levels and/or aspects of wellness. As described in more detail below, graphical representation  740  may be a richer display comprising more than a single graphic or image (e.g.,  FIGS. 10 and 11 ). 
         [0065]    In some examples, aggregation engine  710  may receive or gather inputs from one or more sources over a period of time, or over multiple periods of time, and organize those inputs into a database (not shown) or other type of organized form of information storage. In some examples, graphical representation  740  may be a simple representation of a facial expression, as shown. In other examples, graphical representation  740  may be implemented as a richer graphical display comprising inputs gathered over time (e.g.,  FIGS. 10 and 11  below). 
         [0066]      FIG. 8  illustrates representative calculations executed by an exemplary conversion module to determine an aggregate value for producing a graphical representation of a user&#39;s wellness. In some examples, conversion module  820  may be configured to process data associated with exercise, data associated with sleep, data associated with eating or food intake, and data associated with other miscellaneous activity data (e.g., sending a message to a friend, gifting to a friend, donating, receiving gifts, etc.), and generate values from the data. For example, conversion module  820  may perform calculations using data associated with activities (“activity data”) to generate values for types of exercise (e.g., walking, vigorous exercise, not enough exercise, etc.) ( 810 ), types of sleep (e.g., deep sleep, no sleep, not enough deep sleep, etc.) ( 812 ), types of meals (e.g., a sluggish/heavy meal, a good meal, an energizing meal, etc.) ( 814 ), or other miscellaneous activities (e.g., sending a message to a friend, gifting to a friend, donating, receiving gifts, etc.) ( 816 ). In some implementations, these values may include positive values for activities that are beneficial to a user&#39;s wellness and negative values for activities that are detrimental to a user&#39;s wellness, or for lack of activity (e.g., not enough sleep, too many minutes without exercise, etc.). In one example, the values may be calculated using a reference activity. For example, conversion module  820  may equate a step to the numerical value 0.0001, and then equate various other activities to a number of steps ( 810 ,  812 ,  814 ,  816 ). Note that while in this example types of sleep  812 , types of meals  814 , and miscellaneous activities  816  are expressed in numbers of steps,  FIG. 8  is not intended to be limiting is one of numerous ways in which to express types of sleep  812 , types of meals  814 , and miscellaneous activities  816 . For example, types of sleep  812 , types of meals  814 , and miscellaneous activities  816  can correspond to different point values of which one or more scores can be derived to determine aggregate value  830 . Similarly, aggregate value  830  can be expressed in terms of points or a score. In some examples, these values may be Weighted according to the quality of the activity. For example, each minute of deep sleep equals a higher number of steps than each minute of other sleep ( 812 ). As described in more detail below ( FIGS. 10 and 11 ), these values may be modulated by time. For example, positive values for exercise may be modulated by negative values for extended time periods without exercise ( 810 ). In another example, positive values for sleep or deep sleep may be modulated by time without sleep or not enough time spent in deep sleep ( 812 ). In some examples, conversion module  820  is configured to aggregate these values to generate an aggregate value  830 . In some examples, aggregate value  830  may be used by an aggregation engine (e.g., aggregation engine  710  described above) to generate a graphical representation of a user&#39;s wellness (e.g., graphical representation  740  described above,  FIGS. 10 and 11  described below, or others). 
         [0067]      FIG. 9  illustrates an exemplary process for generating and displaying a graphical representation of a user&#39;s wellness based upon the user&#39;s activities. Process  900  may be implemented as an exemplary process for creating and presenting a graphical representation of a user&#39;s wellness. In some examples, process  900  may begin with receiving activity data from a source ( 902 ). For example, the source may comprise one of the data-capable bands described herein (e.g., band  730 , band  732 , etc.). In another example, the source may comprise another type of data and communications capable device, such as those described above (e.g., computer, laptop, computer, mobile communications device, mobile computing device, etc.), which may enable a user to provide activity data via various inputs (e.g., textual input  734 , other input  736 , etc.). For example, activity data may be received from a data-capable band. In another example, activity data may be received from data manually input using an application user interface via a mobile communications device or a laptop. In other examples, activity data may be received from sources or combinations of sources. After receiving the activity data, another activity data is received from another source ( 904 ). The another source also may be any of the types of sources described above. Once received, the activity data from the source, and the another activity data from another source, is then used to determine (e.g., by conversion module  720  or  730 , etc.) an aggregate value ( 906 ). Once determined, the aggregate value is used to generate a graphical representation of a user&#39;s present wellness ( 908 ) (e.g., graphical representation  740  described above, etc.). The aggregate value also may be combined with other information, of the same type or different, to generate a richer graphical representation (e.g.,  FIGS. 10 and 11  described below, etc.). 
         [0068]    In other examples, activity data may be received from multiple sources. These multiple sources may comprise a combination of sources (e.g., a band and a mobile communications device, two bands and a laptop, etc.) (not shown). Such activity data may be accumulated continuously, periodically, or otherwise, over a time period. As activity data is accumulated, the aggregate value may be updated and/or accumulated, and in turn, the graphical representation may be updated. In some examples, as activity data is accumulated and the aggregate value updated and/or accumulated, additional graphical representations may be generated based on the updated or accumulated aggregate value(s). In other examples, the above-described process may be varied in the implementation, order, function, or structure of each or all steps and is not limited to those provided. 
         [0069]      FIG. 10  illustrates an exemplary graphical representation of a user&#39;s wellness over a time period. Here, exemplary graphical representation  1000  shows a user&#39;s wellness progress over the course of a partial day. Exemplary graphical representation  1000  may comprise a rich graph displaying multiple vectors of data associated with a user&#39;s wellness over time, including a status  1002 , a time  1004 , alarm graphic  1006 , points progress line  1008 , points gained for completion of activities  1012 - 1016 , total points accumulated  1010 , graphical representations  1030 - 1034  of a user&#39;s wellness at specific times over the time period, activity summary data and analysis over time ( 1018 - 1022 ), and an indication of syncing activity  1024 . Here, status  1002  may comprise a brief (e.g., single word) general summary of a user&#39;s wellness. In some examples, time  1004  may indicate the current time, or in other examples, it may indicate the time that graphical representation  1000  was generated or last updated. In some other examples, time  1004  may be implemented using different time zones. In still other examples, time  1004  may be implemented differently. In some examples, alarm graphic  1006  may indicate the time that the user&#39;s alarm rang, or in other examples, it may indicate the time when a band sensed the user awoke, whether or not an alarm rang. In other examples, alarm graphic  1006  may indicate the time when a user&#39;s band began a sequence of notifications to wake up the user (e.g., using notification facility  208 , as described above), and in still other examples, alarm graphic  1006  may represent something different. As shown here, graphical representation  1000  may include other graphical representations of the user&#39;s wellness at specific times of the day ( 1030 ,  1032 ,  1034 ), for example, indicating a low level of wellness or low energy level soon after waking up ( 1030 ) and a more alert or higher energy or wellness level after some activity ( 1032 ,  1034 ). Graphical representation  1000  may also include displays of various analyses of activity over time. For example, graphical representation may include graphical representations of the user&#39;s sleep ( 1018 ), including how many total hours slept and the quality of sleep (e.g., bars may represent depth of sleep during periods of time). In another example, graphical representation may include graphical representations of various aspects of a user&#39;s exercise level for a particular workout, including the magnitude of the activity level ( 1020 ), duration ( 1020 ), the number of steps taken ( 1022 ), the user&#39;s heart rate during the workout (not shown), and still other useful information (e.g., altitude climbed, laps of a pool, number of pitches, etc.). Graphical representation  1000  may further comprise an indication of syncing activity ( 1024 ) showing that graphical representation  1000  is being updated to include additional information from a device (e.g., a data-capable band) or application. Graphical representation  1000  may also include indications of a user&#39;s total accumulated points  1010 , as well as points awarded at certain times for certain activities ( 1012 ,  1014 ,  1016 ). For example, shown here graphical representation  1000  displays the user has accumulated 2,017 points in total (e.g., over a lifetime, over a set period of time, etc.) ( 1010 ). 
         [0070]    In some examples, points awarded may be time-dependent or may expire after a period of time. For example, points awarded for eating a good meal may be valid only for a certain period of time. This period of time may be a predetermined period of time, or it may be dynamically determined. In an example where the period of time is dynamically determined, the points may be valid only until the user next feels hunger. In another example where the period of time is dynamically determined, the points may be valid depending on the glycemic load of the meal (e.g., a meal with low glycemic load may have positive effects that meal carry over to subsequent meals, whereas a meal with a higher glycemic load may have a positive effect only until the next meal). In some examples, a user&#39;s total accumulated points  1010  may reflect that certain points have expired and are no longer valid. 
         [0071]    In some examples, these points may be used for obtaining various types of rewards, or as virtual or actual currency, for example, in an online wellness marketplace, as described herein (e.g., a fitness marketplace). For example, points may be redeemed for virtual prizes (e.g., for games, challenges, etc.), or physical goods (e.g., products associated with a user&#39;s goals or activities, higher level bands, which may be distinguished by different colors, looks and/or features, etc.). In some examples, the points may automatically be tracked by a provider of data-capable bands, such that a prize (e.g., higher level band) is automatically sent to the user upon reaching a given points threshold without any affirmative action by the user. In other examples, a user may redeem a prize (e.g., higher level band) from a store. In still other examples, a user may receive deals. These deals or virtual prizes may be received digitally via a data-capable band, a mobile communications device, or otherwise. 
         [0072]      FIG. 11  illustrates another exemplary graphical representation of a user&#39;s wellness over a time period. Here, exemplary graphical representation  1100  shows a summary of a user&#39;s wellness progress over the course of a week. Exemplary graphical representation  1100  may comprise a rich graph displaying multiple vectors of data associated with a user&#39;s wellness over time, including a status  1102 , a time  1104 , summary graphical representations  1106 - 1116  of a user&#39;s wellness on each days, points earned each day  1120 - 1130 , total points accumulated  1132 , points progress line  1134 , an indication of syncing activity  1118 , and bars  1136 - 1140 . Here, as with status  1002  in  FIG. 10 , status  1102  may comprise a brief (e.g., single word) general summary of a user&#39;s wellness. In some examples, time  1104  may indicate the current time, or in other examples, it may indicate the time that graphical representation  1100  was generated or last updated. In some other examples, time  1104  may be implemented using different time zones. In still other examples, time  1104  may be implemented differently. As shown here, graphical representation  1100  may include summary graphical representations  1106 - 1116  of the user&#39;s wellness on each day, for example, indicating a distress or tiredness on Wednesday ( 1110 ) or a positive spike in wellness on Friday ( 1116 ). In some examples, summary graphical representations  1106 - 1116  may indicate a summary wellness for that particular day. In other examples, summary graphical representations  1106 - 1116  may indicate a cumulative wellness, e.g., at the end of each day. Graphical representation  1100  may further comprise an indication of syncing activity  1118  showing that graphical representation  1100  is being updated to include additional information from a device (e.g., a data-capable band) or application. Graphical representation  1100  may also include indications of a user&#39;s total accumulated points  1132 , as well as points earned each day  1120 - 1130 . For example, shown here graphical representation  1100  displays the user has accumulated 2,017 points thus far, which includes 325 points earned on Saturday ( 1130 ), 263 points earned on Friday ( 1128 ), 251 points earned on Thursday ( 1126 ), and so on. As described above, these points may be used for obtaining various types of rewards, or as virtual or actual currency, for example, in an online wellness marketplace (e.g., a fitness marketplace as described above). In some examples, graphical representation  1100  also may comprise bars  1136 - 1140 . Each bar may represent an aspect of a user&#39;s wellness (e.g., food, exercise, sleep, etc.). In some examples, the bar may display the user&#39;s daily progress toward a personal goal for each aspect (e.g., to sleep eight hours, complete sixty minutes of vigorous exercise, etc.). In other examples, the bar may display the user&#39;s daily progress toward a standardized goal (e.g., a health and fitness expert&#39;s published guidelines, a government agency&#39;s published guidelines, etc.), or other types of goals. 
         [0073]      FIGS. 12A-12F  illustrate exemplary wireframes of exemplary webpages associated with a wellness marketplace. Here, wireframe  1200  comprises navigation  1202 , selected page  1204 A, sync widget  1216 , avatar and goals element  1206 , statistics element  1208 , information ticker  1210 , social feed  1212 , check-in/calendar element  1214 , deal element  1218 , and team summary element  1220 . As described above, a wellness marketplace may be implemented as a portal, website or application where users, may find, purchase, or download applications, products, information, etc., for various uses, as well as share information with other users (e.g., users with like interests). Here, navigation  1202  comprises buttons and widgets for navigating through various pages of the wellness marketplace, including the selected page  1204 A- 1204 F (e.g., the Home page, Team page, Public page, Move page, Eat page, Live page, etc.) and sync widget  1216 . In some examples, sync widget  1216  may be implemented to sync a data-capable band to the user&#39;s account on the wellness marketplace. In some examples, the Home page may include avatar and goals element  1206 , which may be configured to display a user&#39;s avatar and goals. Avatar and goals element  1206  also may enable a user to create an avatar, either by selecting from predetermined avatars, by uploading a user&#39;s own picture or graphic, or other known methods for creating an avatar. Avatar and goals element  1206  also may enable a user to set goals associated with the user&#39;s health, eating/drinking habits, exercise, sleep, socializing, or other aspects of the user&#39;s wellness. The Home page may further include statistics element  1208 , which may be implemented to display statistics associated with the user&#39;s wellness (e.g., the graphical representations described above). As shown here, in some examples, statistics element  1208  may be implemented as a dynamic graphical, and even navigable, element (e.g., a video or interactive graphic), wherein a user may view the user&#39;s wellness progress over time. In other examples, the statistics element  1208  may be implemented as described above (e.g.,  FIGS. 10 and 11 ). The Home page may further include information ticker  1210 , which may stream information associated with a user&#39;s activities, or other information relevant to the wellness marketplace. The Home page may further include social feed  1212 , which may be implemented as a scrolling list of messages or information (e.g., encouragement, news, feedback, recommendations, comments, etc.) from friends, advisors, coaches, or other users. The messages or information may include auto-generated encouragement, comments, news, recommendations, feedback, achievements, opinions, actions taken by teammates, or other information, by a wellness application in response to data associated with the user&#39;s wellness and activities (e.g., gathered by a data-capable band). In some examples, social feed  1212  may be searchable. In some examples, social feed  1212  may enable a user to filter or select the types of messages or information that shows up in the feed (e.g., from the public, only from the team, only from the user, etc.). Social feed  1212  also may be configured to enable a user to select an action associated with each feed message (e.g., cheer, follow, gift, etc.). In some examples, check-in/calendar element  1214  may be configured to allow a user to log their fitness and nutrition. In some examples, check-in/calendar element  1214  also may be configured to enable a user to maintain a calendar. Deal element  1218  may provide a daily deal to the user. The daily deal may be featured for the marketplace, it may be associated with the user&#39;s activities, or it may be generated using a variety of known advertising models. Team summary element  1220  may provide summary information about the user&#39;s team. As used herein, the term “team” may refer to any group of users that elect to use the wellness marketplace together. In some examples, a user may be part of more than one team. In other examples, a group of users may form different teams for different activities, or they may form a single team that participates in, tracks, and shares information regarding, more than one activity. A Home page may be implemented differently than described here. 
         [0074]    Wireframe  1230  comprises an exemplary Team page, which may include a navigation  1202 , selected page  1204 B, sync widget  1216 , team manager element  1228 , leaderboard element  1240 , comparison element  1242 , avatar and goals element  1206 A, statistics element  1208 A, social feed  1212 A, and scrolling member snapshots element  1226 . Avatar and goals element  1206 A and statistics element  1208 A may be implemented as described above with regard to like-numbered or corresponding elements. Navigation  1202 , selected page  1204 B and sync widget  1216  also may be implemented as described above with regard to like-numbered or corresponding elements. In some examples, team manager element  1228  may be implemented as an area for displaying information, or providing widgets, associated with team management. Access to team manager element  1228  may be restricted, in some examples, or access may be provided to the entire team. Leaderboard element  1240  may be implemented to display leaders in various aspects of an activity in which the team is participating (e.g., various sports, social functions (e.g., clubs), drinking abstinence, etc.). In some examples, leaderboard element  1240  may be implemented to display leaders among various groupings (e.g., site-wide, team only, other users determined to be “like” the user according to certain criteria (e.g., similar activities), etc.). In other examples, leaderboard element  1240  may be organized or filtered by various parameters (e.g., date, demographics, geography, activity level, etc.). Comparison element  1242  may be implemented, in some examples, to provide comparisons regarding a user&#39;s performance with respect to an activity, or various aspects of an activity, with the performance of the user&#39;s teammates or with the team as a whole (e.g., team average, team median, team favorites, etc.). Scrolling member snapshots element  1226  may be configured to provide brief summary information regarding each of the members of the team in a scrolling fashion. A Team page may be implemented differently than described here. 
         [0075]    Wireframe  1250  comprises an exemplary Public page, which may include navigation  1202 , selected page  1204 C, sync widget  1216 , leaderboard element  1240 A, social feed  1212 B, statistics report engine  1254 , comparison element  1242 A, and challenge element  1256 . Navigation  1202 , selected page  1204 C and sync widget  1216  may be implemented as described above with regard to like-numbered or corresponding elements. Leaderboard element  1240 A also may be implemented as described above with regard to leaderboard element  1240 , and in some examples, may display leaders amongst all of the users of the wellness marketplace. Social feed  1212 B also may be implemented as described above with regard social feed  1212  and social feed  1212 A. Comparison element  1242 A may be implemented as described above with regard to comparison element  1242 , and in some examples, may display comparisons of a user&#39;s performance of an activity against the performance of all of the other users of the wellness marketplace. Statistics report engine  1254  may generate and display statistical reports associated with various activities being monitored by, and discussed in, the wellness marketplace. In some examples, challenge element  1256  may enable a user to participate in marketplace-wide challenges with other users. In other examples, challenge element  1256  may display the status of or other information associated with, ongoing challenges among users. A Public page may be implemented differently than described here. 
         [0076]    Wireframe  1260  comprises an exemplary Move page, which may include navigation  1202 , selected page  1204 D, sync widget  1216 , leaderboard element  1240 B, statistics report engine  1254 , comparison element  1242 B, search and recommendations element  1272 , product sales element  1282 , exercise science element  1264 , daily movement element  1266 , maps element  1280  and titles element  1258 . Navigation  1202 , selected page  1204 D, sync widget  1216 , leaderboard element  1240 B, statistics report engine  1254 , and comparison element  1242 B may be implemented as described above with regard to like-numbered or corresponding elements. The Move page may be implemented to include a search and recommendations element  1272 , which may be implemented to enable searching of the wellness marketplace. In some examples, in addition to results of the search, recommendations associated with the user&#39;s search may be provided to the user. In other examples, recommendations may be provided to the user based on any other data associated with the user&#39;s activities, as received by, gathered by, or otherwise input into, the wellness marketplace. Product sales element  1282  may be implemented to display products for sale and provide widgets to enable purchases of products by users. The products may be associated with the user&#39;s activities or activity level. Daily movement element  1266  may be implemented to suggest an exercise each day. Maps element  1280  may be implemented to display information associated with the activity of users of the wellness marketplace on a map. In some examples, maps element  1280  may display a percentage of users that are physically active in a geographical region. In other examples, maps element  1280  may display a percentage of users that have eaten well over a particular time period (e.g., currently, today, this week, etc.). In still other examples, maps element  1280  may be implemented differently. In some examples, titles element  1258  may display a list of users and the titles they have earned based on their activities and activity levels (e.g., a most improved user, a hardest working user, etc.). A Move page may be implemented differently than described here. 
         [0077]    Wireframe  1270  comprises an exemplary Eat page, which may include navigation  1202 , selected page  1204 E, sync widget  1216 , leaderboard elements  1240 C and  1240 D, statistics report engine  1254 , comparison element  1242 C, search and recommendations element  1272 , product sales element  1282 , maps element  1280 A, nutrition science element  1276 , and daily food/supplement element  1278 . Navigation  1202 , selected page  1204 E, sync widget  1216 , leaderboard elements  1240 C and  1240 D, statistics report engine  1254 , comparison element  1242 C, search and recommendations element  1272 , product sales element  1282 , and maps element  1280 A may be implemented as described above with regard to like-numbered or corresponding elements. The Eat page may be implemented to include a nutrition science element  1276 , which may display, or provide widgets for accessing, information associated with nutrition science. The Eat page also may be implemented with a daily food/supplement element  1278 , which may be implemented to suggest an food and/or supplement each day. An Eat page may be implemented differently than described here. 
         [0078]    Wireframe  1280  comprises an exemplary Live page, which may include navigation  1202 , selected page  1204 F, sync widget  1216 , leaderboard element  1240 E, search and recommendations element  1272 , product sales element  1282 , maps element  1280 B, social feed  1212 C, health research element  1286 , and product research element  1290 . Navigation  1202 , selected page  1204 F, sync widget  1216 , leaderboard element  1240 E, search and recommendations element  1272 , product sales element  1282 , maps element  1280 B and social feed  1212 C may be implemented as described above with regard to like-numbered or corresponding elements. In some examples, the Live page may include health research element  1286  configured to display, or to enable a user to research, information regarding health topics. In some examples, the Live page may include product research element  1290  configured to display, or to enable a user to research, information regarding products. In some examples, the products may be associated with a user&#39;s particular activities or activity level. In other examples, the products may be associated with any of the activities monitored by, or discussed on, the wellness marketplace. A Live page may be implemented differently than described here. 
         [0079]      FIG. 13  illustrates an exemplary computer system suitable for implementation of a wellness application and use with a data-capable band. In some examples, computer system  1300  may be used to implement computer programs, applications, methods, processes, or other software to perform the above-described techniques. Computer system  1300  includes a bus  1302  or other communication mechanism for communicating information, which interconnects subsystems and devices, such as processor  1304 , system memory  1306  (e.g., RAM), storage device  1308  (e.g., ROM), disk drive  1310  (e.g., magnetic or optical), communication interface  1312  (e.g., modem or Ethernet card), display  1314  (e.g., CRT or LCD), input device  1316  (e.g., keyboard), and cursor control  1318  (e.g., mouse or trackball). 
         [0080]    According to some examples, computer system  1300  performs specific operations by processor  1304  executing one or more sequences of one or more instructions stored in system memory  1306 . Such instructions may be read into system memory  1306  from another computer readable medium, such as static storage device  1308  or disk drive  1310 . In some examples, hard-wired circuitry may be used in place of or in combination with software instructions for implementation. 
         [0081]    The term “computer readable medium” refers to any tangible medium that participates in providing instructions to processor  1304  for execution. Such a medium may take many forms, including but not limited to, non-volatile media and volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as disk drive  1310 . Volatile media includes dynamic memory, such as system memory  1306 . 
         [0082]    Common forms of computer readable media includes, for example, floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer can read. 
         [0083]    Instructions may further be transmitted or received using a transmission medium. The term “transmission medium” may include any tangible or intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such instructions. Transmission media includes coaxial cables, copper wire, and fiber optics, including wires that comprise bus  1302  for transmitting a computer data signal. 
         [0084]    In some examples, execution of the sequences of instructions may be performed by a single computer system  1300 . According to some examples, two or more computer systems  1300  coupled by communication link  1320  (e.g., LAN, PSTN, or wireless network) may perform the sequence of instructions in coordination with one another. Computer system  1300  may transmit and receive messages, data, and instructions, including program, i.e., application code, through communication link  1320  and communication interface  1312 . Received program code may be executed by processor  1304  as it is received, and/or stored in disk drive  1310 , or other non-volatile storage for later execution. 
         [0085]      FIG. 14  depicts an example of an aggregation engine, according to some examples. Diagram  1400  depicts an aggregation engine  1410  including one or more of the following: a sleep manager  1430 , an activity manager  1432 , a nutrition manager  1434 , a general health/wellness manager  1436 , and a conversion module  1420 . As described herein, aggregation engine  1410  is configured to process data, such as data representing parameters based on sensor measurements or the like, as well as derived parameters that can be derived (e.g., mathematically) based on data generated by one or more sensors. Aggregation engine  1410  also can be configured to determine an aggregate value (or score) from which a graphical representation or any other representation can be generated. Conversion module  1420  is configured to convert data or scores representing parameters into values or scores indicating relative states of sleep, activity, nutrition, or general fitness or health (e.g., based on combined states of sleep, activity, nutrition). Further, values or scores generated by conversion module  1420  can be based on team achievements (e.g., one or more other users&#39; sensor data or parameters). 
         [0086]    Sleep manager  1430  is configured to receive data representing parameters relating to sleep activities of a user, and configured to maintain data representing one or more sleep profiles. Parameters describe characteristics, factors or attributes of, for example, sleep, and can be formed from sensor data or derived based on computations. Examples of parameters include a sleep start time (e.g., in terms of Coordinated Universal Time, “UTC,” or Greenwich Mean Time), a sleep end time, and a duration of sleep, which is derived from determining the difference between the sleep end and start times. Sleep manager  1430  cooperates with conversion module  1420  to form a target sleep score to which a user strives to attain. As such, sleep manager  1430  is configured to track a user&#39;s progress and to motivate the user to modify sleep patterns to attain an optimal sleep profile. Sleep manager  1430 , therefore, is configured to coach a user to improve the user&#39;s health and wellness by improving the user&#39;s sleep activity. According to various one or more examples, sleep-related parameters can be acquired or derived by any of the sensors or sensor functions described in, for example,  FIGS. 3 to 5E . For example, other parameters (e.g., location-related parameters describing a home/bedroom location or social-related parameters describing proximity with family members) can be used to determine whether a user is engaged in a sleep-related activity and a quality or condition thereof. 
         [0087]    Activity manager  1432  is configured to receive data representing parameters relating to one or more motion or movement-related activities of a user and to maintain data representing one or more activity profiles. Activity-related parameters describe characteristics, factors or attributes of motion or movements in which a user is engaged, and can be established from sensor data or derived based on computations. Examples of parameters include motion actions, such as a step, stride, swim stroke, rowing stroke, bike pedal stroke, and the like, depending on the activity in which a user is participating. As used herein, a motion action is a unit of motion (e.g., a substantially repetitive motion) indicative of either a single activity or a subset of activities and can be detected, for example, with one or more accelerometers and/or logic configured to determine an activity composed of specific motion actions. Activity manager  1432  cooperates with conversion module  1420  to form a target activity score to which a user strives to attain. As such, activity manager  1432  is configured to track a user&#39;s progress and to motivate the user to modify anaerobic and/or aerobic activities to attain or match the activities defined by an optimal activity profile. Activity manager  1432 , therefore, is configured to coach a user to improve the user&#39;s health and wellness by improving the user&#39;s physical activity, including primary activities of exercise and incidental activities (e.g., walking and climbing stairs in the home, work, etc.). According to various one or more examples, activity-related parameters can be acquired or derived by any of the sensors or sensor functions described in, for example,  FIGS. 3 to 5E . For example, other parameters (e.g., location-related parameters describing a gym location or social-related parameters describing proximity to other persons working out) can be used to determine whether a user is engaged in a movement-related activity, as well as the aspects thereof. 
         [0088]    Nutrition manager  1434  is configured to receive data representing parameters relating to one or more activities relating to nutrition intake of a user and to maintain data representing one or more nutrition profiles. Nutrition-related parameters describe characteristics, factors or attributes of consumable materials (e.g., food and drink), including nutrients, such as vitamins, minerals, etc. that a user consumes. Nutrition-related parameters also include calories. The nutrition-related parameters can be formed from sensor data or derived based on computations. In some cases, a user provides or initiates data retrieval representing the nutrition of food and drink consumed. Nutrition-related parameters also can be derived, such as calories burned or expended. Examples of parameters include an amount (e.g., expressed in international units, “IU”) of a nutrient, such as a vitamin, fiber, mineral, fat (various types), or a macro-nutrient, such as water, carbohydrate, and the like. Nutrition manager  1434  cooperates with conversion module  1420  to form a target nutrition score to which a user strives to attain. As such, nutrition manager  1434  is configured to track a user&#39;s progress and to motivate the user to modify dietary-related activities and consumption to attain an optimal nutrition profile. Nutrition manager  1434 , therefore, is configured to motivate a user to improve the user&#39;s health and wellness by improving the user&#39;s eating habits and nutrition. According to various one or more examples, nutrition-related parameters can be acquired or derived by any of the sensors or sensor functions described in, for example,  FIGS. 3 to 5E . For example, other parameters (e.g., location-related parameters identifying the user is at a restaurant, or social-related parameters describing proximity to others during meal times) can be used to determine whether a user is engaged in a nutrition intake-related activity as well the aspects thereof. In one example, acquired parameters include detected audio converted to text that describes the types of food or drink being consumed. For example, a user in the restaurant may verbally convey an order to a server, such as “I will take the cooked beef, a crab appetizer and an ice tea.” Logic can decode the audio to perform voice recognition. Location data received from a sensor can be used to confirm the audio is detected in the context of a restaurant, whereby the logic determines that the utterances likely constitute an order of food. This logic can reside in nutrition manager  1434 , which can be disposed in or distributed across any of a wearable computing device, an application, a mobile device, a server, in the cloud, or any other structure. 
         [0089]    General health/wellness manager  1436  is configured to manage any aspect of a user&#39;s health or wellness in a manner similar to sleep manager  1430 , activity manager  1432 , and nutrition manager  1434 . For example, general health/wellness manager  1436  can be configured to manage electromagnetic radiation exposure (e.g., in microsieverts), such as radiation generated by a mobile phone or any other device, such as an airport body scanner. Also, general health/wellness manager  1436  can be configured to manage amounts or doses of sunlight sufficient for vitamin D production while advising a user against an amount likely to cause damage to the skin. According to various embodiments, general health/wellness manager  1436  can be configured to perform or control any of the above-described managers or any generic managers (not shown) configured to monitor, detect, or characterize, among other things, any one or more acquired parameters for determining a state or condition of any aspect of health and wellness that can be monitored for purposes of determining trend data and/or progress of an aspect of health and wellness of a user against a target value or score. As the user demonstrates consistent improvement (or deficiencies) in meeting one or more scores representing one or more health and wellness scores, the target value or score can be modified dynamically to motivate a user to continue toward a health and wellness goal, which can be custom-designed for a specific user. The dynamic modification of a target goal can also induce a user to overcome slow or deficient performance by recommending various activities or actions in which to engage to improve nutrition, sleep, movement; cardio goals, or any other health and wellness objective. Further, a wearable device or any structure described herein can be configured to provide feedback related to the progress of attaining a goal as well as to induce the user to engage in or refrain from certain activities. The feedback can be graphical or haptic in nature, but is not so limiting. Thus, the feedback can be transmitted to the user in any medium to be perceived by the user by any of the senses of sight, auditory, touch, etc. 
         [0090]    Therefore, that general health/wellness manager  1436  is not limited to controlling or facilitating sleep, activity and nutrition as aspects of health and wellness, but can monitor, track and generate recommendations for health and wellness based on other acquired parameters, including those related to the environment, such as location, and social interactions, including proximity to others (e.g., other users wearing similar wearable computing devices) and communications via phone, text or emails that can be analyzed to determine whether a user is scheduling time with other persons for a specific activity (e.g., playing ice hockey, dining at a relative&#39;s house for the holidays, or joining colleagues for happy hour). Furthermore, general health/wellness manager  1436  and/or aggregator engine  1410  is not limited to the examples described herein to generate scores, the relative weightings of activities, or by the various instances by which scores can be calculated. The use of points and values, as well as a use of a target score are just a few ways to implement the variety of techniques and/or structures described herein. A target score can be a range of values or can be a function of any number of health and wellness representations. In some examples, specific point values and ways of calculating scores are described herein for purposes of illustration and are not intended to be limiting. 
         [0091]    Conversion module  1420  includes a score generator  1422  and an emphasis manager  1424 . Score generator  1422  is configured to generate a sub-score, score or target score based on sleep-related parameters, activity-related parameters, and nutrition-related parameters, or a combination thereof. Emphasis manger  1424  is configured emphasize one or more parameters of interest to draw a user&#39;s attention to addressing a health-related goal. For example, a nutrition parameter indicating an amount of sodium consumed by a user can be emphasized by weighting the amount of sodium such that it contributes, at least initially, to a relatively larger portion of a target score. As the user succeeds in attaining the goal of reducing sodium, the amount of sodium and its contribution to the target score can be deemphasized. 
         [0092]    Status manager  1450  includes a haptic engine  1452  and a display engine  1454 . Haptic engine  1452  can be configured to impart vibratory energy, for example, from a wearable device  1470  to a user&#39;s body, as a notification, reminder, or alert relating to the progress or fulfillment of user&#39;s sleep, activity, nutrition, or other health and wellness goals relative to target scores. Display engine  1454  can be configured to generate a graphical representation on an interface, such as a touch-sensitive screen on a mobile phone  1472 . In various embodiments, elements of aggregation engine  1410  and elements of status manager  1450  can be disposed in either wearable device  1470  or mobile phone  1472 , or can be distributed among device  1470 , phone  1472  or any other device not shown. Elements of aggregation engine  1410  and elements of status manager  1450  can be implemented in either hardware or software, or a combination thereof. Further, the structures and/or functionalities of aggregation engine  1410  and/or its components can be varied and are not limited to the examples provided. 
         [0093]      FIG. 15  depicts an example of a sleep manager, according to some examples. Diagram  1500  depicts sleep manager  1410  including one or more of the following: a data interface  1501 , a sleep evaluator  1502 , a circadian rhythm (“CR”) manager  1504 , a sleep wellness module  1506 , a repository  1507  configured to store data representing one or more sleep profiles  1509 , one or more sleep deficiency profiles  1508 , and a profile generator  1510 . A bus  1505  couples each of the elements for purposes of communication. Profile generator  1510  can generate one or more profiles representative of a user&#39;s sleep patterns based on trend analysis (i.e., empirically over time and various cycles of sleep) or as an input via data  1520  for an initial sleep profile. Profile generator can generate data representing a subset of acquired parameters to establish sleep profile  1540 . For example, sleep profile  1540  can represent one or more sleep cycles during which acquired parameters were used to determine sleep trends of a user. Or, sleep profile  1540  can represent a current sleep cycle undergoing monitoring and, optionally, modification to conform a user&#39;s sleep habit to that associated with a target sleep score, which can be determine by a sleep profile  1509 . 
         [0094]    Data interface  1501  is configured to receive data representing parameters, such as physical parameters  1511  and environmental parameters  1512 . Examples of physical parameters  1511  include a sleep start time, a sleep end time, a duration of light sleep (and/or a total duration of light sleep between the start and sleep end times), a duration of deep sleep (and/or a total duration of deep sleep between the start and sleep end times), a heart rate, a body temperature, and the like. In some examples, deep sleep includes REM sleep and sleep stages 3 and 4, whereas light sleep includes sleep stages 1 and 2 of typical sleep. Examples of environmental parameters  1512  include an amount of light, a level of sound energy, an ambient temperature, and the like. Parameters also can include steps, minutes of activity/motion, minutes of inactivity/no motion, intensity of activity, aerobic minutes, aerobic intensity, calories burned, training sessions, length of training sessions, intensity of training sessions, calories burned during training session(s), type of activities, duration of each type of activity, intensity of each type of activity, calories burned during each type of activity, instantaneous body temperature, average body temperature, instantaneous skin galvanization, average skin galvanization, instantaneous heart rate, average heart rate, instantaneous perspiration, average perspiration, instantaneous blood sugar level, average blood sugar level, instantaneous respiration rate, average respiration rate, and the like. 
         [0095]    Sleep evaluator  1502  is configured to acquire data representing acquired parameters describing the sleep and sleep-related characteristics of user. In particular, sleep evaluator  1502  is configured to determine characteristics as depicted in generated sleep profile  1540  as generated by profile generator  1510 . As shown, sleep evaluator  1502  is configured to determine a sleep start time  1550  when sleep evaluator  1502  detects, for example, cessation of motion indicative of a wakefulness state  1542 . Sleep evaluator  1502  is configured to determine a light sleep state  1546  and a duration  1554  thereof. For example, sleep evaluator  1502  can detect, for example, motion indicative of “hypnic jerks” or involuntary muscle twitching motions typical during light sleep state  1546 . Also, sleep evaluator  1502  is configured to determine a deep sleep state  1548  and a REM state  1544  for durations  1555  and  1553 , respectively. For example, sleep evaluator  1502  can detect, for example, a decreased heart rate and body temperature, and the absence voluntary muscle motions to confirm or establish that a user is in a deep sleep state. Further, sleep evaluator  1502  is configured to detect initiation of motion indicative of a wakefulness state  1542  to determine a sleep end time  1552 , with which a duration  1551  of sleep can be derived. Sleep evaluator  1502  can also compare acquired parameters to expected parameters set forth in optimal sleep profiles  1509  to confirm the stages of sleep. Optimal sleep profiles  1509  include expected durations of light sleep and deep sleep, as well as number of times light and deep sleep is entered and exited during sleep. 
         [0096]    A circadian rhythm manager  1504  is configured to monitor whether a user&#39;s current sleep activity or overall sleep habits align with optimal human circadian biological clock, such as sleeping during the hours of 10 pm and 6 am (or the duration associated with darkness). For example, a user may travel over multiple time zones and experience jet lag, whereby the period of darkness or optimal human circadian biological clock for the new time zone is depicted as time diagram  1570 . Sleep wellness module  1506  is configured to determine an optimal sleep profile aligned with the new sleep start time  1560  and the new sleep end time  1562 , and is further configured to motivate or induce a user to adjust the sleep habits to adjust the human circadian biological clock to the new time zone. 
         [0097]    Sleep wellness module  1506  also is configured to compare a user&#39;s sleep profile  1540  against data representing one or more sleep deficiency profiles  1508  to determine whether a deficiency exists (e.g., an irregular sleep schedule, a lack of deep sleep, whether a sleep deficit exists, etc.). The one or more sleep deficiency profiles  1508  can include data representative of sleep deficiencies or suboptimal sleeping environments. Examples of suboptimal sleeping environments include too much light, too much sound energy (e.g., too much noise), ambient temperature that is colder or warmer than that is optimal, and the like. Further, sleep wellness module  1506  is configured to provide recommendations to modify the user&#39;s behavior to optimize the user&#39;s sleep score, thereby optimizing the user&#39;s sleep activities to ensure health and wellness. Sleep wellness module  1506  generates notifications and alerts (e.g., graphical, haptic, audio, or otherwise perceptible to a user) to induce a user to modify user behavior, or environmental and physical parameters to improve the sleep of the user. In some examples, sleep wellness module  1506  is configured to cause generation of a graphical representation on an interface to induce modification of an acquired parameter (e.g., a level of light beyond a recommended amount, or a duration of sleep), or to cause generation of a haptic-related signal for providing vibratory feedback to induce modification of the acquired parameter. 
         [0098]      FIG. 16  is an example flow diagram for a technique of managing sleep using wearable devices, including sensors, according to some examples. At  1602 , data representing one or more baseline parameters is received. The baseline parameters include sleep-related characteristics that define parameters upon which a target sleep score is established. For example, the baseline parameters can be set forth in a data arrangement constituting a sleep profile  1509  of  FIG. 15 . In some cases, the values of the baseline parameters are such that if the user attains or fulfils the goals of optimizing sleep, the target sleep score having a value of 100. At  1604 , parameters are acquired that describe a state or characteristics of user&#39;s sleep activity. Examples of acquired parameters can include—via derivation or measurement—a heart rate, a duration, of sleep, a duration of wakefulness, a sleep start time, a sleep end time, a body temperature, an ambient temperature, an amount of light, an amount of sound energy, a quantity of minutes for a one or more durations of light sleep, a quantity of minutes for a one or more durations of deep sleep, a quantity of minutes for sleep aligned with a specific circadian clock (e.g., sleep occurring between 10 pm to 6 am), etc. 
         [0099]    Scores are calculated at  1606  relative to or associated with baseline parameters. A first score can be calculated for a first acquired parameter, such as a duration of sleep in a sleep cycle, based on a first quantity associated with a sleep profile. The first quantity can be a point value assigned to each minute of sleep. A second score can be calculated for a second acquired parameter, such as one or more durations of deep sleep, based on a second quantity associated with the sleep profile. The second quantity can be another point value assigned to each minute of deep sleep. A third score can be calculated for a third acquired parameter, such as a duration of the sleep associated with a time of day representing sleep times aligned with an optimal human circadian biological clock, based on a third quantity associated with the sleep profile. The third quantity can be yet another point value or weighting factor assigned to each minute of sleep during the aligned sleep times (e.g., between 10 pm and 6 am). A sleep score is calculated at  1608  based on the one or more acquired parameters. A difference between the calculated sleep score and the target sleep score indicates a deficiency between optimal sleep activity for health and wellness. 
         [0100]    A determination is made at  1610  whether to implement normative feedback to bring the sleep patterns of the user to conformity with target sleep patterns. If so, then flow  1600  moves to  1612  at which characteristics (or parameters) of a sleep activity is identified for modification to improve the sleep score. For example, a duration of sleep to improve a sleep score ought to be between 7 and 9 hours. A duration of 4 hours can be indicative of a deficient sleep pattern. Thus, flow  1600  can identify the duration of sleep for modification to improve the user&#39;s health and wellness. At  1616 , modifications to improve the sleep score is implemented. At  1614 , the determination of a sleep score can be modified relative to a threshold. For example, when the sleep score exceeds the target score, the rate at which the sleep score can be reduced as a function of the difference between the sleep score and the target score. That is, it gets more difficult to accrue points for the sleep score when exceeding the target score. For example, for sleep scores between 100 and 110, it is 50% harder to obtain sleep score points (e.g., 25% fewer points are rewarded), for sleep scores between 111 and 125, it is 75% harder to obtain sleep score points, and for sleep scores above 126 it is 100% harder. 
         [0101]    At  1618 , a classification for a user can be either leveled up or down. For example, a subset of sleep scores can be determined and the classification associated with a user can be changed based on the subset of sleep scores. The classification can be changed by leveling up to a first sleep profile if the subset of sleep scores is associated with a first range, or the classification can be changed by leveling down to a second sleep profile if the subset of sleep scores is associated with a second range. The first range of activity scores are nearer to the target score than the second range of activity scores. To illustrate, if the sleep score is 95% of the target score (e.g., for a duration), the user is either leveled up or provided the opportunity to level up to implement, for example, a new value of a parameter of a different sleep profile. But if the sleep score is 70% or less of the target score, the user is given the option to level down (e.g., to a less ambitious or rigorous sleep profile). 
         [0102]      FIG. 17  is another example flow diagram for another technique of managing sleep using wearable devices, including sensors, according to some examples. At  1702 , a sleep start time is determined, and an amount of time of sleep at a first level (e.g., a light sleep level) is determined at  1704 . If one or more portions of the time is not associated with a duration of sleep aligned with a circadian rhythm profile, then a first score is determined at  1712  based on a second value (e.g., +1.00 points per minute of light sleep are awarded). But if one or more portion&#39;s of the time is associated with the duration of sleep aligned with the circadian rhythm profile, then a first score is determined at  1714  based on a first value (e.g., +1.20 points per minute of light sleep are awarded). At  1716 , an amount of time of sleep at a second level (e.g., a deep sleep level) is determined. If one or more portions of the time is not associated with a duration of sleep aligned with a circadian rhythm profile, then a second score is determined at  1722  based on a third value (e.g., +2.00 points per minute of deep sleep are awarded). But if one or more portions of the time is associated with the duration of sleep aligned with the circadian rhythm profile, then a second score is determined at  1724  based on a fourth value (e.g., +2.40 points per minute of deep sleep are awarded). 
         [0103]    At  1726 , a score is calculated by the first and/or second scores. One or more acquired parameters are determined at  1728  to modify the sleep score to equalize with the target score. Inducement adjustments are applied at  1730 , whereby a user may be prompted or motivated to modify sleep behavior to improve the health or wellness of the user. At  1732 , a sleep end time is determined, If flow  1700  does not terminate at  1734 , then flow  1700  moves to  1736 , at which the effects of the inducement adjustments are monitored to determine the effectiveness. At  1738 , sleep trend data is generated and analyzed to determine how well a user&#39;s sleep pattern is converging on the patterns defined by an optimal sleep profile. 
         [0104]      FIG. 18  depicts a functional interaction between a sleep evaluator and score generator, according to some examples. Sleep evaluator  1502  provides an indication of light sleep to score generator  1422   a  and an indication of deep sleep to score generator  1422   b . Score generator  1422   a  adds +x points per minute of light sleep between 10 pm and 6 am and adds +y points per minute of light sleep between 6 am to 10 pm. Score generator  1422   b  adds +X points per minute of deep sleep between 10 pm and 6 am and adds +Y points per minute of deep sleep between 6 am to 10 pm. Optionally, score  1830  calculated by either score generator  1422   a  or  1422   b , or both, is modified responsive to disruptive sleep activity (e.g., states of wakefulness during sleep). For example, score  1830  is modified by subtracting −Z points for suboptimal sleep. 
         [0105]    Although the foregoing examples have been described in some detail for purposes of clarity of understanding, the above-described inventive techniques are not limited to the details provided. There are many alternative ways of implementing the above-described invention techniques. The disclosed examples are illustrative and not restrictive.

Technology Classification (CPC): 0