Patent Publication Number: US-2023135146-A1

Title: Activity monitoring device with assessment of exercise intensity

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 17/001,231, filed Aug. 24, 2020, entitled “Activity Monitoring Device With Assessment of Exercise Intensity”, which is a continuation of U.S. application Ser. No. 15/166,702, filed May 27, 2016, which is a national phase application of International Application No. PCT/US2016/027771, filed Apr. 15, 2016, which claims priority to U.S. Provisional Application No. 62/148,027, filed Apr. 15, 2015, U.S. Provisional Patent Application No. 62/168,059, filed on May 29, 2015, U.S. Provisional Patent Application No. 62/168,066, filed May 29, 2015, U.S. Provisional Patent Application No. 62/168,079, filed May 29, 2015, U.S. Provisional Patent Application No. 62/168,095, filed May 29, 2015, and U.S. Provisional Patent Application No. 62/168,110, filed May 29, 2015, each of which are expressly incorporated herein by reference in their entireties for any and all non-limiting purposes. 
    
    
     BACKGROUND 
     While most people appreciate the importance of physical fitness, many have difficulty finding the motivation required to maintain a regular exercise program. Some people find it particularly difficult to maintain an exercise regimen that involves continuously repetitive motions, such as running, walking and bicycling. Devices for tracking a user&#39;s activity may offer motivation in this regard, providing feedback on past activity, and encouragement to continue with an exercise routine in order to meet various exercise goals. 
     However, certain exercise metrics for athletes are assessed in formal lab-based settings, and using cumbersome equipment to monitor an individual while he/she exercises at a fixed location (e.g. on a treadmill or stationary bike). As such, these exercise metrics may not be readily available to the general population. Therefore, improved systems and methods to address at least one or more of these shortcomings in the art are desired. 
     BRIEF SUMMARY 
     The following presents a simplified summary of the present disclosure in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the invention. It is not intended to identify key or critical elements of the invention or to delineate the scope of the invention. The following summary merely presents some concepts of the invention in a simplified form as a prelude to the more detailed description provided below. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    illustrates an example system that may be configured to provide personal training and/or obtain data from the physical movements of a user in accordance with example embodiments; 
         FIG.  2    illustrates an example computer device that may be part of or in communication with the system of  FIG.  1   . 
         FIG.  3    shows an illustrative sensor assembly that may be worn by a user in accordance with example embodiments; 
         FIG.  4    shows another example sensor assembly that may be worn by a user in accordance with example embodiments; 
         FIG.  5    shows illustrative locations for sensory input which may include physical sensors located on/in a user&#39;s clothing and/or be based upon identification of relationships between two moving body parts of the user; 
         FIGS.  6 A- 6 C  depict graphs of exercise data associated with three exercise intensity domains, according to one or more aspects described herein; 
         FIG.  7    schematically depicts an activity monitoring device, according to one or more aspects described herein; 
         FIG.  8    schematically depicts a flowchart diagram for calculation of a critical tissue oxygenation percentage and/or an anaerobic work capacity, from a tissue oxygenation sensor data, according to one or more aspects described herein; 
         FIGS.  9 A- 9 B  depict graphs of muscle oxygenation sensor data from multiple exercise sessions, according to one or more aspects described herein; 
         FIG.  10    depicts a flowchart diagram for determination as to whether a user is exercising at an unsustainable work rate within a severe exercise intensity domain, according to one or more aspects described herein; 
         FIG.  11    depicts a flowchart diagram for determination as to whether a user is exercising at an unsustainable, a sustainable, or a critical work rate, according to one or more aspects described herein; 
         FIG.  12    depicts graphs of speed and muscle oxygenation output data generated during an exercise session, according to one or more aspects described herein; 
         FIG.  13    depicts a flowchart diagram for determination as to whether a user is exercising within a severe exercise intensity domain, according to one or more aspects described herein; 
         FIGS.  14 A- 14 B  depict graphs of power and muscle oxygenation output data from two exercise sessions, according to one or more aspects described herein; 
         FIG.  15    depicts a flowchart diagram for determination as to whether the received tissue oxygenation data represents exercise at a critical intensity, according to one or more aspects described herein; 
         FIG.  16    depicts a graph of muscle oxygenation percentage for different exercise sessions, according to one or more aspects described herein; 
         FIG.  17    depicts graphs of speed and muscle oxygenation output data generated during a same exercise session, according to one or more aspects described herein; 
         FIG.  18    depicts a graph of power output data from an exercise session, according to one or more aspects described herein; 
         FIG.  19    depicts a flowchart diagram for calculation of a critical power associated with an exercise session, according to one or more aspects described herein; 
         FIG.  20    depicts a graph of output speed data for an exercise session, according to one or more aspects described herein; 
         FIG.  21    is a flowchart diagram that may be utilized to calculate a critical speed and an anaerobic work capacity based upon speed sensor data, according to one or more aspects described herein; 
         FIG.  22    is a flowchart diagram that may be utilized to calculate a critical speed and/or an anaerobic work capacity of the user, according to one or more aspects described herein; 
         FIG.  23    is a chart that plots distance data for multiple exercise sessions of a user, according to one or more aspects described herein; 
         FIG.  24    schematically depicts a model for prediction of a critical velocity fraction for running, according to one or more aspects described herein; 
         FIG.  25    schematically depicts a model for prediction of a critical velocity fraction for running, according to one or more aspects described herein; 
         FIG.  26    schematically depicts a model for prediction of a critical velocity fraction for cycling, according to one or more aspects described herein; 
         FIG.  27    schematically depicts a model for prediction of a critical velocity fraction for cycling, according to one or more aspects described herein; 
         FIG.  28    is a flowchart diagram that may be utilized to calculate a critical velocity and an anaerobic work capacity based upon a single input data point, according to one or more aspects described herein; 
         FIG.  29    is a flowchart diagram that may be utilized to estimate a volume of oxygen consumption in response to a received rate of perceived exertion of the user, according to one or more aspects described herein; and 
         FIG.  30    schematically depicts an anaerobic work capacity replenishment rate, according to one or more aspects described herein. 
     
    
    
     DETAILED DESCRIPTION 
     Aspects of this disclosure involve obtaining, storing, and/or processing athletic data relating to the physical movements of an athlete. The athletic data may be actively or passively sensed and/or stored in one or more non-transitory storage mediums. Still further aspects relate to using athletic data to generate an output, such as for example, calculated athletic attributes, feedback signals to provide guidance, and/or other information. These and other aspects will be discussed in the context of the following illustrative examples of a personal training system. 
     In the following description of the various embodiments, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration various embodiments in which aspects of the disclosure may be practiced. It is to be understood that other embodiments may be utilized and structural and functional modifications may be made without departing from the scope and spirit of the present disclosure. Further, headings within this disclosure should not be considered as limiting aspects of the disclosure and the example embodiments are not limited to the example headings. 
     I. Example Personal Training System 
     A. Illustrative Networks 
     Aspects of this disclosure relate to systems and methods that may be utilized across a plurality of networks. In this regard, certain embodiments may be configured to adapt to dynamic network environments. Further embodiments may be operable in differing discrete network environments.  FIG.  1    illustrates an example of a personal training system  100  in accordance with example embodiments. Example system  100  may include one or more interconnected networks, such as the illustrative body area network (BAN)  102 , local area network (LAN)  104 , and wide area network (WAN)  106 . As shown in  FIG.  1    (and described throughout this disclosure), one or more networks (e.g., BAN  102 , LAN  104 , and/or WAN  106 ), may overlap or otherwise be inclusive of each other. Those skilled in the art will appreciate that the illustrative networks  102 - 106  are logical networks that may each comprise one or more different communication protocols and/or network architectures and yet may be configured to have gateways to each other or other networks. For example, each of BAN  102 , LAN  104  and/or WAN  106  may be operatively connected to the same physical network architecture, such as cellular network architecture  108  and/or WAN architecture  110 . For example, portable electronic device  112 , which may be considered a component of both BAN  102  and LAN  104 , may comprise a network adapter or network interface card (NIC) configured to translate data and control signals into and from network messages according to one or more communication protocols, such as the Transmission Control Protocol (TCP), the Internet Protocol (IP), and the User Datagram Protocol (UDP) through one or more of architectures  108  and/or  110 . These protocols are well known in the art, and thus will not be discussed here in more detail. 
     Network architectures  108  and  110  may include one or more information distribution network(s), of any type(s) or topology(s), alone or in combination(s), such as for example, cable, fiber, satellite, telephone, cellular, wireless, etc. and as such, may be variously configured such as having one or more wired or wireless communication channels (including but not limited to: WiFi®, Bluetooth®, Near-Field Communication (NFC) and/or ANT technologies). Thus, any device within a network of  FIG.  1   , (such as portable electronic device  112  or any other device described herein) may be considered inclusive to one or more of the different logical networks  102 - 106 . With the foregoing in mind, example components of an illustrative BAN and LAN (which may be coupled to WAN  106 ) will be described. 
     1. Example Local Area Network 
     LAN  104  may include one or more electronic devices, such as for example, computer device  114 . Computer device  114 , or any other component of system  100 , may comprise a mobile terminal, such as a telephone, music player, tablet, netbook or any portable device. In other embodiments, computer device  114  may comprise a media player or recorder, desktop computer, server(s), a gaming console, such as for example, a Microsoft® XBOX, Sony® Playstation, and/or a Nintendo® Wii gaming consoles. Those skilled in the art will appreciate that these are merely example devices for descriptive purposes and this disclosure is not limited to any console or computing device. 
     Those skilled in the art will appreciate that the design and structure of computer device  114  may vary depending on several factors, such as its intended purpose. One example implementation of computer device  114  is provided in  FIG.  2   , which illustrates a block diagram of computing device  200 . Those skilled in the art will appreciate that the disclosure of  FIG.  2    may be applicable to any device disclosed herein. Device  200  may include one or more processors, such as processor  202 - 1  and  202 - 2  (generally referred to herein as “processors  202 ” or “processor  202 ”). Processors  202  may communicate with each other or other components via an interconnection network or bus  204 . Processor  202  may include one or more processing cores, such as cores  206 - 1  and  206 - 2  (referred to herein as “cores  206 ” or more generally as “core  206 ”), which may be implemented on a single integrated circuit (IC) chip. 
     Cores  206  may comprise a shared cache  208  and/or a private cache (e.g., caches  210 - 1  and  210 - 2 , respectively). One or more caches  208 / 210  may locally cache data stored in a system memory, such as memory  212 , for faster access by components of the processor  202 . Memory  212  may be in communication with the processors  202  via a chipset  216 . Cache  208  may be part of system memory  212  in certain embodiments. Memory  212  may include, but is not limited to, random access memory (RAM), read only memory (ROM), and include one or more of solid-state memory, optical or magnetic storage, and/or any other medium that can be used to store electronic information. Yet other embodiments may omit system memory  212 . 
     System  200  may include one or more /O devices (e.g., I/O devices  214 - 1  through  214 - 3 , each generally referred to as I/O device  214 ). I/O data from one or more I/O devices  214  may be stored at one or more caches  208 ,  210  and/or system memory  212 . Each of I/O devices  214  may be permanently or temporarily configured to be in operative communication with a component of system  100  using any physical or wireless communication protocol. 
     Returning to  FIG.  1   , four example I/O devices (shown as elements  116 - 122 ) are shown as being in communication with computer device  114 . Those skilled in the art will appreciate that one or more of devices  116 - 122  may be stand-alone devices or may be associated with another device besides computer device  114 . For example, one or more I/O devices may be associated with or interact with a component of BAN  102  and/or WAN  106 . I/O devices  116 - 122  may include, but are not limited to athletic data acquisition units, such as for example, sensors. One or more I/O devices may be configured to sense, detect, and/or measure an athletic parameter from a user, such as user  124 . Examples include, but are not limited to: an accelerometer, a gyroscope, a location-determining device (e.g., GPS), light (including non-visible light) sensor, temperature sensor (including ambient temperature and/or body temperature), sleep pattern sensors, heart rate monitor, image-capturing sensor, moisture sensor, force sensor, compass, angular rate sensor, and/or combinations thereof among others. 
     In further embodiments, I/O devices  116 - 122  may be used to provide an output (e.g., audible, visual, or tactile cue) and/or receive an input, such as a user input from athlete  124 . Example uses for these illustrative I/O devices are provided below, however, those skilled in the art will appreciate that such discussions are merely descriptive of some of the many options within the scope of this disclosure. Further, reference to any data acquisition unit, I/O device, or sensor is to be interpreted disclosing an embodiment that may have one or more I/O device, data acquisition unit, and/or sensor disclosed herein or known in the art (either individually or in combination). 
     Information from one or more devices (across one or more networks) may be used to provide (or be utilized in the formation of) a variety of different parameters, metrics or physiological characteristics including but not limited to: motion parameters, such as speed, acceleration, distance, steps taken, direction, relative movement of certain body portions or objects to others, or other motion parameters which may be expressed as angular rates, rectilinear rates or combinations thereof, physiological parameters, such as calories, heart rate, sweat detection, effort, oxygen consumed, oxygen kinetics, and other metrics which may fall within one or more categories, such as: pressure, impact forces, information regarding the athlete, such as height, weight, age, demographic information and combinations thereof. 
     System  100  may be configured to transmit and/or receive athletic data, including the parameters, metrics, or physiological characteristics collected within system  100  or otherwise provided to system  100 . As one example, WAN  106  may comprise server  111 . Server  111  may have one or more components of system  200  of  FIG.  2   . In one embodiment, server  111  comprises at least a processor and a memory, such as processor  206  and memory  212 . Server  111  may be configured to store computer-executable instructions on a non-transitory computer-readable medium. The instructions may comprise athletic data, such as raw or processed data collected within system  100 . System  100  may be configured to transmit data, such as energy expenditure points, to a social networking website or host such a site. Server  111  may be utilized to permit one or more users to access and/or compare athletic data. As such, server  111  may be configured to transmit and/or receive notifications based upon athletic data or other information. 
     Returning to LAN  104 , computer device  114  is shown in operative communication with a display device  116 , an image-capturing device  118 , sensor  120  and exercise device  122 , which are discussed in turn below with reference to example embodiments. In one embodiment, display device  116  may provide audio-visual cues to athlete  124  to perform a specific athletic movement. The audio-visual cues may be provided in response to computer-executable instruction executed on computer device  114  or any other device, including a device of BAN  102  and/or WAN. Display device  116  may be a touchscreen device or otherwise configured to receive a user-input. 
     In one embodiment, data may be obtained from image-capturing device  118  and/or other sensors, such as sensor  120 , which may be used to detect (and/or measure) athletic parameters, either alone or in combination with other devices, or stored information. Image-capturing device  118  and/or sensor  120  may comprise a transceiver device. In one embodiment sensor  128  may comprise an infrared (IR), electromagnetic (EM) or acoustic transceiver. For example, image-capturing device  118 , and/or sensor  120  may transmit waveforms into the environment, including towards the direction of athlete  124  and receive a “reflection” or otherwise detect alterations of those released waveforms. Those skilled in the art will readily appreciate that signals corresponding to a multitude of different data spectrums may be utilized in accordance with various embodiments. In this regard, devices  118  and/or  120  may detect waveforms emitted from external sources (e.g., not system  100 ). For example, devices  118  and/or  120  may detect heat being emitted from user  124  and/or the surrounding environment. Thus, image-capturing device  126  and/or sensor  128  may comprise one or more thermal imaging devices. In one embodiment, image-capturing device  126  and/or sensor  128  may comprise an IR device configured to perform range phenomenology. 
     In one embodiment, exercise device  122  may be any device configurable to permit or facilitate the athlete  124  performing a physical movement, such as for example a treadmill, step machine, etc. There is no requirement that the device be stationary. In this regard, wireless technologies permit portable devices to be utilized, thus a bicycle or other mobile exercising device may be utilized in accordance with certain embodiments. Those skilled in the art will appreciate that equipment  122  may be or comprise an interface for receiving an electronic device containing athletic data performed remotely from computer device  114 . For example, a user may use a sporting device (described below in relation to BAN  102 ) and upon returning home or the location of equipment  122 , download athletic data into element  122  or any other device of system  100 . Any I/O device disclosed herein may be configured to receive activity data. 
     2. Body Area Network 
     BAN  102  may include two or more devices configured to receive, transmit, or otherwise facilitate the collection of athletic data (including passive devices). Exemplary devices may include one or more data acquisition units, sensors, or devices known in the art or disclosed herein, including but not limited to I/O devices  116 - 122 . Two or more components of BAN  102  may communicate directly, yet in other embodiments, communication may be conducted via a third device, which may be part of BAN  102 , LAN  104 , and/or WAN  106 . One or more components of LAN  104  or WAN  106  may form part of BAN  102 . In certain implementations, whether a device, such as portable device  112 , is part of BAN  102 , LAN  104 , and/or WAN  106 , may depend on the athlete&#39;s proximity to an access point to permit communication with mobile cellular network architecture  108  and/or WAN architecture  110 . User activity and/or preference may also influence whether one or more components are utilized as part of BAN  102 . Example embodiments are provided below. 
     User  124  may be associated with (e.g., possess, carry, wear, and/or interact with) any number of devices, such as portable device  112 , shoe-mounted device  126 , wrist-worn device  128  and/or a sensing location, such as sensing location  130 , which may comprise a physical device or a location that is used to collect information. One or more devices  112 ,  126 ,  128 , and/or  130  may not be specially designed for fitness or athletic purposes. Indeed, aspects of this disclosure relate to utilizing data from a plurality of devices, some of which are not fitness devices, to collect, detect, and/or measure athletic data. In certain embodiments, one or more devices of BAN  102  (or any other network) may comprise a fitness or sporting device that is specifically designed for a particular sporting use. As used herein, the term “sporting device” includes any physical object that may be used or implicated during a specific sport or fitness activity. Exemplary sporting devices may include, but are not limited to: golf balls, basketballs, baseballs, soccer balls, footballs, powerballs, hockey pucks, weights, bats, clubs, sticks, paddles, mats, and combinations thereof. In further embodiments, exemplary fitness devices may include objects within a sporting environment where a specific sport occurs, including the environment itself, such as a goal net, hoop, backboard, portions of a field, such as a midline, outer boundary marker, base, and combinations thereof. 
     In this regard, those skilled in the art will appreciate that one or more sporting devices may also be part of (or form) a structure and vice-versa, a structure may comprise one or more sporting devices or be configured to interact with a sporting device. For example, a first structure may comprise a basketball hoop and a backboard, which may be removable and replaced with a goal post. In this regard, one or more sporting devices may comprise one or more sensors, such as one or more of the sensors discussed above in relation to  FIGS.  1 - 3   , that may provide information utilized, either independently or in conjunction with other sensors, such as one or more sensors associated with one or more structures. For example, a backboard may comprise a first sensor configured to measure a force and a direction of the force by a basketball upon the backboard and the hoop may comprise a second sensor to detect a force. Similarly, a golf club may comprise a first sensor configured to detect grip attributes on the shaft and a second sensor configured to measure impact with a golf ball. 
     Looking to the illustrative portable device  112 , it may be a multi-purpose electronic device, that for example, includes a telephone or digital music player, including an IPOD®, IPAD®, or iPhone®, brand devices available from Apple, Inc. of Cupertino, California or Zune® or Microsoft® Windows devices available from Microsoft of Redmond, Washington. As known in the art, digital media players can serve as an output device, input device, and/or storage device for a computer. Device  112  may be configured as an input device for receiving raw or processed data collected from one or more devices in BAN  102 , LAN  104 , or WAN  106 . In one or more embodiments, portable device  112  may comprise one or more components of computer device  114 . For example, portable device  112  may be include a display  116 , image-capturing device  118 , and/or one or more data acquisition devices, such as any of the I/O devices  116 - 122  discussed above, with or without additional components, so as to comprise a mobile terminal. 
     a. Illustrative Apparel/Accessory Sensors 
     In certain embodiments, I/O devices may be formed within or otherwise associated with user&#39;s  124  clothing or accessories, including a watch, armband, wristband, necklace, shirt, shoe, or the like. These devices may be configured to monitor athletic movements of a user. It is to be understood that they may detect athletic movement during user&#39;s  124  interactions with computer device  114  and/or operate independently of computer device  114  (or any other device disclosed herein). For example, one or more devices in BAN  102  may be configured to function as an all-day activity monitor that measures activity regardless of the user&#39;s proximity or interactions with computer device  114 . It is to be further understood that the sensory system  302  shown in  FIG.  3    and the device assembly  400  shown in  FIG.  4   , each of which are described in the following paragraphs, are merely illustrative examples. 
     i. Shoe-Mounted Device 
     In certain embodiments, device  126  shown in  FIG.  1   , may comprise footwear which may include one or more sensors, including but not limited to those disclosed herein and/or known in the art.  FIG.  3    illustrates one example embodiment of a sensor system  302  providing one or more sensor assemblies  304 . Assembly  304  may comprise one or more sensors, such as for example, an accelerometer, gyroscope, location-determining components, force sensors and/or or any other sensor disclosed herein or known in the art. In the illustrated embodiment, assembly  304  incorporates a plurality of sensors, which may include force-sensitive resistor (FSR) sensors  306 ; however, other sensor(s) may be utilized. Port  308  may be positioned within a sole structure  309  of a shoe, and is generally configured for communication with one or more electronic devices. Port  308  may optionally be provided to be in communication with an electronic module  310 , and the sole structure  309  may optionally include a housing  311  or other structure to receive the module  310 . The sensor system  302  may also include a plurality of leads  312  connecting the FSR sensors  306  to the port  308 , to enable communication with the module  310  and/or another electronic device through the port  308 . Module  310  may be contained within a well or cavity in a sole structure of a shoe, and the housing  311  may be positioned within the well or cavity. In one embodiment, at least one gyroscope and at least one accelerometer are provided within a single housing, such as module  310  and/or housing  311 . In at least a further embodiment, one or more sensors are provided that, when operational, are configured to provide directional information and angular rate data. The port  308  and the module  310  include complementary interfaces  314 ,  316  for connection and communication. 
     In certain embodiments, at least one force-sensitive resistor  306  shown in  FIG.  3    may contain first and second electrodes or electrical contacts  318 ,  320  and a force-sensitive resistive material  322  disposed between the electrodes  318 ,  320  to electrically connect the electrodes  318 ,  320  together. When pressure is applied to the force-sensitive material  322 , the resistivity and/or conductivity of the force-sensitive material  322  changes, which changes the electrical potential between the electrodes  318 ,  320 . The change in resistance can be detected by the sensor system  302  to detect the force applied on the sensor  316 . The force-sensitive resistive material  322  may change its resistance under pressure in a variety of ways. For example, the force-sensitive material  322  may have an internal resistance that decreases when the material is compressed. Further embodiments may utilize “volume-based resistance”, which may be implemented through “smart materials.” As another example, the material  322  may change the resistance by changing the degree of surface-to-surface contact, such as between two pieces of the force sensitive material  322  or between the force sensitive material  322  and one or both electrodes  318 ,  320 . In some circumstances, this type of force-sensitive resistive behavior may be described as “contact-based resistance.” 
     ii. Wrist-Worn Device 
     As shown in  FIG.  4   , device  400  (which may resemble or comprise sensory device  128  shown in  FIG.  1   ), may be configured to be worn by user  124 , such as around a wrist, arm, ankle, neck or the like. Device  400  may include an input mechanism, such as a depressible input button  402  configured to be used during operation of the device  400 . The input button  402  may be operably connected to a controller  404  and/or any other electronic components, such as one or more of the elements discussed in relation to computer device  114  shown in  FIG.  1   . Controller  404  may be embedded or otherwise part of housing  406 . Housing  406  may be formed of one or more materials, including elastomeric components and comprise one or more displays, such as display  408 . The display may be considered an illuminable portion of the device  400 . The display  408  may include a series of individual lighting elements or light members such as LED lights  410 . The lights may be formed in an array and operably connected to the controller  404 . Device  400  may include an indicator system  412 , which may also be considered a portion or component of the overall display  408 . Indicator system  412  can operate and illuminate in conjunction with the display  408  (which may have pixel member  414 ) or completely separate from the display  408 . The indicator system  412  may also include a plurality of additional lighting elements or light members, which may also take the form of LED lights in an exemplary embodiment. In certain embodiments, indicator system may provide a visual indication of goals, such as by illuminating a portion of lighting members of indicator system  412  to represent accomplishment towards one or more goals. Device  400  may be configured to display data expressed in terms of activity points or currency earned by the user based on the activity of the user, either through display  408  and/or indicator system  412 . 
     A fastening mechanism  416  can be disengaged wherein the device  400  can be positioned around a wrist or portion of the user  124  and the fastening mechanism  416  can be subsequently placed in an engaged position. In one embodiment, fastening mechanism  416  may comprise an interface, including but not limited to a USB port, for operative interaction with computer device  114  and/or devices, such as devices  120  and/or  112 . In certain embodiments, fastening member may comprise one or more magnets. In one embodiment, fastening member may be devoid of moving parts and rely entirely on magnetic forces. 
     In certain embodiments, device  400  may comprise a sensor assembly (not shown in  FIG.  4   ). The sensor assembly may comprise a plurality of different sensors, including those disclosed herein and/or known in the art. In an example embodiment, the sensor assembly may comprise or permit operative connection to any sensor disclosed herein or known in the art. Device  400  and or its sensor assembly may be configured to receive data obtained from one or more external sensors. 
     iii. Apparel and/or Body Location Sensing 
     Element  130  of  FIG.  1    shows an example sensory location which may be associated with a physical apparatus, such as a sensor, data acquisition unit, or other device. Yet in other embodiments, it may be a specific location of a body portion or region that is monitored, such as via an image capturing device (e.g., image capturing device  118 ). In certain embodiments, element  130  may comprise a sensor, such that elements  130   a  and  130   b  may be sensors integrated into apparel, such as athletic clothing. Such sensors may be placed at any desired location of the body of user  124 . Sensors  130   a/b  may communicate (e.g., wirelessly) with one or more devices (including other sensors) of BAN  102 , LAN  104 , and/or WAN  106 . In certain embodiments, passive sensing surfaces may reflect waveforms, such as infrared light, emitted by image-capturing device  118  and/or sensor  120 . In one embodiment, passive sensors located on user&#39;s  124  apparel may comprise generally spherical structures made of glass or other transparent or translucent surfaces which may reflect waveforms. Different classes of apparel may be utilized in which a given class of apparel has specific sensors configured to be located proximate to a specific portion of the user&#39;s  124  body when properly worn. For example, golf apparel may include one or more sensors positioned on the apparel in a first configuration and yet soccer apparel may include one or more sensors positioned on apparel in a second configuration. 
       FIG.  5    shows illustrative locations for sensory input (see, e.g., sensory locations  130   a - 130   o ). In this regard, sensors may be physical sensors located on/in a user&#39;s clothing, yet in other embodiments, sensor locations  130   a - 130   o  may be based upon identification of relationships between two moving body parts. For example, sensor location  130   a  may be determined by identifying motions of user  124  with an image-capturing device, such as image-capturing device  118 . Thus, in certain embodiments, a sensor may not physically be located at a specific location (such as one or more of sensor locations  130   a - 130   o ), but is configured to sense properties of that location, such as with image-capturing device  118  or other sensor data gathered from other locations. In this regard, the overall shape or portion of a user&#39;s body may permit identification of certain body parts. Regardless of whether an image-capturing device is utilized and/or a physical sensor located on the user  124 , and/or using data from other devices, (such as sensory system  302 ), device assembly  400  and/or any other device or sensor disclosed herein or known in the art is utilized, the sensors may sense a current location of a body part and/or track movement of the body part. In one embodiment, sensory data relating to location  130   m  may be utilized in a determination of the user&#39;s center of gravity (a.k.a, center of mass). For example, relationships between location  130   a  and location(s)  130   f / 130   l  with respect to one or more of location(s)  130   m - 130   o  may be utilized to determine if a user&#39;s center of gravity has been elevated along the vertical axis (such as during a jump) or if a user is attempting to “fake” a jump by bending and flexing their knees. In one embodiment, sensor location  1306   n  may be located at about the sternum of user  124 . Likewise, sensor location  130   o  may be located approximate to the naval of user  124 . In certain embodiments, data from sensor locations  130   m - 130   o  may be utilized (alone or in combination with other data) to determine the center of gravity for user  124 . In further embodiments, relationships between multiple sensor locations, such as sensors  130   m - 130   o,  may be utilized in determining orientation of the user  124  and/or rotational forces, such as twisting of user&#39;s  124  torso. Further, one or more locations, such as location(s), may be utilized as (or approximate) a center of moment location. For example, in one embodiment, one or more of location(s)  130   m - 130   o  may serve as a point for a center of moment location of user  124 . In another embodiment, one or more locations may serve as a center of moment of specific body parts or regions. 
     Exercise may be categorized into multiple intensity domains. In one example, exercise may be categorized into four intensity domains, including: moderate, heavy, severe, and extreme, which are defined based on distinct metabolic profiles of an athlete or user. In one example, an athlete&#39;s exertion may be monitored using a power metric.  FIG.  6 A  depicts three graphs;  606 ,  608 , and  610 , corresponding to three exercise sessions undertaken by a user, and such that exertion is graphed as power (y-axis  602 ) versus time (x-axis  604 ). Accordingly, graphs  606 ,  608 , and  610  may correspond to three separate exercise sessions carried out at an approximately constant work rate. As such, graphs  606 ,  608 , and  610  are depicted in  FIG.  6 A  as approximately level graphs. In one specific example, the exercise sessions associated with graph  606 ,  608 , and  610  may correspond to a user cycling against an approximately constant resistance (approximately constant speed, approximately constant gradient, approximately constant wind resistance, among others). In one example, each of the exercise sessions associated with graph  606 ,  608 , and  610  may be carried out in a controlled environment, such as a lab-based environment, and such that an athlete may cycle on a stationary exercise bicycle against a controlled, and approximately constant resistance, and at an approximately constant speed. As such, a power associated with an exercise session may be calculated based on a resistance applied to the exercise bicycle, and a speed at which the person being monitored (referred to as the athlete or user) is cycling. In another example, graphs  606 ,  608 , and  610  may correspond to three monitored running exercise sessions carried out against an approximately constant resistance (at an approximately constant speed, and an approximately constant gradient). As such, graphs  606 ,  608 , and  610  may correspond to a user running on a treadmill at approximately constant speed and an approximately constant gradient. Additionally or alternatively, graphs  606 ,  608 , and  610  may correspond to three exercise sessions monitored as a user cycles at three approximately constant work rates (approximately constant power) in a non-lab-based environment on a regular bicycle, or as a user runs at three approximately constant work rates in a non-lab-based environment. Furthermore, graphs  606 ,  608 , and  610  may correspond to alternative forms of exercise (e.g. cross-country skiing, speed skating, among others). 
     Graphs  606 ,  608 , and  610  schematically depict a same exercise type carried out by a same user at three approximately constant work rates corresponding to three different exercise intensity domains for that user. In particular, graph  610  may correspond to a moderate exercise intensity domain, graph  608  corresponds to a heavy exercise intensity domain, and graph  606  corresponds to a severe exercise intensity domain. In one example, a moderate exercise intensity domain may be defined as corresponding to an exercise intensity (power level) below a lactate threshold (LT), which is schematically depicted as threshold line  612  in  FIG.  6 A , and otherwise referred to as a gas exchange threshold (GET), lactate inflection point (LIP), or anaerobic threshold (AT). As such, a lactate threshold may correspond to an exercise intensity at which lactate (in particular, lactic acid) starts to accumulate in the bloodstream of the exercising user. In one specific example, graph  610  may be approximately 10% below the lactate threshold for the user being monitored. 
     In one example, graph  608  may correspond to a heavy exercise intensity domain, and such that a heavy exercise intensity domain may be defined as an exercise intensity carried out between the lactate threshold associated with line  612 , and a critical intensity (CI) (otherwise referred to as a critical power (CP)). In one example, a critical intensity for the user associated with graphs  606 ,  608 , and  610  may be denoted by line  614 . As such, when an exercise intensity is below the critical intensity, any elevation in blood lactate and oxygen consumption (VO 2 ) may be stabilized after approximately 10 to 15 minutes. The work rate at a critical intensity may be defined as the highest sustainable work rate for a prolonged duration that does not elicit maximal oxygen uptake. In one example, graph  608  may be approximately 15% below the critical intensity  614 . 
     Graph  606  may correspond to a severe exercise intensity domain. A severe exercise intensity domain may correspond to an exercise intensity above the critical intensity schematically depicted by line  614 . As such, a work rate within the severe exercise intensity domain may lead, inexorably, to maximal oxygen consumption, which may be referred to as acute fatigue. The amount of work a user is able to do above the critical intensity may be capacity-limited, but rate-independent. In other words, the amount of work that a given user is able to perform above a critical intensity may be fixed, regardless of the rate at which the work is done (i.e. the power). This amount of work that the user is able to perform may be referred to as a finite reserve capacity, and may be denoted as W′. In one example, the finite reserve capacity may, alternatively, be referred to as an anaerobic capacity, or anaerobic work capacity. In one example, where the finite reserve capacity is expressed as a distance, it may be alternatively denoted by D′. In one example, the severe exercise session associated with graph  606  may be approximately 15% above the critical intensity associated with line  614 . 
     Line  616  schematically denotes maximal oxygen consumption (VO 2max ) for the user associated with graphs  606 ,  608 , and  610 . This maximal oxygen consumption may, alternatively, be referred to as maximal oxygen uptake, peak oxygen uptake, or maximal aerobic capacity, and may be the maximum rate of oxygen consumption for the user. In one example, the maximal oxygen consumption may be expressed in liters of oxygen per minute (L/min), or in milliliters of oxygen per kilogram of body mass per minute (mL/(kg. min)). 
     The length of each of graphs  606 ,  608 , and  610  corresponds to the durations of the three exercise sessions within the moderate (graph  610 ), heavy (graph  608 ), and severe (graph  606 ) exercise intensity domains. Accordingly, the severe exercise intensity session  606  was carried out for a duration corresponding to time  618 . Similarly, the heavy exercise intensity session  608  was carried out for a duration corresponding to time  620 , and the moderate exercise intensity session  610  was carried out for a duration corresponding to time  622 . In one implementation, knowledge of the critical intensity corresponding to line  614 , an intensity above the critical intensity (e.g. an intensity associated with an exercise session corresponding to graph  606 ), and a duration  618  of the exercise session within the severe exercise intensity domain (i.e. above the critical intensity) may be utilized to calculate the finite reserve capacity for a user. In one example, the finite reserve capacity may be calculated as an integration of a power graph above the critical power. For the example of graph  606  (at constant power), the area under the graph but above the critical power may be calculated as: 
       Finite reserve capacity,  W ′( J )=Intensity above a critical intensity ( W )×time to fatigue ( s ).
 
     In one example, once a critical intensity and a finite work capacity associated with a given athlete are known (i.e. are identified and/or calculated), real-time monitoring of an athletic performance of the athlete, by an activity monitoring device, such as one or more of devices  112 ,  114 ,  128 ,  200 , and/or  400 , among others, may be used to provide feedback regarding a current exercise intensity relative to a critical intensity for the athlete. Additionally or alternatively, given a critical intensity and a finite work capacity associated with the athlete, the activity monitoring device may be utilized to predict one or more outcomes of a current exercise session. As such, an activity monitoring device may be utilized to, among others, predict a race time for an athlete. Further details related to utilization of critical intensity and finite work capacity information to provide feedback to a user are discussed later in the various disclosures that follow. 
     Muscle oxygenation, MO 2 , may be utilized as a metric for monitoring exercise performance of an athlete. In one example, muscle oxygenation may be monitored in order to identify, among others, a critical intensity and/or anaerobic work capacity associated with an athlete. An activity monitoring device incorporating a muscle oxygenation sensor is discussed in further detail in relation to  FIG.  7   .  FIG.  6 B  schematically depicts three graphs  628 ,  630 , and  632  of muscle oxygenation percentage, MO 2  (%) (y-axis  624 ) versus time (x-axis  626 ). The three graphs  628 ,  630 , and  632  correspond to the three graphs  606 ,  608 , and  610  from  FIG.  6 A  (i.e. the muscle oxygenation percentage data used to plot graphs  628 ,  630 , and  632  was received from the exercise testing associated with graphs  606 ,  608 , and  610 ). In one example, graphs  628 ,  630 , and  632  may each depict muscle oxygenation percentage associated with a quadriceps muscle of an athlete, and such that for each of graphs  628 ,  630 , and  632 , the muscle oxygenation percentage data may be detected by a same sensor type (as described in further detail in relation to  FIG.  7   ), and detected from an approximately same location on an athlete&#39;s body (i.e. proximate a quadriceps muscle of the athlete). 
     Graph  628  schematically depicts a progression of muscle oxygenation percentage of a quadriceps muscle of an athlete exercising within a severe exercise intensity domain (i.e. exercising at an intensity corresponding to graph  606 ). As depicted in  FIG.  6 B , graph  628  depicts a steady decline in muscle oxygenation percentage, without exhibiting an increase in muscle oxygenation percentage before the end of the exercise (point of fatigue) at time  618 . Graph  630  schematically depicts a progression of muscle oxygenation percentage of a quadriceps muscle of an athlete exercising within a heavy exercise intensity domain (i.e. exercising at an intensity corresponding to graph  608 ). As depicted in  FIG.  6 B , graph  630  depicts a decline in muscle oxygenation percentage during a first time period, until point  634 . At point  634 , graph  630  exhibits a recovery in muscle oxygenation percentage before the exercise is completed (before the athlete fatigues) at time  620 . Graph  632  schematically depicts a progression of muscle oxygenation percentage of the quadriceps muscle of the athlete exercising within a moderate exercise intensity domain (i.e. exercising at an intensity corresponding to graph  610 ). As depicted in  FIG.  6 B , graph  632  depicts a decline in muscle oxygenation percentage during a first time period, until point  636 . At point  636 , graph  632  exhibits a recovery in muscle oxygenation percentage before the exercise is completed at time  622 . 
     As previously discussed, the data used to plot graphs  628 ,  630 , and  632  may be received from a sensor configured to detect muscle oxygenation of a quadriceps muscle of an athlete. Further, the exercise sessions associated with graphs  628 ,  630 , and  632  may include cycling or running sessions, among others, and such that the athlete&#39;s leg muscles are considered active muscles for the exercise sessions (i.e. as opposed to the athlete&#39;s arm muscles, among others). In summary,  FIG.  6 B  schematically depicts that muscle oxygenation percentage of an active quadriceps muscle for a given exercise section may exhibit recovery from an initial decline when exercising within a moderate (graph  632 ) or a heavy (graph  630 ) exercise intensity domain, but that muscle oxygenation percentage will not recover when exercising within a severe exercise intensity domain. 
       FIG.  6 C  schematically depicts three graphs  644 ,  646 , and  648  of muscle oxygenation percentage (y-axis  640 ) versus time (x-axis  642 ). The three graphs  644 ,  646 , and  648  correspond to the three graphs  606 ,  608 , and  610  from  FIG.  6 A  (i.e. the muscle oxygenation percentage data used to plot graphs  644 ,  646 , and  648  may be received from the exercise testing associated with graphs  606 ,  608 , and  610 ). In one example, graphs  644 ,  646 , and  648  may each depict muscle oxygenation percentage associated with a forearm muscle of the athlete. As such, graphs  644 ,  646 , and  648  may be associated with an inactive muscle for an exercise session that concentrates on the athlete&#39;s leg muscles (e.g. cycling or running, among others). As such, graphs  644 ,  646 , and  648  may be plotted from data detected by a same sensor type (as described in further detail in relation to FIG.  7 ), and detected from an approximately same location on the athlete&#39;s body (i.e. proximate a forearm muscle of the athlete). 
     In one example, graphs  644 ,  646 , and  648  may exhibit similar trends to graphs  628 ,  630 , and  632  from  FIG.  6 B . In particular, graph  644  depicts a steady decline in muscle oxygenation percentage, without exhibiting an increase in muscle oxygenation percentage before the end of the exercise (at point  618 ). As such, graph  644  may be associated with a severe exercise intensity domain, and with graph  606  from  FIG.  6 A . Graph  646  schematically depicts a progression of muscle oxygenation percentage of a forearm muscle of an athlete exercising within a heavy exercise intensity domain (i.e. exercising at an intensity corresponding to graph  608 ). As depicted in  FIG.  6 C , graph  646  depicts a decline in muscle oxygenation percentage during a first time period, up until point  650 . At point  650 , graph  646  exhibits a recovery in muscle oxygenation percentage before the exercise is completed at time  620 . Graph  648  schematically depicts a progression of muscle oxygenation percentage of a forearm muscle of the athlete exercising within a moderate exercise intensity domain (i.e. exercising at an intensity corresponding to graph  610 ). As depicted in  FIG.  6 C , graph  648  depicts a decline in muscle oxygenation percentage during a first time period, up until point  652 . At point  652 , graph  648  exhibits a recovery in muscle oxygenation percentage before the exercise is completed at time  622 . 
     Accordingly,  FIGS.  6 B and  6 C  schematically depict that similar trends in muscle oxygenation percentage may be exhibited by both active and inactive muscles when exercising within moderate, heavy, and severe exercise intensity domains. In this way, a muscle oxygenation sensor, such as that described in further detail in relation to  FIG.  7   , may be positioned on an active or inactive muscle in order to detect useful activity data for an athlete. 
       FIG.  7    schematically depicts an activity monitoring device  700 . In one example, the activity monitoring device  700  may include one or more elements and/or functionality similar to devices  112 ,  114 ,  128 ,  200 , and/or  400 , among others. Accordingly, the activity monitoring device  700  may comprise a processor  702 , which may be similar to one or more of processors  202 - 1  and  202 - 2 . Processor  702  may comprise one or more processing cores configured to execute one or more computational instructions in parallel. Additionally or alternatively, processor  702  may utilize one or more processing cores to execute computational instructions in series, or a combination of series and parallel processing. Further, processor  702  may be embodied with any computational clock speed (clock speed may be related to a rate at which computational instructions may be executed by the processor  702 ) disclosed herein or generally known in the art. In one example, the processor  702  may be configured to execute computer-executable instructions stored on a non-transitory computer-readable medium, such as memory  704 . As such, memory  704  may be similar to memory  212 , and may include, but may not be limited to, persistent or volatile memory. As such, memory  704  may include one or more of random access memory (RAM), read only memory (ROM), solid-state memory, optical or magnetic storage, and/or any other medium that can be used to store electronic information. 
     In one implementation, electrical energy may be provided to one or more of the components (i.e. components  702 ,  704 ,  706 ,  708 , and/or  710 ) of the activity monitoring device  700  by a power supply  703 . As such, the power supply  703  may comprise one or more of a battery, a photovoltaic cell, a thermoelectric generator, or a wired electrical supply from an external source. Further, the power supply  703  may be configured to supply one or more components of the activity monitoring device  700  with an electrical output having any voltage, and configured to supply any current, without departing from the scope of these disclosures. 
     The activity monitoring device  700  may include a sensor  706 . As such, sensor  706  may comprise an accelerometer, a gyroscope, a location-determining device (e.g., GPS), temperature sensor (including ambient temperature and/or body temperature), sleep pattern sensors, heart rate monitor, image-capturing sensor, moisture sensor, force sensor, compass, angular rate sensor, and/or combinations thereof among others. In one implementation, the activity monitoring device  700  may include an interface  708 . As such, the interface  708  may be embodied with hardware and/or firmware and software configured to facilitate communication between the activity monitoring device  700  and external device or network, not depicted in  FIG.  7   . In one example, the interface  708  may facilitate wireless and/or wired communication between the activity monitoring device  700  and an external device or network. In one example, interface  708  may facilitate communication using one or more of Wi-Fi, Bluetooth, an Ethernet cable, a USB connection, or any other connection type disclosed herein or known in the art. As such, interface  708  may facilitate communication between the activity monitoring device  700  and an external device across a local area network (LAN), a wide area network (WAN), or the Internet, among others. Additionally or alternatively, interface  708  may facilitate communication between the activity monitoring device  708  and a user interface. As such, a user interface may include a display device, such as display device  116  and/or one or more input interfaces (e.g. one or more button interfaces, touchscreen interfaces, microphone interfaces, and the like). 
     Additionally or alternatively, the activity monitoring device  700  may include a muscle oxygenation sensor  710 . In one example, the muscle oxygenation sensor  710  may be configured to emit electromagnetic radiation in a near-infrared wavelength range. As such, the muscle oxygenation sensor  710  may utilize near-infrared spectroscopy (NIRS). By positioning the muscle oxygenation sensor  710  proximate an area of skin  716  of a user, the emitted electromagnetic radiation, which is schematically depicted by arrow  722 , may travel into the user&#39;s body through, in one example, a layer of skin  716 , fat  718 , and into a muscle tissue  720 . In one example, oxyhemoglobin and deoxyhemoglobin may act as chromophores (absorbing differing amounts of light at different wavelengths). Further, oxyhemoglobin and deoxyhemoglobin may exhibit comparatively larger differences in absorption characteristics across a range of near infrared electromagnetic radiation. As such, the emitter  712  may be configured to emit electromagnetic radiation in a near infrared spectrum having a wavelength range of approximately 600 to 900 nm. In another example, the emitter  712  may be configured to emit infrared light having a wavelength range of approximately 630 to 850 nm. In yet another example, the emitter  712  may be configured to emit infrared light across another range, without departing from the scope of these disclosures. In one example, the emitter  712  may comprise one or more light emitting diode (LED) elements. In one specific example, the emitter  712  may comprise four light emitting diodes. 
     Accordingly, a portion of light emitted from the emitter  712  may be backscattered and detected by detector  714 . In one example, line  724  may represent a portion of back scattered light detected by detector  714 . As such, the muscle oxygenation sensor  710  may be configured to calculate an attenuation in intensity between emitted and detected light. This attenuation may be related to an amount of light absorbed by, among others, the oxyhemoglobin and deoxyhemoglobin chromophores. As such, by detecting an attenuation in emitted near infrared light, the muscle oxygenation sensor  710  may determine a concentration of oxyhemoglobin or deoxyhemoglobin. In turn, based upon the determined concentration of oxyhemoglobin or deoxyhemoglobin, the muscle oxygenation sensor  710  may calculate a muscle oxygenation percentage associated with the muscle  720 . 
     In one implementation, the muscle oxygenation sensor  710  may calculate a muscle oxygenation percentage associated with muscle tissue  720  according to the Beer-Lambert law: 
       log(I out /I in )=ε.L.c
 
     where I in  is an intensity of near-infrared radiation emitted from the emitter  712 , I out  is an intensity of near-infrared radiation detected by detector  714 , ε is a molar attenuation coefficient of the chromophore, c is an amount concentration of the chromophore, and l is a path length that the emitted near infrared radiation travels through the body (i.e. one or more of skin  716 , fat  718 , and muscle  720 . 
     It will be appreciated that the light emitted from emitter  712  is schematically represented by line  722 , and that portion of emitted light detected by detector  714  is schematically represented by line  724 . In practice, a path of light emitted from emitter  712  traveling into a user&#39;s body (i.e. through one or more of skin  716 , fat  718 , and muscle  720 ), and light detected by detector  714  may be complex, and comprise a plurality of different paths. 
     In one example, the muscle oxygenation sensor  710  of the activity monitoring device  700  may be configured to be positioned proximate an area of skin  716  of a user. As such, in one example, the emitter  712  and the detector  714  may be positioned such that there exists substantially no separation between the emitter  712  and the skin  716 , and similarly, substantially no separation between the detector  714  and the skin  716 . In another example, the activity monitoring device  700  may be configured to be positioned proximate an area of skin  716  of a user, such that a gap between the emitter  712  and the skin  716 , and/or the detector  714  in the skin  716  does not include a layer of clothing. In yet another example, the activity monitoring device  700  may be configured to be positioned proximate an area of skin  716  of a user, such that one or more layers of clothing may be positioned between the emitter  712  and the skin  716 , and/or the detector  714  and the skin  716 . 
     In one example, the activity monitoring device  700 , and in particular, the muscle oxygenation sensor  710 , may be utilized to determine (in one example, to calculate) a critical intensity and/or an anaerobic work capacity associated with a user. In one specific example, the muscle oxygenation sensor  710  may be utilized to determine a critical muscle oxygenation percentage at which a user reaches a critical intensity of exercise. Accordingly,  FIG.  8    schematically depicts a flowchart diagram  800  that may be utilized to calculate one or more of a critical tissue oxygenation percentage and/or an anaerobic work capacity of a user from data outputted from a tissue oxygenation sensor, such as sensor  710 . As such, the critical tissue oxygenation percentage may, in one example, be a critical tissue oxygenation percentage of muscle tissue, among others. 
     In one implementation, in order to calculate one or more of a critical tissue oxygenation percentage and/or an anaerobic work capacity, a user may provide an activity monitoring device, such as device  700 , with test data. In one example, test data may be generated by the sensor, such as sensor  710 , during an exercise period, which may otherwise be referred to as an exercise session. In one example, an exercise period may comprise a prescribed duration during which a user is instructed to run as quickly as possible (i.e. as far as possible) within the prescribed time limit. In certain specific examples, an exercise period may instruct a user to run as far as possible within, for example, a minute, two minutes, three minutes, four minutes, five minutes, six minutes, seven minutes, eight minutes, nine minutes, 10 minutes, 12 minutes, 15 minutes, 20 minutes, or any other duration. Accordingly, a tissue oxygenation sensor, such as sensor  710 , may be configured to output a tissue oxygenation percentage data point each second during an exercise period. Alternatively, the tissue oxygenation sensor, such as sensor  710 , may be configured to output a tissue oxygenation percentage data point at a different frequency, which may be 0.25 Hz, 0.5 Hz, 2 Hz, 3 Hz, 4 Hz, or any other frequency. In one example, an exercise period prescribed for a user in order to generate test data may ensure that the user exercises at an intensity above a critical intensity for the user during at least a portion of the prescribed duration of the exercise period. Accordingly, in one example, one or more processes may be executed to instruct a user to begin an exercise period at block  802  of flowchart  800 . 
     As previously discussed, a tissue oxygenation sensor, such as sensor  710 , may be used to detect, and to output data indicative of, a tissue oxygenation percentage. In one example, the tissue oxygenation sensor  710  may output a data point indicative of a current tissue oxygenation percentage for each second of an exercise period. Accordingly, in one example, the outputted tissue oxygenation data may be received for further processing by, in one example, processor  702 , at block  804  of flowchart  800 . 
     In one implementation, a tissue oxygenation percentage for each second of a prescribed exercise period may be stored. As such, tissue oxygenation percentages for each second of a prescribed exercise period may be stored in, for example, memory  704 . Upon completion of a given exercise period, a number may be calculated corresponding to a total number of the stored tissue oxygenation percentages for each second of a duration of a prescribed exercise period. This number may be referred to as a total number of tissue oxygenation data points for an exercise period. This total number of tissue oxygenation data points may be calculated by, in one example, processor  702 , and at block  806  of flowchart  800 . 
     An exercise period utilized to generate data in order to determine a critical muscle oxygenation percentage and/or an anaerobic work capacity of the user may be summarized as a data point comprising two pieces of information. This exercise period summary data point may comprise the total number of tissue oxygenation data points, as determined, in one example, at block  806 , in addition to the total time (i.e. the duration) of the exercise period/session. In one example, the two pieces of information (i.e. the total number of tissue oxygenation data points, and the total time) may be expressed as a coordinate point. In one example, this coordinate point P may be of the form P(x 1 , y 1 ), where y 1  may be the total number of tissue oxygenation data points (muscle oxygenation percentage (%)×time (s)), and x 1  may be the total time (s). In this way, the exercise period summary data point expressed as a coordinate point may be plotted, as schematically depicted in  FIG.  9   . In one example, the exercise period summary data point may be calculated at block  808  of flowchart  800 . 
     In one implementation, in order to calculate one or more of a critical tissue oxygenation percentage and/or an anaerobic work capacity of a user, two or more exercise period summary data points may be utilized. In one example, the durations of the exercise periods used to generate the two or more exercise period summary data points may be different. Accordingly, in one example, one or more processes may be executed to determine whether a threshold number of exercise periods have been completed by the user in order to calculate one or more of a critical tissue oxygenation percentage and/or an anaerobic work capacity for the user. As previously described, this threshold number of exercise periods may be at least two, at least three, at least four, or at least five, among others. In one specific example, one or more processes may be executed by processor  702  to determine whether the threshold number of exercise periods have been completed at decision block  810  of flowchart  800 . Accordingly, if the threshold number of exercise periods has been met or exceeded, flowchart  800  proceeds to block  812 . If, however, the threshold number of exercise periods has not been met, flowchart  800  proceeds from decision block  810  back to block  802 . 
     In one example, a regression may be calculated using the two or more exercise period summary data points that were calculated from two or more prescribed exercise periods. This regression may be a linear, or a curvilinear regression. As such, any computational processes known in the art for calculation of a linear or curvilinear regression may be utilized with this disclosure. In one implementation, at least a portion of a calculated regression may be utilized to determine one or more of a critical tissue oxygenation percentage and/or an anaerobic work capacity of the user. In one specific example, one or more processes may be executed to calculate a regression at block  812  of flowchart  800 . 
     At least a portion of a regression calculated using the two or more exercise period summary data points may be utilized to determine a critical tissue oxygenation percentage for a user. Specifically, the critical tissue oxygenation percentage may correspond to a slope of the regression line (or a slope of a linear portion of a curvilinear regression). One or more processes may be executed to output a critical tissue oxygenation percentage calculated as a slope of a regression line through the two or more exercise period summary data points at block  814  of flowchart  800 . 
     At least a portion of a regression calculated using the two or more exercise period summary data points may be utilized to determine an anaerobic capacity of the user. Specifically, the anaerobic capacity may correspond to an intercept of the regression line (or an intercept of a linear portion of a curvilinear regression). In one example, the anaerobic capacity may be expressed as a total number of tissue oxygenation data points (tissue oxygenation percentage (%)×time (s)) above a critical oxygenation percentage (%). In one implementation, one or more processes may be executed to output an anaerobic capacity calculated as an intercept of a regression line through the two or more exercise period summary data points at block  816  of flowchart  800 . 
       FIG.  9 A  is a chart that plots testing data from multiple exercise periods, or sessions, for a given user. In particular,  FIG.  9 A  is a chart  900  plotting total muscle oxygenation points  902  against time  904 . Points  906 ,  908 , and  910  are each exercise period summary data points, as described in relation to  FIG.  8   . As such, the exercise period summary data points  906 ,  908 ,  910  may be calculated for a same user, and for, in this example, three separate exercise sessions. Accordingly, each of the exercise period summary data points  906 ,  908 , and  910  may represent a separate exercise session. In particular, the exercise sessions associated with the exercise period summary data points  906 ,  908 , and  910  may have durations of approximately 300 seconds, 720 seconds, and 900 seconds, respectively. Further, during the respective exercise sessions, muscle oxygenation percentage data detected for the user may be integrated for each second of a total duration of a given exercise session to give a total number of muscle oxygenation points equal to approximately 6000, 25,000, and 30,000 for the respective exercise period summary data points  906 ,  908 , and  910 . 
     In one example, the exercise period summary data points  906 ,  908 ,  910  may each represent a separate exercise session, and such that a portion of each of these exercise sessions is carried out within a severe exercise intensity domain for a user. In one implementation, the exercise period summary data points  906 ,  908 ,  910  may each represent a separate exercise session carried out in a continuous manner (nonstop exercise without breaks, e.g. continuous running and/or cycling). However, in another implementation, one or more of the exercise period summary data points  906 ,  908 ,  910  may each represent a separate exercise session carried out in an intermittent manner (a non-continuous exercise session with one or more periods of inactivity/low activity and one or more periods of high activity, e.g. participation in team sports, such as basketball, soccer, and the like). 
     In one implementation, a regression line  912  may be calculated using the three exercise period summary data points  906 ,  908 , and  910 , as plotted on chart  900 . In one example, this regression line  912  may be of the form: 
     
       
      
       y=m 
       a 
       x+c 
       a  
      
     
     where y is a total number of muscle oxygenation points (y-axis), x is a time (s) (x-axis), m a  is the slope of the regression line  912 , and c a  is the intercept of the regression line  912  on the y-axis. 
     For the example experimental data used to generate the exercise period summary data points  906 ,  908 , and  910 , the regression line  912  may have the form: y=39.62x−5112.13, with an r 2  value of 0.99999. It is noted that this regression line  912  formula is merely included as one example result, and may not correspond to the example values discussed above for exercise period summary data points  906 ,  908 , and  910 . 
     In one example, a regression line, such as regression line  912 , through two or more exercise period summary data points, such as the exercise period summary data points  906 ,  908 , and  910 , may be used to calculate a critical muscle oxygenation percentage and/or a total number of muscle oxygenation points above a critical muscle oxygenation percentage (which may be proportional to an anaerobic work capacity) for a user. In one example, given regression line  912  of the form: y=mx+c, the critical muscle oxygenation percentage may be equal to m, the slope of the regression line  912 , and the total number of muscle oxygenation points above the critical muscle oxygenation percentage may be equal to c (or |c|, the absolute value of c), the intercept of the regression line  912  on the y-axis. In particular, given the experimental data depicted in chart  900 , the critical muscle oxygenation percentage for the user may be 39.62%, and the total number of muscle oxygenation points above the critical muscle oxygenation percentage may be 5112.13. 
     In another example, the regression line  912  may be calculated through a plurality of exercise period summary data points greater than the three exercise period summary data points  906 ,  908 ,  910  depicted in  FIG.  9 A . Further, any methodology known in the art for calculation of a linear regression may be utilized with these disclosures to calculate a regression line  912 . Additionally, while  FIG.  9 A  graphically depicts a regression line  912 , the activity monitoring device  700  may be configured to calculate a critical muscle oxygenation percentage for a user and/or a total number of muscle oxygenation points above a critical muscle oxygenation percentage, without requiring that a regression line be plotted. I.e. the activity monitoring device  700  may calculate one or more of a critical muscle oxygenation percentage and/or a total number of muscle oxygenation points above a critical muscle oxygenation percentage from muscle oxygenation data outputted from the muscle oxygenation sensor  710  without calculating and/or plotting a regression line through exercise period summary data points. As such, the depiction of regression line  912  may a pictorial description of methodology used by the activity monitoring device  700 ; however the activity monitoring device  700  may utilize alternative computational processes to calculate the same resulting critical muscle oxygenation percentage and/or total number of muscle oxygenation points above the critical muscle oxygenation percentage. 
       FIG.  9 B  depicts a chart  920  plotting test data from multiple exercise periods for a same user. In particular,  FIG.  9 B  depicts chart  920  plotting muscle oxygenation percentage (%)  922  against inverse time (s −1 )  924 . In one example, the data points  926 ,  928 , and  930  may each represent a separate exercise session. In one implementation, a data point, from the data points  926 ,  928 ,  930 , may be of the form (x 2 ,y 2 ), where y 2  is an average muscle oxygenation percentage over a total exercise session, and is calculated, in one example, as a sum of muscle oxygenation percentages for each second of an exercise period (in seconds), divided by the duration of the exercise period (in seconds). Those of ordinary skill in the art will recognize that the time resolution (or sampling rate) utilized may be different than the one second resolution described herein, without departing from the scope of these disclosures. For example, y 2  may be calculated, in another example, as a sum of muscle oxygenation percentages for each half second interval an exercise period divided by the total number of half seconds in the exercise period, among many other resolutions. Accordingly, x 2  may be calculated as 1/(total duration of a given exercise period), giving a result as an inverse time, with units of seconds −1  (s −1 ). In one specific implementation, data point  926  may include the same information as exercise period summary data point  910 . Similarly, data point  928  may include the same information as exercise period summary data point  908 , and data point  930  may include the same information as exercise period summary data point  906 . 
     In one implementation, a regression line  932  may be calculated using two or more data points, such as data points  926 ,  928 ,  930 . In one example, the regression line  932  may be of the form: 
     
       
      
       y=m 
       b 
       x+c 
       b  
      
     
     where y is a muscle oxygenation percentage (%) (y-axis), x is an inverse time (s −1 ) (x-axis), m b  is the slope of the regression line  932 , and c b  is the intercept of the regression line  932  on the y-axis. 
     In one example, for the specific data depicted in chart  920 , the regression line  932  may have the form: y=−102.35x+39.6, with an r 2  value of 0.99999. Accordingly, m b  the slope of the regression line  932  (or |m b | the absolute value), may equal the number of muscle oxygenation points above a critical muscle oxygenation percentage for a user. Similarly, c, the intercept may be equal to the critical muscle oxygenation percentage for the user. 
       FIG.  10    is a flowchart diagram  1000  that may be utilized to determine whether a user is exercising at an unsustainable work rate within a severe exercise intensity domain. In one implementation, an activity monitoring device, such as device  700 , may receive tissue oxygenation data indicative of a real-time tissue oxygenation percentage for the user while exercising. Accordingly, this tissue oxygenation data may be generated by, in one example, muscle oxygenation sensor  710 . As such, one or more processes may be executed by, in one example, processor  702 , to receive the tissue oxygenation data. These one or more processes to receive the tissue oxygenation data may be executed at block  1002  of flowchart  1000 . 
     In one implementation, received tissue oxygenation data may be compared to a critical tissue oxygenation percentage for the user. As such, a critical oxygenation percentage for a user may be calculated by an activity monitoring device, such as processor  702  of device  700 , and stored memory, such as memory  704 . Further, the critical muscle oxygenation percentage for a user may be calculated using one or more processes described in relation to  FIG.  8   . In one example, the received tissue oxygenation data may be compared to the critical tissue oxygenation percentage for the user by processor  702 . As such, one or more processes executed by processor  702  to compare the received tissue oxygenation data to the critical tissue oxygenation percentage for the user may be executed at block  1004  of flowchart  1000 . 
     A comparison of received tissue oxygenation data to a critical tissue oxygenation percentage for a user may include a determination as to whether the received, real-time tissue oxygenation percentage is above or equal to the critical tissue oxygenation percentage. This determination may represented by decision block  1006  of flowchart  1000 . Accordingly, if the received tissue oxygenation data represents a tissue oxygenation percentage that is equal to or above a critical tissue oxygenation percentage for the user, the activity monitoring device  700  may output a signal indicating that the user is exercising at a sustainable work rate. In one example, this output signal may be communicated to a user via one or more indicator lights, a user interface, an audible signal, or a haptic feedback signal, among others. As such, the output signal may be communicated through interface  708  of the activity monitoring device  700 . In one example, the one or more processes executed to output a signal indicating that the user is exercising at a sustainable work rate (i.e. outside of a severe exercise intensity domain) may be executed at block  1008  of flowchart  1000 . In one implementation, if the received tissue oxygenation data represents a tissue oxygenation percentage that is below a critical tissue oxygenation percentage for the user, the activity monitoring device  700  may output a signal indicating that the user is exercising at an unsustainable work rate. In one example, this output signal may be delivered in a similar manner to the output signal described in relation to block  1008 . Further, the one or more processes executed to output a signal indicating that the user is exercising at an unsustainable work rate (i.e. within a severe exercise intensity domain) may be executed at block  1010  of flowchart  1000 . 
       FIG.  11    is a flowchart diagram  1100  that may be utilized to determine if a user is exercising at an unsustainable, a sustainable, or a critical work rate. In one implementation, an activity monitoring device, such as device  700 , may receive periodic data from a tissue oxygenation sensor, such as sensor  710 , indicative of a real-time oxygenation percentage of a tissue of a user while exercising. Accordingly, tissue oxygenation percentage data may be received by, in one example, processor  702 , from sensor  710 , and with a periodicity of one sample per second (1 Hz). In another implementation, the sensor  710  may output a data point indicative of a tissue oxygenation percentage once every two seconds (0.5 Hz), once every three seconds (0.33 Hz), or once every four seconds (0.25 Hz), among others. Further, tissue oxygenation data may be generated by, and received from, the sensor  710  at any rate, without departing from the scope of these disclosures. In one example, one or more processes may be executed to receive, by a processor, such as processor  702 , periodic data from a tissue oxygenation sensor indicative of a tissue oxygenation percentage at block  1102  of flowchart  1100 . 
     In one implementation, tissue oxygenation data received from a tissue oxygenation sensor, such as sensor  710 , may be stored in memory, such as memory  704 . Accordingly, in one example, a trend in tissue oxygenation data may be calculated based upon a comparison of a most recently-received tissue oxygenation percentage data point to one or more previously-stored tissue oxygenation percentage data points. In one example, one or more processes may be executed by processor  702  of the activity monitoring device  700 , to calculate a change in tissue oxygenation percentage over a time spanning between a saved tissue oxygenation percentage data point, and a most recently-received tissue oxygenation percentage data point. In one implementation, this change may be calculated as a positive number, which may be indicative of an increase in tissue oxygenation percentage, as the negative number, which may be indicative of a decrease in tissue oxygenation percentage, or as a zero value, which may be indicative of no change in tissue oxygenation percentage. In another implementation, one or more processes may be executed by processor  702  of the activity monitoring device  700  to calculate a trend in tissue oxygenation percentage as a slope of a regression line, and calculated using two or more tissue oxygenation percentage data points. As such, if the slope of the calculated line has a negative value, it may be indicative of a decrease in tissue oxygenation percentage. Similarly, if the slope of the line is calculated as having a positive value, it may be indicative of an increase in tissue oxygenation percentage, and if the slope of the line is calculated as having a zero value, it may be indicative of no change in tissue oxygenation. In one example, one or more processes for calculation of a tissue oxygenation trend may be executed at block  1104  of flowchart  1100 . 
     In additional or alternative implementations, a trend in tissue oxygenation may be calculated, such as at block  1104 , according to the one or more processes described in relation to  FIG.  15   . 
     Decision block  1106  may represent one or more processes executed by processor  702  to determine if the calculated tissue oxygenation trend from block  1104  represents a negative trend. Accordingly, if it is determined that the calculated tissue oxygenation trend is negative, flowchart  1100  may proceed to block  1108 . In one implementation, upon determining that data received from a tissue oxygenation sensor is representative of a negative trend, one or more processes may be executed to output a signal indicating that the user may be exercising at an unsustainable work rate. As such, one or more processes configured to output a signal indicating that the user may be exercising at an unsustainable work rate may be executed at block  1108 . In another implementation, if it is determined that data received from a tissue oxygenation sensor is not representative of a negative trend, flowchart  1100  may proceed to decision block  1110 . Accordingly, decision block  1110  may be associated with one or more processes executed to determine whether the calculated tissue oxygenation trend is positive. If it is determined that the calculated tissue oxygenation trend is positive, flowchart  1100  may proceed to block  1112 . Accordingly, in one example, if it is determined that the calculated tissue oxygenation trend is positive, a signal may be outputted to indicate that the user is exercising at a sustainable work rate. In one example, the output signal indicating that the user is exercising at a sustainable work may be executed at block  1112  of flowchart  1100 . In another example, if it is determined that the calculated tissue oxygenation trend is not positive, flowchart  1100  may proceed to block  1114 . In this way, it may be determined that the calculated tissue oxygenation trend is approximately level (unchanged). As such, a level tissue oxygenation trend may be indicative of a user exercising at a critical work rate. Accordingly, in response to determining that the tissue oxygenation trend is approximately level, one or more processes may be executed to output a signal indicating that the user is exercising at a critical work rate. In one implementation, these one or more processes may be executed at block  1114  of flowchart  1100 . 
     It is noted that flowchart  1100  may calculate a tissue oxygenation trend from two or more data points indicative of muscle oxygenation percentages at two or more different time points. As such, any numerical methodology known in the art may be utilized to calculate a trend between two or more such points, including, among others, calculation of a slope of a line connecting two points, or calculation of a regression line using a plurality of points, among others. 
       FIG.  12    depicts two graphs of data generated during a same exercise session. The two depicted graphs include muscle oxygenation percentage data  1208  and running speed data  1210  plotted against a common timescale  1206 . In one implementation, the muscle oxygenation percentage data  1208  may be generated by a muscle oxygenation sensor, such as sensor  710 . Further, the running speed data  1210  may be calculated based on sensor data generated by sensor  706 , which may include, among others, an accelerometer, or a location-determining sensor. Accordingly, the graph of running speed  1210  may be associated with scale  1202 , and the graph of muscle oxygenation  1208  may be associated with scale  1204 . In one example, graphs  1208  and  1210  schematically depict relationships between muscle oxygenation percentage and a running speed. In one example, the period between points  1218  and  1220  on the muscle oxygenation percentage graph  1208  may represent a substantially level trend in muscle oxygenation percentage. Accordingly, points  1214  and  1216  on the speed graph  1210  may correspond to points  1218  and  1220 , and such that the approximately level trend in muscle oxygenation percentage between points  1218  and  1220  corresponds to a critical speed, as schematically indicated by line  1212 . In another example, a substantially negative trend in muscle oxygenation percentage between points  1220  and  1224  on the muscle oxygenation graph  1208  may correspond to an increase in speed above the critical speed between points  1216  and  1222  on the speed graph  1210 . In yet another example, a positive trend in muscle oxygenation between points  1226  and  1228  on the muscle oxygenation graph  1208  may correspond to a decrease in speed below a critical speed between points  1230  and  1232  on the speed graph  1210 . 
       FIG.  13    is a flowchart diagram  1300  that may be utilized to determine if a user is exercising within a severe exercise intensity domain. In one implementation, an activity monitoring device, such as device  700 , may receive periodic data from a tissue oxygenation sensor, such as sensor  710 , indicative of a real-time oxygenation percentage of a tissue of a user while exercising. Accordingly, tissue oxygenation percentage data may be received by, in one example, processor  702 , from sensor  710 , and with a periodicity of one sample per second (1 Hz). In another implementation, the sensor  710  may output a data point indicative of a tissue oxygenation percentage once every two seconds (0.5 Hz), once every three seconds (0.33 Hz), once every four seconds (0.25 Hz), among others. Further, tissue oxygenation data may be generated by, and received from, the sensor  710  at any rate, without departing from the scope of these disclosures. In one example, one or more processes may be executed to receive, by a processor, such as processor  702 , periodic data from a tissue oxygenation sensor indicative of a tissue oxygenation percentage at block  1302  of flowchart  1300 . 
     In one implementation, tissue oxygenation data received from a tissue oxygenation sensor, such as sensor  710 , may be stored in memory, such as memory  704 . Accordingly, in one example, a trend in tissue oxygenation data may be calculated based upon a comparison of a most recently-received tissue oxygenation percentage data point, to one or more previously-stored tissue oxygenation percentage data points. In one example, one or more processes may be executed by processor  702  of the activity monitoring device  700  to calculate a change in tissue oxygenation percentage over a time spanning between a saved tissue oxygenation percentage data point, and a most recently-received tissue oxygenation percentage data point. In another implementation, one or more processes may be executed by processor  702  of the activity monitoring device  700  to calculate a trend in tissue oxygenation percentage as a slope of a regression line calculated using two or more tissue oxygenation percentage data points. In one example, one or more processes for calculation of a tissue oxygenation trend may be executed at block  1304  of flowchart  1300 . 
     Decision block  1306  may represent one or more processes executed by processor  702  to determine if the calculated tissue oxygenation trend from block  1304  represents a negative trend. Accordingly, if it is determined that the calculated tissue oxygenation trend is negative, flowchart  1300  may proceed to decision block  1308 . In one implementation, decision block  1308  may execute one or more processes to calculate an absolute value of a negative trend (negative slope) identified at decision block  1306 . Additionally, decision block  1308  may represent one or more processes configured to compare the absolute value of the negative trend to a threshold value. In one example, if the absolute value is above a threshold value, flowchart  1300  may proceed to block  1310 . Accordingly, the threshold value may comprise any value, without departing from the scope of these disclosures. In one example, upon determining that the absolute value is above a threshold value, one or more processes may be configured to output a signal indicating that the user is exercising in a severe intensity domain. As such, these one or more processes configured to output a signal indicating that the user is exercising within a severe intensity domain may be executed at block  1310 . If, however, the absolute value is below a threshold, flowchart  1300  may proceed to block  1312 . Accordingly, block  1312  may comprise one or more processes that may be executed to output a signal indicating that the user is exercising at an unsustainable work rate. 
     In another implementation, if it is determined that data received from a tissue oxygenation sensor is not representative of a negative trend, flowchart  1300  may proceed to decision block  1314 . Accordingly, decision block  1314  may be associated with one or more processes executed to determine whether the calculated tissue oxygenation trend is positive. If it is determined that the calculated tissue oxygenation trend is positive, flowchart  1300  may proceed to block  1316 . Accordingly, in one example, if it is determined that the calculated tissue oxygenation trend is positive, a signal may be outputted to indicate that the user is exercising at a sustainable work rate. In one example, the output signal indicating that the user is exercising at a sustainable work may be executed at block  1316  of flowchart  1300 . In another example, if it is determined that the calculated tissue oxygenation trend is not positive, flowchart  1300  may proceed to block  1318 . In this way, it may be determined that the calculated tissue oxygenation trend is approximately level (unchanged). As such, a level tissue oxygenation trend may be indicative of a user exercising at a critical work rate. Accordingly, in response to determining that the tissue oxygenation trend is approximately level, one or more processes may be executed to output a signal indicating that the user is exercising at a critical work rate. In one implementation, these one or more processes may be executed at block  1318  of flowchart  1300 . 
       FIG.  14 A  depicts two graphs plotted using data from two separate exercise sessions participated in by a same user. In particular, graph  1406  comprises output data from a ramped work rate exercise session. In one example, work rate (W) may be depicted on the y-axis  1402 . Accordingly, the data associated with graph  1406  may be generated from data outputted during an exercise session that prescribes a linearly-increasing work rate that increases from a work rate below a critical intensity, to a work rate above a critical intensity for the user. Graph  1408  may be generated from data outputted during an exercise session that prescribes a constant work rate. In one example, the constant work rate associated with graph  1408  may be approximately 15% above a critical intensity for the user. Accordingly, the exercise session associated with graph  1408  may be within a severe exercise intensity domain for the user. In one example, the x-axis  1404  represents a percentage of time to the end of an exercise session. Further, point  1410  represents an approximate time at which the ramped work rate exercise session reaches the critical intensity for the user. 
       FIG.  14 B  depicts two graphs plotted using data from the same two separate exercise sessions from  FIG.  14 A . In particular, graph  1424  may correspond to the ramped work rate exercise session associated with graph  1406 . Additionally, graph  1426  may correspond to the constant work rate exercise session associated with graph  1408 . In one example, graphs  1424  and  1426  may be plotted as tissue oxygenation percentage on a y-axis  1420  versus percentage of time to an end of exercise session on an x-axis  1422 . In one example, graphs  1406 ,  1408 ,  1424 , and  1426  may share a common x-axis scale. 
     In one implementation, graph  1426 , which is plotted using data from a constant work rate exercise session with a constant work rate at approximately 15% above a critical intensity for the user, may exhibit a steep slope between points  1432  and  1434  at the beginning of the exercise session (i.e. between approximately 0 and 20% of the time to the end of the exercise session). However, a graph  1426  may transition to a shallower slope between points  1434  and  1430  as the constant work rate exercise session is completed. 
     In one example, graph  1424 , which may commence at a work rate below a critical intensity, may exhibit a shallower slope between points  1432  and  1428 . Accordingly, point  1428  may approximately correspond to a point at which the ramped exercise intensity session associated with graph  1406  reaches the critical intensity for the user (i.e. transitions from a heavy to a severe exercise intensity domain for the user). As such, a slope of the graph  1424  may steepen between points  1428  and  1430 . Accordingly, in one example, a slope of graph  1424  between points  1428  and  1430  may represent a slope with absolute value above a threshold value, said threshold value corresponding to a critical intensity for the user. In one example, a slope of graphs  1424  between points  1428  and  1430  may be approximately equal to a slope of graph  1426  between points  1432  and  1434 . 
       FIG.  15    is a flowchart diagram  1500  that may be executed as one or more processes, such as by device  700 , to determine if received tissue oxygenation data represents exercise by a user at a critical intensity. In one example, tissue oxygenation data may be received from a sensor, such as sensor  710  associated with the device  700 . As such, the tissue oxygenation data may correspond to muscle oxygenation, and may be expressed as muscle oxygenation percentages. Accordingly, one or more processes may be executed to receive tissue oxygenation data from a sensor at block  1502  of process  1500 . Further, tissue oxygenation data may be received from a sensor, such as sensor  710 , with any periodicity, or at non-periodic intervals, without departing from the scope of these disclosures. In one implementation, data received from a tissue oxygenation sensor at block  1502  may be stored in memory, such as memory  704 . 
     In one example, a change in tissue oxygenation may be calculated as a difference between a current rolling average and a previous rolling average of tissue oxygenation. Accordingly, a current rolling average may be calculated as an average value of tissue oxygenation percentage over a first duration, whereby the current rolling average may include a most-recently received sensor data point. In another implementation, a current rolling average may be calculated as an average value of tissue oxygenation percentage over a predetermined number of received sensor data points (which may be received with a periodicity, or non-periodically). In certain specific examples, a current rolling average may be calculated as an average muscle oxygenation percentage for those muscle oxygenation percentage data points received during the past five seconds, including a most-recently received data point. However, alternative times for the first duration may be utilized, without departing from the scope of these disclosures. For example, the first duration may be one second, two seconds, three seconds, four seconds, and six seconds, seven seconds, eight seconds, nine seconds, ten seconds, or any other duration. Further, the previous rolling average may be calculated as an average value of tissue oxygenation percentage over a second duration, whereby the previous rolling average does not include the most-recently received sensor data point (i.e. may include at least all the data points used to calculate the current rolling average, except the most-recently received sensor data point). In one example, the previous rolling average may be calculated for a second duration, equal to the first duration. In one implementation, the difference between the present rolling average, and the previous rolling average may be calculated by subtraction, thereby resulting in a percentage muscle oxygenation difference. In one implementation, one or more processes utilized to calculate a change in tissue oxygenation may be executed at block  1504  of process  1500 , and by, in one example, processor  702 . 
     In order to determine whether a calculated change in tissue oxygenation corresponds to a critical tissue oxygenation (critical intensity) for a given user, the calculated change in tissue oxygenation may be compared to a threshold value. In one example, this threshold value of change in tissue oxygenation percentage may include any oxygenation value. In one specific example, the threshold value of change in tissue oxygenation percentage may be less than 0.1 (i.e. the received tissue oxygenation percentage may not correspond to a critical tissue oxygenation percentage unless a difference between a current rolling average of tissue oxygenation percentage and a previous rolling average is less than 0.1 (units of tissue oxygenation percentage)). In another example, the received tissue oxygenation percentage may not correspond to a critical tissue oxygenation percentage unless a difference between a current rolling average of tissue oxygenation percentage and a previous rolling average is less than or equal to 0.1 (units of tissue oxygenation percentage). Additional or alternative tissue oxygenation thresholds may include 0.2, 0.3, 0.4, 0.5, 0.6 among others. In one example, one or more processes may be executed to determine whether a change in tissue oxygenation is less than a threshold at decision block  1506  of process  1500 . 
     If it is determined that a calculated change in tissue oxygenation is greater than or equal to a threshold value (or in another implementation, if it is determined that a calculated change in tissue oxygenation is greater than to a threshold value), one or more processes may be executed to output a signal indicating that the user is not exercising at a critical tissue oxygenation. Accordingly, these one or more processes may be executed at block  1510 , and by a processor, such as processor  702 . If, however, it is determined that the calculated change in tissue oxygenation is less than a threshold value (or in another implementation, less than or equal to the threshold value), flowchart  1500  may proceed to decision block  1508 . 
     Accordingly, decision block  1508  may represent one or more processes executed to determine whether the change in tissue oxygenation (that is less than the previously-described threshold value, or in another implementation, less than or equal to the threshold value) is consistent/steady for a threshold duration (i.e. that the change in tissue oxygenation percentage is less than a threshold change, and for a predetermined threshold duration). Accordingly, any threshold duration may be utilized with these disclosures. In certain specific examples, the threshold duration may be equal to at least one second, at least two seconds, at least three seconds, at least four seconds, at least five seconds, or at least 10 seconds, among others. If it is determined that the calculated change in tissue oxygenation is not consistent for the threshold duration, flowchart  1500  may proceed to block  1510 . If, however, it is determined that the calculated change in tissue oxygenation is consistent for the threshold duration, flowchart  1500  may proceed to block  1512 . 
     As such, upon determining that the calculated change in tissue oxygenation is consistent for a threshold duration, one or more processes may be executed to output a signal indicating that a user is exercising at a critical work rate/critical tissue oxygenation (which may be expressed as a tissue oxygenation percentage). These one or more processes configured to output a signal indicating that the user is exercising at a critical work rate may be executed by processor  702 . Further, one or more processes may be executed at block  1512  to output a tissue oxygenation percentage corresponding to those received sensor values for which the difference between the current rolling average and previous rolling average is less than the threshold. This tissue oxygenation percentage may be a critical tissue oxygenation percentage for the user. 
     In one example, there may be variation in a critical muscle oxygenation percentage calculated for a user during different times of a same exercise. Accordingly, in one example, the critical tissue oxygenation percentage outputted at block  1512  may be averaged across multiple separately-calculated critical tissue oxygenation percentages for a same user during an exercise session, among others. 
     In one implementation, the tissue oxygenation discussed in relation to flowchart  1500 , as well as throughout this disclosure, may comprise a muscle oxygenation for any muscle within a user&#39;s body. Further, this calculated critical tissue oxygenation percentage value may be utilized to calculate an anaerobic work capacity (M′) for the user (in one example, this anaerobic work capacity may be expressed as a total number of muscle oxygenation points), and calculated as a difference between a current muscle oxygenation percentage (MO 2 current ) (above the critical muscle oxygenation percentage) and the critical muscle oxygenation percentage (MO 2 crit ), summed over a duration of an exercise session to fatigue: 
         M′=Σ   t=0   fatigue (MO 2 current −MO 2 crit ) (Units: muscle oxygenation points);   (First anaerobic work capacity equation)
 
       FIG.  16    depicts a graph  1602  of muscle oxygenation percentage (%) plotted on the y-axis  1604  versus duration (time) (s) on the x-axis  1606 . In one example, graph  1602  comprises data points  1608 ,  1610 ,  1612 , and  1614 , wherein data points  1608 ,  1610 ,  1612 , and  1614  represent separate exercise sessions. As such, a data point, from data points  1608 ,  1610 ,  1612 , and  1614 , may be associated with a total time of an exercise session, and a muscle oxygenation percentage associated with that exercise session. In one example, this muscle oxygenation percentage may be an average muscle oxygenation over the total time of the exercise session. In another example, this muscle oxygenation percentage may be a muscle oxygenation percentage at the end of the exercise session, among others. In one implementation, graph  1602  displays a trend in muscle oxygenation percentage for different exercise session durations. In particular, graph  1602  may indicate that a comparatively shorter exercise session, such as that exercise session associated with data point  1614 , may be associated with a lower muscle oxygenation percentage. This trend may be due to a user exercising at a comparatively higher work rate for a comparatively shorter time. In contrast to a data point  1614 , data point  1608  may be associated with a comparatively longer exercise session, and may be associated with a higher muscle oxygenation percentage as a result of a user exercising for a comparatively longer time, and adopting, in one example, a less strenuous pacing strategy in order to conserve energy for the comparatively longer exercise session duration. In one example, graph  1602  may comprise a curvilinear regression plotted through data points  1608 ,  1610 ,  1612 , and  1614 . As such, any processes known in the art may be utilized to construct graph  1610 , without departing from these disclosures. 
       FIG.  17    depicts two graphs of data generated during a same exercise session. The two depicted graphs include muscle oxygenation percentage data  1702  and running speed data  1704  plotted against a common timescale  1706 . In one implementation, the muscle oxygenation percentage data  1702  may be generated by a muscle oxygenation sensor, such as sensor  710 . Further, the running speed data  1704  may be calculated based on sensor data generated by sensor  706 , which may include, among others, an accelerometer, or a location-determining sensor. Accordingly, the graph of running speed  1702  may be associated with scale  1708 , and the graph of muscle oxygenation  1704  may be associated with scale  1710 . In one example, graphs  1702  and  1704  schematically depict relationships between muscle oxygenation percentage and a running speed. In one implementation, given the critical intensity (critical running speed) denoted by line  1712 , and the critical intensity (critical muscle oxygenation percentage) denoted by line  1714 , a relationship between the speed  1702 , and the muscle oxygenation percentage  1704  may be recognized. In particular, when a user&#39;s speed is below a critical speed, such as within shaded area  1716 , a corresponding muscle oxygenation percentage for the user will be above a critical muscle oxygenation percentage  1714 , such as within that shaded area  1718 , and vice versa. 
       FIG.  18    depicts a graph  1802  of power on a y-axis  1804  versus time on the x-axis  1806  for an exercise session. As will be readily appreciated, the data associated with  FIG.  18    may be derived from any exercise/ sport type, without departing from the scope of these disclosures. For example, the graph  1802  may comprise data outputted from a power sensor during a running session, cycling session, tennis game, basketball game, or soccer game, among others. In one example, graph  1802  may comprise power data received by a processor, such as processor  702  of activity monitoring device  700 . As such, the activity monitoring device  700  may comprise, or may be configured to communicate with, a power sensor from which power data is directly outputted, or from which power values may be calculated. Accordingly, as described herein, a power sensor may comprise an accelerometer from which acceleration data input may be utilized to calculate a user&#39;s speed, and further, a user&#39;s rate of energy consumption (power). In another example, a power sensor may comprise a dynamometer that may be operatively coupled to an exercise bike on which a user is exercising, among others. 
     In one example, the data points  1808 ,  1810 , and  1812  may represent calculated critical power values for the user. Accordingly, in one example, these critical power values may be calculated using one or more processes described in relation to  FIG.  19   . As such,  FIG.  19    depicts a flowchart diagram  1900  that may be executed by activity monitoring device  700 . In one implementation, flowchart diagram  1900  may be utilized to calculate a critical power associated with an exercise session undertaken by a user. Further, this exercise session may comprise at least a portion undertaken within a severe exercise intensity domain. In one example, flowchart diagram  1900  may utilize a tissue oxygenation sensor, such as sensor  710 , and a power sensor, which may comprise one or more of a dynamometer, or an accelerometer, among others. In one example, the tissue oxygenation sensor may be configured to output data indicative of a tissue oxygenation percentage with a periodicity, or at a non-periodic rate. Accordingly, a periodicity with which the tissue oxygenation sensor outputs data points indicative of a tissue oxygenation percentage may have any value, without departing from the scope of these disclosures. Further, the activity monitoring device  700  may execute one or more processes to receive tissue oxygenation data at block  1902  of flowchart  1900 . The activity monitoring device  700  may, in one example, execute one or more processes to receive power data from a sensor, at block  1903  of flowchart  1900 . 
     A change in tissue oxygenation may be calculated as a difference between a current tissue oxygenation value and a previous tissue oxygenation value. Accordingly, in one implementation, the current tissue oxygenation value may correspond to a rolling average, and similarly, the previous tissue oxygenation value may correspond to a previous rolling average of tissue oxygenation. As such, the current rolling average may be calculated as an average value of tissue oxygenation percentage over a first duration, whereby the current rolling average may include a most-recently received sensor data point. In another implementation, a current rolling average may be calculated as an average value of tissue oxygenation percentage over a predetermined number of received sensor data points (which may be received with a periodicity, or at a non-periodic rate). In certain specific examples, a current rolling average may be calculated as an average muscle oxygenation percentage for those muscle oxygenation percentage data points received during the past five seconds, including a most-recently received data point. However, alternative times for this first duration may be utilized, without departing from the scope of these disclosures. For example, the first duration may be at least one second, two seconds, three seconds, four seconds, and six seconds, seven seconds, eight seconds, nine seconds, ten seconds. In another example, the first duration may range between zero and one seconds, one and three seconds, two and six seconds, or five and ten seconds, or any other duration or time range. 
     In one example, the previous rolling average may be calculated as an average value of tissue oxygenation percentage over a second duration, whereby the previous rolling average may not include the most-recently received sensor data point (i.e. may include at least all the data points used to calculate the current rolling average, except the most-recently received sensor data point). In one example, the previous rolling average may be calculated for a second duration, equal to the first duration. In one implementation, the difference between the current rolling average, and the previous rolling average may be calculated by subtraction, thereby resulting in a percentage muscle oxygenation difference. In one implementation, one or more processes utilized to calculate a change in tissue oxygenation may be executed at block  1904  of process  1900 , and by, in one example, processor  702 . 
     In one implementation, in order to determine whether a calculated change in tissue oxygenation corresponds to a critical tissue oxygenation (critical intensity) for a given user, the calculated change in tissue oxygenation may be compared to a threshold value. In one example, this threshold value of change in tissue oxygenation percentage may include any oxygenation value. In one specific example, the threshold value of change in tissue oxygenation percentage may be less than or equal to 0.1 (i.e. the received tissue oxygenation percentage may not correspond to a critical tissue oxygenation percentage unless a difference between a current rolling average of tissue oxygenation percentage and a previous rolling average is less than or equal to 0.1 (units of tissue oxygenation percentage)). Additional or alternative tissue oxygenation thresholds may include, among others, 0.2, 0.3, 0.4, 0.5, 0.6. In one example, one or more processes may be executed to determine whether a change in tissue oxygenation is less than a threshold at decision block  1906  of process  1900 . 
     If it is determined that a calculated change in tissue oxygenation is greater than a threshold value, one or more processes may be executed to output a signal (output to, in one example, an interface, such as a graphical user interface, or a wireless interface/transceiver) indicating that the user is not exercising at a critical tissue oxygenation. Accordingly, these one or more processes may be executed at block  1910 , and by a processor, such as processor  702 . If, however, it is determined that the calculated change in tissue oxygenation is less than or equal to a threshold value, flowchart  1900  may proceed to decision block  1908 . 
     Accordingly, decision block  1908  may represent one or more processes executed to determine whether the change in tissue oxygenation (that is less than or equal to the previously-described threshold value) is consistent/steady for a threshold duration (i.e. that the change in tissue oxygenation percentage is less than or equal to a threshold change, and for a predetermined threshold duration). Accordingly, any threshold duration may be utilized with these disclosures. In certain specific examples, the threshold duration may be equal to at least one second, at least two seconds, at least three seconds, at least four seconds, at least five seconds, or at least 10 seconds, or range between approximately 1 and 10 seconds, or 5 and 15 seconds, among others. If it is determined that the calculated change in tissue oxygenation is not consistent for the threshold duration, flowchart  1900  may proceed to block  1910 . If, however, it is determined that the calculated change in tissue oxygenation is consistent for the threshold duration, flowchart  1900  may proceed to block  1912 . 
     As such, upon determining that the calculated change in tissue oxygenation is consistent for a threshold duration, one or more processes may be executed to output a signal indicating that a user is exercising at a critical work rate/critical tissue oxygenation. Specifically, in one example, one or more processes may be executed to output a critical power of the user equal to the current power as indicated by the power sensor at a time corresponding to the identified critical tissue oxygenation. Alternatively, a critical power may correspond to an average power as indicated by the power sensor over a time period corresponding to the calculation of the calculated consistent change in tissue oxygenation. As such, these one or more processes configured to output a critical power to an interface may be executed by processor  702 . Further, one or more processes may be executed at block  1912  to output the critical power corresponding to those received sensor values for which the difference between the current rolling average and previous rolling average is less than or equal to the threshold. 
     In one example, there may be some degree of variation in a calculated/identified critical power for a user based upon multiple calculations of the critical muscle oxygenation during different times of a same exercise. Accordingly, in one example, the critical power outputted at block  1912  may be averaged across multiple separately-calculated critical tissue oxygenation percentages for a same user during an exercise session, among others. Accordingly, in one example, the data points  1808 ,  1810 , and  1812  may represent exemplary data points from a plurality of critical power results corresponding to multiple calculations of critical tissue oxygenation of the user. As such, in one example, the critical power values associated with data points  1808 ,  1810 , and  1812  may be averaged. 
     In one implementation, the tissue oxygenation discussed in relation to flowchart  1900 , as well as throughout this disclosure, may comprise a muscle oxygenation for any muscle within a user&#39;s body. Further, the critical power value calculated at block  1912  may be utilized to calculate an anaerobic work capacity (W′) for the user (in one example, this anaerobic work capacity may be expressed as a power (units: W)), and calculated as a difference between a current muscle oxygenation percentage (Power current ) (above the critical muscle oxygenation percentage) and the critical power (Power crit ), summed over a duration of an exercise session. As such, these calculated differences may be referred to as positive difference values. In one example, an exercise session may end in user fatigue. Accordingly, one or more processes utilized to calculate an anaerobic work capacity may be executed by processor  702  at block  1914 : 
         W′=Σ   t=0   fatigue (Power current −Power crit ) (Units: W);   (Second anaerobic work capacity equation)
 
     In certain examples, a critical velocity and an anaerobic work capacity for a user may be calculated based upon sensor output data indicating, or used to calculate, a speed of the user. As such, a critical velocity and an anaerobic work capacity for a user may be calculated based upon sensor data received from, among others, an accelerometer, a location-determining sensor, or a bicycle speedometer, and without utilizing a tissue oxygenation sensor, as previously described. In one implementation, the present disclosure describes results of a plurality of validation tests utilized to validate a relationship between speed data and a critical intensity for a user. Accordingly, in one example, an end speed may be calculated in order to estimate a critical speed for a user. 
       FIG.  20    depicts a graph  2000  that may be utilized to calculate an end speed of the user during an exercise session, and comprising speed on the y-axis  2002  versus time on the x-axis  2004 . The plotted data points, of which data points  2006 ,  2008 ,  2010  are an exemplary sub-set, comprise measurements of a speed of the user at a given time during a same exercise session. In one example, the end speed, denoted by line  2012 , may be calculated as an average of a sub-set of the plurality of data points that make up the graph  2000 . In particular, the end speed  2012  may be calculated as an average of those data points during a final 30 seconds of the duration of the exercise session (e.g. an average of those data points between lines  2014  and  2016 ). However, an end speed may be calculated as an average speed for different durations, such as, among others, a final 20 seconds, 10 seconds, or 5 seconds of an exercise session, or any other duration. In one example, graph  2000  may represent data points associated with an exercise session having a prescribed duration. Accordingly, the prescribed duration may range from 1 minute to 10 minutes. In one specific example, the exercise session associated with graph  2000  may be a three minute all-out trial, whereby a user is instructed to exercise as a highest subjective intensity level for the prescribed duration (three minutes). Additional or alternative exercise session prescriptions (times and/or intensity levels) may be utilized without departing from the scope of these disclosures. 
     In one example, based upon validation testing comparing calculated end speeds for multiple users across multiple separate exercise sessions, a relationship between a calculated end speed and a critical speed for a given user may was identified. In particular, for a plurality of validation tests, 90% of a sample population of users were found to be able to sustain exercise for up to 15 minutes when exercising between 5% and 10% below a calculated end speed for a three minute all-out trial. Additionally, for the plurality of validation tests, 85% of the sample population of users were found to be able to sustain exercise for up to 20 minutes when exercised between 5% and 10% below the calculated end speed for the three minute all-out trial. Accordingly, in one example, a critical velocity for a user may be estimated by reducing a calculated end speed by 5 to 10% (e.g. calculating 90-95% of an end speed of a user). In one specific example, a critical velocity may be estimated for a user by calculating 92.5% of an end speed, among others. 
     In one implementation, an anaerobic work capacity, expressed as a distance, it may be calculated based upon a calculated end speed for a user. Accordingly, from a plurality of validation testing comparing a distance above end speed to an anaerobic work capacity for a given user, it was found that an anaerobic work capacity may be estimated by increasing a calculated end speed by, in one example, 25% to 35%. In another specific example, an anaerobic work capacity may be estimated by increasing the calculated end speed by 30%. Accordingly, in one example, the distance above end speed may be as that area  2018  from  FIG.  20    (e.g. an integration of differences between speed data points and the calculated end speed  2012  across the duration of the exercise session associated with graph  2000 ). 
       FIG.  21    is a flowchart diagram  2100  that may be utilized to calculate a critical speed and an anaerobic work capacity for a user based upon sensor data indicative of a speed of the user. Accordingly, in one example, one or more processes associated with flowchart diagram  2100  may be executed by an activity monitoring device, such as device  700 . It is noted that flowchart diagram may utilized a sensor, such as sensor  706  of device  700 , but may not utilize an oxygenation sensor, such as sensor  710 . In one example, the activity monitoring device  700  may receive sensor data from a sensor, such as sensor  706 . The received sensor data may comprise data points indicative of a speed of the user at various time points during an exercise session. In another example, the received sensor data points may comprise data indicative of a location of the user, and may be utilized to calculate a speed. In one example, data points may be received periodically, and with any periodicity, without departing from the scope of these disclosures. The received data points may be associated with an exercise session having a prescribed duration and intensity. In particular, the exercise session may comprise a three minute all-out trial that instructs a user to exercise as a highest subjective intensity for a three minute duration. In another example, a prescribed duration of 2 to 5 minutes maybe utilized. In other examples, alternative durations may be utilized, without departing from the scope of these disclosures. In one example, one or more processes may be executed to receive sensor data at block  2102  of flowchart  2100 . 
     An end speed may be calculated from received sensor data as an average speed of a sub-set of a plurality of sensor data points received during an exercise session. In one specific example, an end speed may be calculated as an average speed during a last 30 seconds of the prescribed duration of the exercise session. However, alternative sub-sections of a prescribed duration of an exercise session may be utilized to calculate an end speed, without departing from the scope of these disclosures. In one example, one or more processes may be executed, such as by a processor  702  to calculate an end speed at block  2104  of flowchart  2100 . 
     A distance above the end speed may be calculated by summing differences between instantaneous speeds (Speed current ) and the calculated end speed (Speed end ) across the duration of the exercise session (i.e. between time t=0 and the end of the session, time t=session end). In one implementation, one or more processes may be executed to calculate a distance above an end speed at block  2106  of flowchart  2100 .: 
       Distance above end speed=Σ t=0   session end (Speed current −Speed end ) (U n its: m);   (Distance above end speed equation)
 
     In one example, a critical speed may be calculated/estimated based upon the calculated end speed. In one implementation, a critical speed may be calculated by decreasing the calculated end speed by between 5 and 10%: 
       Speed critical =Speed end *(90-95%). 
     In one specific example, a critical speed may be calculated as 92.5% of a calculated end speed: 
       Speed critical =Speed end *(92.5%). 
     In one implementation, one or more processes may be executed to calculate a critical speed, based upon the calculated end speed, at block  2108  of flowchart  2100 . 
     In one example, an anaerobic work capacity may be calculated based upon the calculated distance above an end speed. Accordingly, the anaerobic work capacity may be calculated as 125% to 135% of a distance above the calculated end speed: 
       Anaerobic work capacity=(distance above end speed)*(125-135%). 
     In one specific example, an anaerobic work capacity may be calculated as 130% of a distance above the calculated end speed: 
       Anaerobic work capacity=(distance above end speed)*(130%). 
     In one implementation, one or more processes may be executed to calculate the anaerobic work capacity, based upon the calculated distance above the end speed, at block  2110  of flowchart  2100 . 
     In one implementation, a critical speed and an anaerobic work capacity associated with a user may be calculated from data received from a sensor configured to output data indicative of a distance traveled by the user during an exercise session (e.g. distance traveled while running, cycling, and the like). As such, the sensor may comprise one or more of an accelerometer, a location determining sensor, or a bicycle speedometer, among others. As such, the sensor may be configured to output data indicative of a location of a user, which may in turn be used to calculate a distance traveled by the user, as well as to determine a time taken to travel the recorded distance. Accordingly,  FIG.  22    schematically depicts a flowchart diagram  2200  that may be utilized to calculate one or more of a critical speed and/or an anaerobic work capacity of a user from data outputted from a sensor, such as sensor  706 . 
     In one implementation, in order to calculate one or more of a critical speed and/or an anaerobic work capacity, a user may provide an activity monitoring device, such as device  700 , with test data. In one example, test data may be generated by a sensor, such as sensor  706 , during an exercise period, which may otherwise be referred to as an exercise session. 
     In one example, an exercise period may comprise a prescribed duration during which a user is instructed to run as quickly as possible (i.e. as far as possible) within the prescribed time limit. In certain specific examples, an exercise period may instruct a user to run as far as possible within, for example, a minute, two minutes, three minutes, four minutes, five minutes, six minutes, seven minutes, eight minutes, nine minutes, 10 minutes, 12 minutes, 15 minutes, 20 minutes, or any other duration. Accordingly, a sensor, such as sensor  706 , may be configured to output a location of the user for each second of an exercise period. In turn, this location data may be utilized to calculate a total distance travelled by the user during the exercise period. Alternatively, the sensor  706 , may be configured to a location data point at a different frequency, which may be 0.25 Hz, 0.5 Hz, 2 Hz, 3 Hz, 4 Hz, or any other frequency. In one example, an exercise period prescribed for a user in order to generate test data may ensure that the user exercises at an intensity above a critical intensity for the user during at least a portion of the prescribed duration of the exercise period. Accordingly, in one example, one or more processes may be executed to instruct a user to begin an exercise period at block  2202  of flowchart  2200 . 
     In one example, the sensor  706  may output a data point indicative of a current location and/or a distance traveled by the user for each second of an exercise period. Accordingly, in one example, the outputted data may be received for further processing by, in one example, processor  702 , at block  2204  of flowchart  2200 . 
     In one implementation, a data point associated with each second of a prescribed exercise period may be stored. As such, location data for each second of a prescribed exercise period may be stored in, for example, memory  704 . Upon completion of a given exercise period, a total distance traveled during a prescribed exercise period may be calculated. In one implementation, this total distance traveled may be calculated by, in one example processor  702 , and at block  2206  of flowchart  2200 . 
     In one example, an exercise period utilized to generate data in order to determine a critical speed and/or an anaerobic work capacity of the user may be summarized as a data point comprising two pieces of information. This exercise period summary data point may comprise the total distance traveled, as determined, in one example, at block  2206 , in addition to the total time (i.e. the duration) of the exercise period/session. In one example, the two pieces of information (i.e. the total distance, and the total time) may be expressed as a coordinate point. In one example, this coordinate point P may be of the form P(x 3 , y 3 ), where y 3  may be the total distance (m), and x 3  may be the total time (s). In this way, the exercise period summary data point expressed as a coordinate point may be plotted, as schematically depicted in  FIG.  23   . In one example, the exercise period summary data point may be calculated at block  2208  of flowchart  2200 . 
     In one implementation, in order to calculate one or more of a critical speed and/or an anaerobic work capacity of a user, two or more exercise period summary data points may be utilized. In one example, the durations of the exercise periods used to generate the two or more exercise period summary data points may be different. Accordingly, in one example, one or more processes may be executed to determine whether a threshold number of exercise periods have been completed by the user in order to calculate one or more of a critical speed and/or an anaerobic work capacity for the user. As previously described, this threshold number of exercise periods may be at least two, at least three, at least four, or at least five, among others. In one specific example, one or more processes may be executed by processor  702  to determine whether the threshold number of exercise periods have been completed at decision block  2210  of flowchart  2200 . Accordingly, if the threshold number of exercise periods has been met or exceeded, flowchart  2200  proceeds to block  2212 . If, however, the threshold number of exercise periods has not been met, flowchart  2200  proceeds from decision block  2210  back to block  2202 . 
     In one example, a regression may be calculated using the two or more exercise period summary data points that were calculated from two or more prescribed exercise periods. In one example, this regression may be a linear, or a curvilinear regression. As such, any computational processes known in the art for calculation of a linear or curvilinear regression may be utilized with this disclosure. In one implementation, at least a portion of a calculated regression may be utilized to determine one or more of a critical tissue oxygenation percentage and/or an anaerobic work capacity of the user. In one specific example, one or more processes may be executed to calculate a regression at block  2212  of flowchart  2200 . 
     In one example, at least a portion of a regression calculated using the two or more exercise period summary data points may be utilized to determine a critical speed for a user. Specifically, the critical speed may correspond to a slope of the regression line (or a slope of a linear portion of a curvilinear regression). In one implementation, one or more processes may be executed to output a critical speed calculated as a slope of a regression line through the two or more exercise period summary data points at block  2214  of flowchart  2200 . 
     In another example, at least a portion of a regression calculated using the two or more exercise period summary data points may be utilized to determine an anaerobic capacity for of the user. Specifically, the anaerobic capacity may correspond to an intercept of the regression line (or an intercept of a linear portion of a curvilinear regression). In one example, the anaerobic capacity may be expressed as a total distance (m) above a critical speed (m/s). In one implementation, one or more processes may be executed to output an anaerobic capacity calculated as an intercept of a regression line through the two or more exercise period summary data points, at block  2216  of flowchart  2200 . 
       FIG.  23    is a chart that plots testing data from multiple exercise sessions for a given user. 
     In particular,  FIG.  23    is a chart  2300  plotting distance  2302  against time  2304 . Points  2306 ,  2308 ,  2310 , and  2312  may each represent a separate exercise sessions, and such that at least a portion of each of these exercise sessions is carried out within a severe exercise intensity domain for the user. In one implementation, the exercise period summary data points  2306 ,  2308 ,  2310 , and  2312  may each represent a separate exercise session carried out in a continuous manner. However, in another implementation, one or more of the exercise period summary data points  2306 ,  2308 ,  2310 , and  2312  may represent a separate exercise session carried out in an intermittent manner. 
     In one implementation, a regression line  2314  may be calculated using the four exercise period summary data points  2306 ,  2308 ,  2310 , and  2312 , as plotted on chart  2300 . In one example, this regression line  2314  may be of the form: 
     
       
      
       y=m 
       a 
       x+c 
       d  
      
     
     where y is a total distance (y-axis), x is a time (s) (x-axis), and m d  is the slope of the regression line  2314  and c d  is the intercept of the regression line  2314  on the y-axis. 
     For the example experimental data used to generate the exercise period summary data points  2306 ,  2308 ,  2310 , and  2312 , the regression line  2314  may have the form: y=4.21x+181.96, with an r 2  value of 0.99979. It is noted that this regression line  2314  formula is merely included as one example result. 
     In one example, a regression line, such as regression line  2314 , through two or more exercise period summary data points, such as the exercise period summary data points  2306 ,  2308 ,  2310 , and  2312 , may be used to calculate a critical speed and/or a total distance above a critical speed (D′) (which may be proportional to an anaerobic work capacity) for a user. In one example, given regression line  2314  of the form: y=mx+c, the critical speed may be equal to m, the slope of the regression line  2314 , and the total distance above the critical speed may be equal to c (or |c|, the absolute value of c), the intercept of the regression line  2314  on the y-axis. In particular, given the experimental data depicted in chart  2300 , the critical speed for the user may be 4.21 m/s and the total distance above the critical speed may be 181.96 m. 
     In certain examples, a critical velocity, a critical power, and/or an anaerobic work capacity may be calculated based upon a single input data point. In one implementation, this single input data point may comprise a race time (comprising a distance and a time taken to complete the race distance). In one example, a race time may be utilized based upon an assumption that at least a portion of the race was carried out within a severe exercise intensity domain. However, a single input data point comprising a distance completed and a time taken to complete the distance derived from an exercise session other than a race (i.e. an informal running session untaken by a user) may be utilized with the systems and methods described herein. In another implementation, a single input data point may be utilized to calculate a critical power and/or an anaerobic work capacity, and such that the single input data point may comprise the total amount of work done, and a total time taken. 
     In one implementation, a single input data point may be utilized to calculate one or more of a critical velocity, critical power, and/or an anaerobic work capacity based upon relationships (models) developed through analysis of multiple exercise sessions by multiple different users. In particular,  FIG.  24    depicts a model  2402  that may be utilized to predict a fraction of a critical velocity (y-axis  2404 ) based upon an input of a total athletic session time (x-axis  2406 ) for running. The data points  2408 ,  2410 , and  2412  are exemplary data points from a plurality of data points that may be utilized to develop the model  2402 . Accordingly, data points  2408 ,  2410 , and  2412  may represent separate exercise sessions (for a running exercise session) by a same user, or by different users. In one example, model  2402  may be of the form: y=1.8677*x −0.082 , with an r 2  value of 0.6816. In another example, model  2402  may be of the form: y=1.87*x −0.1 . 
       FIG.  25    depicts a model  2502  that may be utilized to predict a fraction of a critical velocity (y-axis  2504 ) based upon an input of a total athletic session distance (x-axis  2506 ) for a running exercise session. The data points  2508 ,  2510 , and  2512  are exemplary data points from a plurality of data points that may be utilized to develop the model  2502 . Accordingly, data points  2508 ,  2510 , and  2512  may represent separate exercise sessions (for a running exercise session) by a same user, or by different users. In one example, model  2502  may be of the form: y=2.2398* −0.09 , with an r 2  value of 0.6779. In another example, model  2502  may be of the form: y=2.2*x −0.1    
       FIG.  26    depicts a model  2602  that may be utilized to predict a fraction of a critical velocity (y-axis  2604 ) based upon an input of a total athletic session time (x-axis  2606 ) for cycling. The data points  2608 ,  2610 , and  2612  are exemplary data points from a plurality of data points that may be utilized to develop the model  2602 . Accordingly, data points  2608 ,  2610 , and  2612  may represent separate exercise sessions (for a cycling exercise session) by a same user, or by different users. In one example, model  2602  may be of the form: y=1.9199*x −0.088 , with an r 2  value of 0.8053. In another example, model  2602  may be of the form: y=1.9*x −0.1    
       FIG.  27    depicts a model  2702  that may be utilized to predict a fraction of a critical velocity (y-axis  2704 ) based upon an input of a total amount of energy expended during an athletic session (x-axis  2706 ) for cycling. The data points  2708 ,  2710 , and  2712  are exemplary data points from a plurality of data points that may be utilized to develop the model  2702 . Accordingly, data points  2708 ,  2710 , and  2712  may represent separate exercise sessions (for a cycling exercise session) by a same user, or by different users. In one example, model  2702  may be of the form: y=3.0889*x −0.086 , with an r 2  value of 0.6769. In another example, model  2702  may be of the form: y=3.1*x −0.1    
     In one implementation, models  2402 ,  2502 ,  2602 , and/or  2702  may be calculated using any mathematical modeling methodology known in the art (e.g. regression modeling methodologies, among others). 
       FIG.  28    is a flowchart diagram  2800  that may be utilized to calculate one or more of a critical velocity (or critical power) and an anaerobic work capacity based upon a single input data point. Accordingly, the one or more processes associated with flowchart  2800  may be executed by a processor, such as processor  702 . In one example, a single input data point may comprise a total time in combination with a total distance for an exercise session. In one example, at least a portion of the exercise session may be carried out within a severe exercise intensity domain. In another example, a single input data point may comprise a total power expended and a total time associated with an exercise session. Accordingly, one or more processes executed to receive the single input data point may be executed at block  2802 . In one specific example, a data point may indicate that a user completed a 5 km race in 1300 seconds. 
     A mathematical model may be utilized to calculate a critical velocity fraction or a critical power fraction. Accordingly, an input to a model, from, in one example, models  2402 ,  2502 ,  2602 , and/or  2702 , may comprise a total distance traveled during an exercise session, a total time to complete an exercise session, or a total power expended during an exercise session. Further, the selection of a model, from, in one example, models  2402 ,  2502 ,  2602 , and/or  2702 , may be based upon an activity type (e.g. running or cycling, among others). In one implementation, one or more processes may be executed to calculate a critical velocity fraction or a critical power fraction at block  2804  of flowchart  2800 . For the specific example of a 5 km race run completed in 1300 seconds, the critical velocity fraction may be calculated as y=1.8677*(1300) −0.082  (model  2402 ), which implies that the critical velocity fraction (y)=1.045. 
     Additionally, an average velocity may be calculated as a total exercise session distance divided by a total time taken to complete the distance. In another implementation, an average exercise session power may be calculated as a total exercise session power divided by a total time taken to complete the exercise. Accordingly, one or more processes may be executed to calculate an average exercise session velocity, or an average exercise session power, at block  2806  of flowchart  2800 . For the specific example of a 5 km race run in 1300 seconds, the average exercise session velocity may be 5000/1300=3.85 m/s. 
     A critical velocity may be calculated as an average velocity divided by the critical velocity fraction. Alternatively a critical power for a user may be calculated as an average power divided by the critical power fraction. Accordingly, one or more processes may be executed to calculate a critical velocity, or a critical power, at block  2808  of flowchart  2800 . For the specific example of a 5 km race run in 1300 seconds, the critical velocity may be calculated as 3.85/1.045=3.68 m/s. 
     A total distance traveled below a critical velocity may be calculated as an average velocity multiplied by a total time associated with an exercise session. Alternatively, the total amount of energy expended below a critical power may be calculated as an average power multiplied by a total time associated with an exercise session. Accordingly, one or more processes may be executed to calculate a total distance traveled below a critical velocity, or a total amount of energy expended below a critical power, at block  2810  of flowchart  2800 . For the specific example of a 5 km race run in 1300 seconds, the distance traveled below the critical velocity may be calculated as 3.68*1300=4784 m. 
     An anaerobic work capacity may be calculated as a distance above a critical velocity, or as a total amount of energy above a critical power. Accordingly, an anaerobic work capacity may be calculated (e.g. for running) as a difference between a total distance traveled during an exercise session and a total distance traveled below a critical velocity, as calculated at block  2810 . Alternatively, an anaerobic work capacity may be calculated (e.g. for cycling) as a difference between a total amount of energy expended during an exercise session and a total amount of energy expended below a critical power, as calculated at block  2810 . Accordingly, one or more processes may be executed to calculate an anaerobic work capacity at block  2812  of flowchart  2800 . For the specific example of a 5 km race run in 1300 seconds, the distance traveled above the critical velocity (i.e. the anaerobic work capacity, D′) may be equal to 5000−4784=216 m. 
     In certain implementations, a volume of oxygen consumption associated with an exercise session participated in by a user may be estimated without using any sensors. In particular, a volume of oxygen consumption of the user may be estimated based upon an athletic profile constructed using one or more questions answered by the user. This questionnaire may be administered to the user in an electronic format, and may comprise one or more questions. In one example, answers to these questions may be based on a scale. In one example, the scale may comprise numbers from 0 to 10. However, additional or alternative scales may be utilized, without departing from the scope of these disclosures. Specifically, the questions may include, among others: an estimation of bone size, an estimation of leanness of the user, an estimation of muscle size, an estimation of sleep quality, an estimation of relaxation habits, an estimation of nutrition quality, an estimation of smoking status, an estimation of drinking habits, and an estimation of an activeness of the user. Additional or alternative questionnaire questions utilized to construct an athletic profile for the user may include, a user&#39;s age, gender, height, waist circumference, weight, as well as an indication as to whether the user is pregnant. Still further questionnaire questions may include an estimation of a 5 km running race pace (or a pace associated with another distance), and an estimation of a number of days active during the week. 
       FIG.  29    depicts a flowchart diagram  2900  that may be utilized to estimate a volume of oxygen consumption of the user in response to a received rate of perceived exertion of the user, and using an athletic profile constructed using one or more questionnaire questions. In particular, a user may be asked to respond to one or more questionnaire questions, which may include one or more of those questions described above. 
     Accordingly, one or more processes may be executed by a processor, such as processor  702  to receive one or more questionnaire responses at block  2902  of flowchart  2900 . 
     An athletic profile may be calculated and stored, such as within memory  704 , based upon one or more of the received questionnaire responses. Accordingly, this athletic profile may account for one or more physical and/or behavioral attributes associated with user, which may impact a volume of oxygen consumption for the user. In one example, the athletic profile may estimate a maximal volume of oxygen consumption associated with user, based upon one or more physical and/or behavioral attributes of the user. Accordingly, one or more processes may be executed to calculate and store the athletic profile at block  2904 . 
     In one example, a user may input a rate of perceived exertion following an exercise session. This rate of perceived exertion may be received by a processor, such as processor  702  via an interface, such as interface  708 . In one example, the rate of perceived exertion may be received as a number on a scale of 0 to 10. However, those of ordinary skill in the art will recognize that additional or alternative scales may be utilized with his rate of perceived exertion, without departing from the scope of these disclosures. In one example, the rate of perceived exertion may be received from the user at block  2906 . 
     The received rate of perceived exertion may be mapped to an oxygen consumption scale for the user, based upon the constructed athletic profile for the user. In one example, the scale of the rate of perceived exertion may be linearly mapped to a volume of oxygen consumption scale delimited by a maximal oxygen consumption estimated for the user based upon the calculated athletic profile for the user. In other implementations, nonlinear mappings of the rate of perceived exertion scale to the volume of oxygen consumption scale may be utilized, without departing from the scope of these disclosures. Accordingly, one or more processes to map the received rate of perceived exertion to the oxygen consumption scale may be executed at block  2908 . Additionally, one or more processes may be executed to output an estimated volume of oxygen consumption, based upon the inputted rate of perceived exertion of the user, at block  2910 . 
     In one example, an anaerobic work capacity of the user may be replenished when the user exercises at an intensity that is below a critical intensity (i.e. within a moderate or heavy exercise intensity domain). As previously described, an anaerobic work capacity may be expressed as a total number of muscle oxygenation points, as derived from muscle oxygenation sensor data, such as data outputted by oxygenation sensor  710 . As such, the anaerobic work capacity may be denoted M′. In one example, replenishment of anaerobic work capacity may be denoted M′_rate and calculated as a difference between a current muscle oxygenation percentage and a critical muscle oxygenation percentage: M′_rate=% MO2−critical % MO2. In one example, the M′_rate may be continuously summed throughout a duration of an exercise period/trial in order to determine an M′ balance (i.e. a total number of muscle oxygenation points). Accordingly, when a current muscle oxygenation percentage is below the critical muscle oxygenation percentage, the calculated M′_rate may be negative, and indicative of a finite work capacity (anaerobic work capacity) being consumed. Further, when a current muscle oxygenation percentage is above the critical muscle oxygenation percentage for a user, the M′_rate may be positive, and the finite work capacity may be replenished.  FIG.  30    schematically depicts this consumption and replenishment of total muscle oxygenation points. In particular,  FIG.  30    graphs M′ replenishment on the y-axis  3002  for four different exercise intensity domains (i.e. severe  3004 , heavy  3006 , moderate  3008  and rest  3010 ). As such,  FIG.  30    schematically demonstrates that an M′_rate associated with a severe exercise intensity domain may be negative, but the anaerobic work capacity of the user may be replenished as the user transitions to exercise within a heavy exercise intensity domain  3006 , in a moderate exercise intensity domain  3008 , and when the user is at rest  3010 . 
     Various systems and methods are described in this disclosure for calculation of a critical intensity (critical velocity, or a critical power) for a user. Additionally, various systems and methods are described in this disclosure for calculation of an anaerobic work capacity/finite work capacity (M′, D′) associated with user. As such, given these calculated critical intensity and finite work capacity values, various activity metrics may be predicted. As such, those of ordinary skill in the art will recognize various methodologies that may be utilized predict athletic metrics using one or more of a critical intensity and a finite work capacity for a user, without departing from the scope of these disclosures. In one example a prediction of a time, t(s), to completion of an athletic event (e.g. a race), given a present velocity, v p (m/s), a critical velocity, v crit (m/s), and a finite work capacity, D′(m), may be given by: t=D′/(v p −v crit ). 
     For the avoidance of doubt, the present application extends to the subject-matter described in the following numbered paragraphs (referred to as “Para” or “Paras”):