Abstract:
A physical activity monitoring method and system in one embodiment includes a communications network, a wearable sensor device configured to generate physiologic data associated with a sensed physiologic condition of a wearer, and to generate context data associated with a sensed context of the wearer, and to transmit the physiologic data and the context data over the communications network, a memory for storing the physiologic data and the context data, a computer and a computer program executed by the computer, wherein the computer program comprises computer instructions for rendering first data associated with the physiologic data and second data associated with the context data, and a user interface operably connected to the computer for rendering the first data and the second data.

Description:
FIELD 
     This invention relates to wearable monitoring devices. 
     BACKGROUND 
     Physical fitness has been a growing concern for both the government as well as the health care industry due to the decline in the time spent on physical activities by both young teens as well as older adults. Self monitoring of individuals has proven to be helpful in increasing awareness of individuals to their activity habits. By way of example, self-monitoring of sugar levels by a diabetic helps the diabetic to modify eating habits leading to a healthier lifestyle. 
     Self-monitoring and precisely quantizing physical activity has also proven to be important in disease management of patients with chronic diseases, many of which have become highly prevalent in the western world. A plethora of different devices and applications have surfaced to serve the needs of the community ranging from simple pedometers to complex web-based tracking programs. 
     Wearable devices and sensors have seen a tremendous global growth in a range of applications including monitoring physical activity. Several physical activity monitoring systems incorporate a variety of sensors which store the sensor data on a wearable device and process the data offline in a separate device. Typically, the known systems require proactive or reactive specification of the physical actions performed by the user. Additionally, while known systems are able, to some extent, to ascertain the general nature of activity that an individual is undertaking, the systems are not able to provide detailed information as to the context in which the activity is being undertaken. 
     Micro-electromechanical system (MEMS) sensors, which have a small form factor and exhibit low power consumption without compromising on performance, have received increased attention for incorporation into wearable sensors. For example, inertial MEMS sensors such as accelerometers can be placed into an easy and light portable device to be worn by users. 
     Accordingly, there is a need for smarter applications and wearable devices that track, record and report physical activities of the wearer. It would be beneficial if such a device did not require user intervention during the course of the activity. A further need exists for such a system that can deduce the nature of the physical activity. A system which performed physical activity monitoring while providing information regarding the context of the activity would be beneficial. 
     SUMMARY 
     A physical activity monitoring method and system in one embodiment includes a communications network, a wearable sensor device configured to generate physiologic data associated with a sensed physiologic condition of a wearer, and to generate context data associated with a sensed context of the wearer, and to transmit the physiologic data and the context data over the communications network, a memory for storing the physiologic data and the context data, a computer and a computer program executed by the computer, wherein the computer program comprises computer instructions for rendering first data associated with the physiologic data and second data associated with the context data, and a user interface operably connected to the computer for rendering the first data and the second data. 
     In accordance with another embodiment, a method of displaying data associated with physical activities comprising storing a multilayer perceptron model, transmitting first physiologic data associated with a first sensed physiologic condition of a wearer, calibrating the multilayer perceptron model with the first transmitted physiologic data, transmitting second physiologic data associated with a second sensed physiologic condition of the wearer during an activity, using the stored multilayer perceptron model to determine at least one characteristic of the wearer during the activity, determining the nature of the activity based upon the determined at least one characteristic, and displaying first data associated with the second physiologic data and second data associated with the determined nature of the activity. 
     In yet another embodiment, a method of monitoring physical activity includes attaching a sensor to a wearer, activating the sensor, generating physiologic data associated with a sensed physiologic condition of the wearer during a wearer activity, generating context data associated with a sensed context of the wearer during the wearer activity, analyzing the physiologic data with a multilayer perceptron, identifying the wearer activity based upon the analyses, and displaying the identity of the activity and the context data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  depicts a block diagram of a physical activity monitoring network including wearable sensor devices in accordance with principles of the present invention; 
         FIG. 2  depicts a schematic of a wearable sensor of  FIG. 1  including at least one communication circuit and at least one sensor suite; 
         FIG. 3  depicts the wearable sensors of  FIG. 1  connected into a piconet; 
         FIG. 4  depicts a process that may be controlled by the processor of  FIG. 1  for obtaining physical activity monitoring data from the wearable sensors of  FIG. 1 ; 
         FIG. 5  depicts a process of analyzing data from a wearable sensor of  FIG. 1  to generate an inference as to the activity of a subject wearing a wearable sensor using a multilayer perceptron; 
         FIG. 6  depicts a screen that may be transmitted over a communications link such as the Internet and used to display obtained physical activity monitoring data from the wearable sensors of  FIG. 1 ; 
         FIG. 7  depicts the contents of an exemplary activity information folder rendered within the screen of  FIG. 6 ; 
         FIG. 8  depicts the contents of an exemplary record activity folder rendered within the screen of  FIG. 6 ; 
         FIG. 9  depicts the contents of an exemplary goals folder rendered within the screen of  FIG. 6 ; 
         FIG. 10  depicts the contents of an exemplary activity review folder rendered within the screen of  FIG. 6 ; and 
         FIG. 11  depicts an alternative screen that may be accessed by a user to review activity of a subject over a twenty-four hour period including a graphic display of energy used, a summary of activity within a focus window, identification of activities within the focus window, the location at which the activities in the focus window were performed, and others accompanying the subject during performance of the activity. 
     
    
    
     DESCRIPTION 
     For the purposes of promoting an understanding of the principles of the invention, reference will now be made to the embodiments illustrated in the drawings and described in the following written specification. It is understood that no limitation to the scope of the invention is thereby intended. It is further understood that the present invention includes any alterations and modifications to the illustrated embodiments and includes further applications of the principles of the invention as would normally occur to one skilled in the art to which this invention pertains. 
     Referring to  FIG. 1 , there is depicted a representation of a physical activity monitoring network generally designated  100 . The network  100  includes a plurality of wearable sensors  102   x , input/output (I/O) devices  104   x , a processing circuit  106  and a memory  108 . The I/O devices  104   x  may include a user interface, graphical user interface, keyboards, pointing devices, remote and/or local communication links, displays, and other devices that allow externally generated information to be provided to the processing circuit  106 , and that allow internal information of the processing circuit  106  to be communicated externally. 
     The processing circuit  106  may suitably be a general purpose computer processing circuit such as a microprocessor and its associated circuitry. The processing circuit  106  is operable to carry out the operations attributed to it herein. 
     Within the memory  108  is a multilayer perceptron (MLP)  110  and program instructions  112 . The program instructions  112 , which are described more fully below, are executable by the processing circuit  106  and/or any other components as appropriate. 
     The memory  108  also includes databases  114 . The databases  114  include a context database  116 , a past activities database  118 , a goals database  120 , and a fitness parameters database  122 . In one embodiment, the databases are populated using object oriented modeling. The use of object oriented modeling allows for a rich description of the relationship between various objects. 
     A communications network  124  provides communications between the processing circuit  106  and the wearable sensors  102   x  while a communications network  126  provides communications between the processing circuit  106  and the I/O devices  104   x . In alternative embodiments, some or all of the communications network  124  and the communications network  126  may include shared components. 
     In the embodiment described herein, the communications network  124  is a wireless communication scheme implemented as a wireless area network. A wireless communication scheme identifies the specific protocols and RF frequency plan employed in wireless communications between sets of wireless devices. To this end, the processing circuit  106  employs a packet-hopping wireless protocol to effect communication by and among the processing circuit  106  and the wearable sensors  102   x . 
     Each of the wearable sensors  102   x  in this embodiment are identical and are described in more detail with reference to the wearable sensor  102   1  shown in  FIG. 2 . The sensor  102   1  includes a network interface  130   1 , a processor  132   1 , a non-volatile memory  134   1 , a micro-electrical mechanical system (MEMS) local RF communication interface  136   1 , a signal processing circuit  138   1 , and sensor suites  140   1-x . 
     The network interface  130   1  is a communication circuit that effectuates communication with one or more components of the communications network  124 . To allow for wireless communication with the other components of the communications network  124 , the network interface  130   1  is preferably a radio frequency (RF) modem configured to communicate using a wireless area network communication scheme such as Bluetooth RF modem, or some other type of short range (about 30-100 feet) RF communication modem. Thus, each of the sensors  102   x  may communicate with components such as other communication subsystems and the processing circuit  106 . 
     The network interface  130   1  is further operable to, either alone or in conjunction with the processor  132   1 , interpret messages in wireless communications received from external devices and determine whether the messages should be retransmitted to another external device as discussed below, or processed by the processor  132   1 . Preferably, the network interface  130   1  employs a packet-hopping protocol to reduce the overall transmission power required. In packet-hopping, each message may be transmitted through multiple intermediate communication subsystem interfaces before it reaches its destination as is known in the relevant art. 
     The processor  132   1  is a processing circuit operable to control the general operation of the sensor  102   1 . In addition, the processor  132   1  may implement control functions and information gathering functions used to maintain the databases  114 . 
     The programmable non-volatile memory  134   1 , which may be embodied as a flash programmable EEPROM, stores configuration information for the sensor suites  140   1-x . The programmable non-volatile memory  134   1  includes an “address” or “ID” of the wearable sensor  102   1  that is appended to any communications generated by the wearable sensor  102   1 . The memory  134   1  further includes set-up configuration information related to the system communication parameters employed by the processor  132   1  to transmit information to other devices. 
     The MEMS local RF communication circuit  136   1  may suitably include a Bluetooth RF modem, or some other type of short range (about 30-100 feet) RF communication modem. The use of a MEMS-based RF communication circuit allows for reduced power consumption, thereby enabling the wearable sensor  102   1  to be battery operated, if desired. The life of the wearable sensor  102   1  may be extended using power management approaches. Additionally, the battery may be augmented or even replaced by incorporating structure within the MEMS module to use or convert energy in the form of vibrations or ambient light. In some embodiments, a single circuit functions as both a network interface and a local RF communication circuit. 
     The local RF communication circuit  136   1  may be self-configuring and self-commissioning. Accordingly, when the wearable sensors  102   x  are placed within communication range of each other, they will form a piconet as is known in the relevant art. In the case that a wearable sensor  102   x  is placed within range of an existent piconet, the wearable sensor  102   x  will join the existent piconet. 
     Accordingly, the wearable sensors  102   x  are formed into one or more communication subsystems  142  as shown in  FIG. 3 . The wearable sensors  102   x  within the communication subsystem  142  include a hub wearable sensor  102   1 , and slave wearable sensor  102   2 ,  102   3 , and  102   4 . Additionally, a slave transmitter  102   5  is within the communication subsystem  142  as a slave to the slave transmitter  102   4 . The hub sensor  102   1  establishes a direct connection with the processing circuit  106  over the network  124 . The slave wearable sensor  102   2 ,  102   3 ,  102   4 , and  102   5  communicate with the processing circuit  106  through the hub sensor  102   1 . It will be appreciated that a particular communication subsystem  142  may contain more or fewer wearable sensors  102   x  than the wearable sensors  102   x  shown in  FIG. 3 . 
     Thus, each of the communication circuits  136   x  in the wearable sensors  102   1 ,  102   2 ,  102   3 , and  102   4  is used to link with the communication circuits  136   x  in the other wearable sensors  102   x  to establish piconet links  144   1-3  (see  FIG. 3 ). The communication circuits  136   x  of the slave wearable sensors  102   4  and  102   5  also establish a piconet link  144   4 . 
     Returning to  FIG. 2 , the signal processing circuit  138   1  includes circuitry that interfaces with the sensor suites  140   1-x , converts analog sensor signals to digital signals, and provides the digital signals to the processor  132   1 . In general, the processor  132   1  receives digital sensor information from the signal processing circuit  138   1 , and from other sensors  102   x , and provides the information to the communication circuit  124 . 
     The sensor suites  140   1-x  include a sensor suite  140   1-1  which in this embodiment is a 3-axis gyroscope sensor suite which provides information as to the orientation of the wearable sensor  102   1 . Other sensors which may be incorporated into the sensor suites  140   1-x  include a calorimeter, a pulse sensor, a blood oxygen content sensor, a GPS sensor, and a temperature sensor. One or more of the sensor suites  140   1-x  may include MEMS technology. 
     Referring to  FIG. 4 , there is depicted a flowchart, generally designated  150 , setting forth an exemplary manner of operation of the network  100 . Initially, the MLP  110  may be stored within the memory  108  (block  152 ). The MLP  110  in one embodiment includes 30 hidden layer neurons and 1 output neuron. The activation function for the hidden layer and output layer neurons are hyperbolic tangent sigmoid and log sigmoid, respectively. Next, a wearable sensor  102   x  is placed on a subject such as an individual (block  154 ). The wearable sensor  102   x  is then activated (block  156 ). Upon activation of the sensor  102   x , the processor  132  initiates&#39; data capture subroutines. Additionally, the wearable sensor  102   x  establishes the communications link  124  with the processing circuit  106  (block  158 ). Alternatively, the wearable sensor  102   x  may join a piconet or other communication system in communication with the processing circuit  106 . 
     Initial output from the sensor suites  140   x  is passed through the signal processing circuit  138   x  to the processor  132   x . The initial sensor data is then transmitted to the processing circuit  106  over the link  124  (block  160 ). The initial sensor data is used by the processing circuit  106  to calibrate the MLP  110  (block  162 ). Calibration of the MLP  110  provides the MLP  110  with an initial state for the subject wearing the sensor  102   x . For example, the output of the sensor suite  140   1-1  is used to establish y-axis and z-axis values for the wearer of the sensor  102   x , in a known position such as standing or prostate. 
     The goals database  120  (block  164 ) is then populated. The data used to populate the goals database  120  may be input from one or more of the I/O devices  104   x . Alternatively, the sensor  102   x  may be configured with a user interface, allowing the wearer of the sensor  102   x  to input goals data. 
     The wearer then proceeds to perform various physical activities (block  166 ). As the activities are performed, data is obtained from the sensor suites  140   x  (block  168 ). The sensor data is passed through the signal processing circuit  138   x  to the processor  132   x . The sensor data is then transmitted to the processing circuit  106  over the link  124  (block  170 ). The sensor data is processed by the processing circuit  106  (block  172 ) and stored in the databases  114  (block  174 ). By way of example, heart rate, respiration rate, temperature, blood oxygen content, and other physical parameters may be stored in the fitness parameters database  122 . 
     The foregoing actions may be performed in different orders. By way of example, goals may be stored prior to attaching a sensor  102   x  to a subject. Additionally, the various actions may be performed by different components of the network  100 . By way of example, in one embodiment, all or portions of the memory  108  may be provided in the sensor  102   x . In such an embodiment, the output of the MLP  110  may be transmitted to a remote location such as a server remote from the sensor for storage. 
     The MLP  110  in one embodiment is configured to identify the activity in which the wearer of the sensor  102   x  is engaged. Accordingly, the MLP  110  is configured to perform the procedure  200  of  FIG. 5 . The processing circuit  106  receives a frame of data from the sensor suite  140   1-1  (block  202 ). One frame of data in one embodiment is based upon a ten second sample. Based upon the initial calibration data (block  162  of  FIG. 4 ) and the most recently received frame data, the change in the orientation of the wearer in the y-axis is determined (block  204 ). Similarly, based upon the initial calibration data (block  162  of  FIG. 4 ) and the most recently received frame data, the change in the orientation of the wearer in the z-axis is determined (block  206 ). 
     The frame data from the sensor suite  140   1-1  is also used to obtain a three dimensional vector indicative of the acceleration of the wearer (block  208 ) and to determine the three dimensional velocity of the wearer (block  210 ). Once the acceleration in the z-axis is obtained, the MLP  110  determines whether or not the acceleration in the z-axis is periodic (block  212 ). Periodicity is determined by analyzing several frames of frame data using an autocorrelation sequence formed from the z-axis acceleration. 
     The spectral flatness measure of the acceleration in all three axes is then determined (block  214 ). The spectral flatness measure is defined as the ratio of geometric mean to arithmetic mean of the power spectral density coefficients. 
     The data from the sensor suite  140   1-1  is further used to determine the relative inclination of the wearer (block  216 ) and data indicative of the energy use of the wearer is also obtained from the frame data and the energy expenditure is determined (block  218 ). Energy usage may be determined, for example, from data obtained by a sensor suite  140   1-x  configured as a thermometer or calorimeter. 
     Thus, the MLP  110  is configured to receive eight static features from a current input frame and eight delta features that capture the difference between the features in the current frame and those in a previous frame. Based upon the foregoing determinations, the MLP  110  infers an activity of the wearer for the time frame associated with the frame data. By way of example, relative inclination, periodicity and spectral flatness help distinguish between sitting, standing and lying-down. Additionally, energy expenditure, velocity, spectral flatness, and periodicity help distinguish between dynamic activities (e.g., walking) and static activities (e.g., standing). The activity determined by the MLP  110  is then stored, with a date/time stamp, in the past activities database  118  (block  222 ). 
     While the MLP  110  is controlled to make a determination as to the nature of the activity of the wearer, date/time stamped data is also being provided to the context database  116 . For example, in embodiments incorporating a GPS sensor in a sensor suite  140   1-x , GPS data may be obtained at a given periodicity, such as once every thirty seconds, transmitted to the processing circuit  106  and stored in the context database  116 . Additionally, data identifying the other transmitters in the piconet  142  is stored in the context database. Of course, transmitters within the piconet  142  need not be associated with a wearable sensor  102   x . For example, a cellular telephone or PDA without any sensors may still emit a signal that can be detected by the sensor  102   x . 
     The data within the memory  108  may be used in various applications either in real time, for example, by transmitting data over the communications link  124  to the sensor  102   x , or at another time selected by the wearer or other authorized individual by access through an I/O device  104   x . The applications include activity monitoring, activity recording, activity goal setting, and activity reviewing. 
     A screen which may be used to provide activity monitoring data from the memory  108 , such as when the data is accessed by an I/O device  104   x  connected to the memory  108  by an internet connection, is depicted in  FIG. 6 . The screen  230  includes a navigation portion  232  and a data portion  234 . A number of folders  236  are rendered within the data portion  234 . The folders  236  include a summary folder  238 , an activity monitoring folder  240 , an activity recording folder  242 , an activity goal setting folder  244 , and an activity reviewing folder  246 . The summary folder  238  includes a chart  248 . Data that may be rendered on the chart  248  include identification of the individual or subject associated with the sensor  102   x , summary fitness data, and other desired data. 
     By selecting the activity monitoring folder  240 , the folder  240  is moved to the forefront of the screen  230 . When in the forefront, a viewer observes the folder  240  as depicted in  FIG. 7 . The activity monitoring folder  240  displays data related to the current activity of the subject. In this embodiment, the activity monitoring folder  240  displays data fields  252 ,  254 , and  256  which are used to display the type of activity, the duration that the activity has been engaged in, and the calories used during the activity, respectively. The data fields presented for different activities may be modified. For example, if the subject is sleeping, the data fields may indicate respiration rate, heart beat rate, and blood oxygen content. 
     The activity monitoring folder  240  further identifies other subjects or individuals in proximity to the monitored subject in a context window  258 . The context window  258  may identify specific individuals if known. A map  260  is also shown. Data for rendering the map  260  may be obtained, for example, from a GPS sensor in the sensor suite  140   x  or from data obtained from a relay station. For embodiments including a GPS sensor in the sensor suite  140   x , or other sensor for obtaining detailed location data, the route  262  of the subject over the course of the monitored activity may also be displayed on the map  260 . 
     By selecting the activity recording folder  242  from the screen  230  of  FIG. 6 , the folder  242  is moved to the forefront of the screen  230 . When in the forefront, a viewer observes the folder  242  as depicted in  FIG. 8 . In this embodiment, the activity recording folder  242  displays editable data fields  264 ,  266 , and  268 . The editable data fields  264 ,  266 , and  268  allow a user to add or modify information related to a recorded activity. For example, unidentified workout partners may be identified to the network  100  by editing the field  268 . This data may be used to modify the context database  116  so that the network  100  recognizes the workout partner in the future. For example, an individual&#39;s identity may be associated with a particular cell phone beacon that was detected with the wearable sensor  102   x . The activity recording folder  242  may include additional editable fields. 
     By selecting the activity goal setting folder  244  from the screen  230  of  FIG. 6 , the folder  244  is moved to the forefront of the screen  230 . When in the forefront, a viewer observes the folder  244  as depicted in  FIG. 9 . In this embodiment, the activity goal setting folder  244  displays editable data fields  270 ,  272 , and  274 . The editable data fields  270 ,  272 , and  274  allow a user to record goals for future activity. For example, a goal of running may be identified in the field  270  and a duration of 90 minutes may be stored in the field  272 . Additionally, a distance goal of, for example, 14 miles may be edited into field  274 . The activity goal setting folder  244  may include additional editable fields such as average speed, etc. 
     By selecting the activity reviewing folder  246  from the screen  230  of  FIG. 6 , the folder  246  is moved to the forefront of the screen  230 . When in the forefront, a viewer observes the folder  246  as depicted in  FIG. 10 . In this embodiment, the activity reviewing folder  246  displays activity data fields  276 ,  278 , and  280 . The activity data fields  276 ,  278 , and  280  allow a user to review activities which were conducted over a user defined time frame. Additional information may also be displayed. For example, context data fields  282  and  284  identify other individuals that were present during the activity associated with the data in the activity data fields  276  and  278 , respectively. 
     A variety of different screens may be used to display data obtained from the memory  108 . Additionally, the data selected for a particular screen, along with the manner in which the data is displayed, may be customized for different applications. For example, the screen  300  depicted in  FIG. 11  may be used to provide an easily navigable interface for reviewing activities over a twenty-four hour window. 
     The screen  300  includes a navigation portion  302  and a data portion  304 . The data portion  304  includes an identification field  306  for identifying the subject and a data field  308  which displays the date associated with the data in the data portion  304 . 
     A daily activity chart  310  within the data portion  304  shows the amount of calories expended by the subject. To this end, bar graphs  312  indicate caloric expenditure over the twenty-four hour period depicted in the chart  310 . The data for the bar graphs  312  may be obtained, for example, from the past activities database  118 . 
     A focus window  314  is controlled by a user to enclose a user variable window of activity. In response, the underlying application accesses the databases  114  and displays data associated with the focus window  314  in an information field  316 , an activities field  318 , a location field  320 , and a people field  322 . 
     The information field  316  displays general data about the focus window  314 . Such data may include the time span selected by the user, the amount of calories expended during the selected time span, the number of steps taken by the subject during the selected time span, maximum speed of the subject during the selected time span, average speed of the subject during the selected time span, etc. 
     The activities field  318  displays each identifiable activity within the focus window  314 . The activity may be specifically identified or generally identified. For example, the network  100  may initially only be configured to distinguish activities based upon, for example, changes in velocity, changes in respiration, changes in heart rate, etc. Thus, the activity identification may be “activity 1,” “walking,” or “running”. 
     The activities field  318  includes, however, an editable field  324 . The field  324  may be used to edit the identified activity with additional descriptive language. Thus, the general identification may be further specified as “loading boxes on a truck”, “cutting grass”, “raking leaves”, etc. Moreover, the network  100  may be configured to “learn” so as to infer a more specific identification of future activities. 
     The location field  320  displays context data in the form of each identifiable location at which the activities within the focus window  314  were conducted. The location may be specifically identified or generally identified. For example, the network  100  may initially only be configured to distinguish location based upon a determined change in location. The location field  320  includes, however, an editable field  326 . The field  326  may be used to edit the identified location with additional descriptive language. Thus, the general identification of a “location 1” may be further specified as “gym”, “office” or “jogging route 1”. 
     The people field  322  displays context data in the form of each identifiable individual or subject present during the activities within the focus window  314 . The people may be specifically identified or generally identified. For example, the MLP  110  may initially only be configured to distinguish different individuals based upon a different cell phone beacons. The people field  322  includes, however, an editable field  328 . The field  328  may be used to edit the identified individual with additional descriptive language. Thus, the general identification of an “individual 1” may be further specified as “Joe”, “Anastasia” or “co-worker”. 
     Various functionalities may be incorporated into the screen  300  in addition to the functions set forth above so as to provide increased insight into the habits of a subject. By way of example, in response to selecting an activity within the activity field  318 , the context data for the selected activity may be highlighted. Thus, by highlighting the area  330  in the activities field  318 , a location  332  and individuals  334  and  336  are highlighted. 
     The network  100  thus provides insight as to a subject&#39;s activities such as standing, sitting, walking, fast walking and running. These activities may be inferred based upon features extracted from historical data. Additionally, by incorporation of a pre-learned classifier, such as a neural net-based classifier, the system can automatically learn new activity classifications. 
     The presentation of data from the databases  114  in the manner described above with reference to  FIGS. 6-11  provides improved accuracy in capturing action specific metrics such as energy expenditure for walking as opposed to that for fast walking or running. By selectively displaying data stored within the databases  114 , subject matter experts (SME) can use the captured historical data to identify factors implicated by past failures for the subject. This allows the SME to design innovative and effective ways of structuring future activities so as to increase the potential for achieving goals. 
     Additionally, while the data may be used retrospectively, the data may also be presented to a subject in real-time. Accordingly, an athlete may easily change his workout routine from walking to running and fast walking so as to maintain a desired rate of energy expenditure. Feedback during activities may be facilitated by provision of the sensor  102   x  as a wearable device. To this end the wearable sensor  102   x  may be embodied as a small device (e.g., a smart phone with inertial sensor) that can be easily worn on the human body (e.g., on the hip or on the arm) or worn by other subject without affecting actions of daily living or recreational activities. Of course, the functionality of the network  100  can be expanded by provision of additional sensors located at multiple locations of the subject body. 
     The network  100  may further be used to set goals and to monitor activities against the established goals. The data may be used to provide motivational feedback to the subject. 
     While the invention has been illustrated and described in detail in the drawings and foregoing description, the same should be considered as illustrative and not restrictive in character. It is understood that only the preferred embodiments have been presented and that all changes, modifications and further applications that come within the spirit of the invention are desired to be protected.