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An exercise monitoring method and system in one embodiment includes a communications network, a wearable transducer configured to generate physiologic data associated with movement of a wearer, and to form a communication link with the communications network, a system memory in which command instruc

An exercise monitoring method and system in one embodiment includes a communications network, a wearable transducer configured to generate physiologic data associated with movement of a wearer, and to form a communication link with the communications network, a system memory in which command instructions are stored, a user interface operably connected to the computer, and a system processor configured to execute the command instructions to receive the generated physiologic data, analyze the received physiologic data with a multilayer perceptron/support vector machine/hidden Markov (MSH) model, model the analyzed physiologic data, and generate feedback based on a comparison of the model and a stored exercise object.

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1. An exercise monitoring system comprising: a communications network;a wearable transducer configured to generate physiologic data associated with movement of a wearer, and to form a communication link with the communications network;a system memory in which command instructions are stored;a user i

1. An exercise monitoring system comprising: a communications network;a wearable transducer configured to generate physiologic data associated with movement of a wearer, and to form a communication link with the communications network;a system memory in which command instructions are stored;a user interface operably connected to the computer; anda system processor configured to execute the command instructions to receive the generated physiologic data,identify a type of movement indicated by the generated physiological data,analyze the received physiologic data with a multilayer perceptron, support vector machine, or hidden Markov (MSH) model based on the idenfied type of movement,model the analyzed physiologic data, andgenerate feedback based on a comparison of the model and a stored exercise object. 2. The system of claim 1, wherein the wearable transducer is activated in response to the wearable transducer sensing movement of the wearer. 3. The system of claim 1, the wearable transducer includes: an actuator interface configured to provide the generated feedback to the wearer;at least one sensor configured to sense physiologic data associated with movement of the wearer;a signal processing circuit configured to pre-process data from the at least one sensor and to post-process data for the actuator;a transducer processor configured to process the pre-processed sensor data and to provide processed feedback data to the signal processing circuit; anda network interface configured to provide communication with the communications network. 4. The system of claim 3, wherein the transducer processor is further configured to transmit the processed sensor data via the network interface to the system processor and to receive the feedback data from the system processor via the network interface. 5. The system of claim 4, wherein the actuator interface provides the generated feedback data by at least one of a tactile-vibrational scheme, an audible scheme, and a thermal feedback scheme. 6. The system of claim 3, the wearable transducer further includes: a transducer memory in which configuration information of the at least one sensor is stored; anda radio frequency communication circuit configured to link the wearable transducer to a plurality of other wearable transducers over an industrial, scientific, and medical frequency band. 7. The system of claim 6, wherein the radio frequency communication circuit is configured to use a BLUETOOTH protocol. 8. The system of claim 1, wherein the MSH model is configured to: determine a change in a x-axis orientation of the wearable transducer;determine a change in a y-axis orientation of the wearable transducer;determine a change in a z-axis orientation of the wearable transducer; anddetermine a change in a three dimensional velocity of the wearable transducer. 9. The system of claim 8, wherein the MSH model is further configured to: determine parameters of human motion kinematics based on the physiologic data generated by the wearable transducer; anddetermine parameters of human motion dynamics based on the physiologic data generated by the wearable transducer. 10. The system of claim 9, wherein the generated feedback data is based on a difference between the modeled analyzed physiologic data and an optimal performance data associated with an exercise routine. 11. The system of claim 10, wherein the difference includes a quantitative comparison and a qualitative comparison. 12. A method of monitoring physiologic data associated with an exercise routine performed by a user, comprising: generating physiologic data using at least one wearable transducer worn by the user;receiving the generated physiologic data at a system processor;identifying a type of movement indicated by the received physiological data using the system processor;analyzing the received physiologic data using the system processor based on the identified type of movement;modeling the analyzed physiologic data using the system processor; andgenerating feedback based on a comparison of the model and a stored exercise object using the system processor. 13. The method of claim 12, wherein analyzing the received physiologic data comprises: analyzing the received physiologic data with a multilayer perceptron, support vector machine, or hidden Markov (MSH) model. 14. The method of claim 13, wherein analyzing the received physiologic data with the MSH model comprises: determining a change in a x-axis orientation of the plurality of parts of a user;determining a change in a y-axis orientation of the plurality of parts of the user;determining a change in a z-axis orientation of the plurality of parts of the user;determining a change in a three dimensional velocity of the plurality of parts of the user;determining a range of motion based on the physiologic data;determining the strength of a muscle based on the physiologic data; andrecommending a corrective action. 15. The method of claim 12, wherein generating feedback based upon the model comprises: generating feedback based on a difference between the modeled analyzed physiologic data and an optimal performance data associated with an exercise routine. 16. The model of claim 15, wherein the difference includes a quantitative comparison and a qualitative comparison. 17. A method of monitoring physiologic data associated with an exercise routine performed by a user, comprising: selecting an exercise routine using an input/output device;receiving an exercise object for a model exercise routine associated with the selected exercise routine at a system processor;transmitting physiologic data associated with sensed physiologic conditions of a user to the system processor using a wearable transducer worn by the user;identifying a type of movement indicated by the received physiological data using the system processor;analyzing the transmitted physiologic data using the system processor based on the identified type of movement;generating a model based on the analyzed transmitted physiologic data using the system processor;comparing the exercise object with the model using the system processor; andgenerating selective feedback based on the comparison using the system processor. 18. The method of claim 17, wherein analyzing the transmitted physiologic data comprises: analyzing the transmitted physiologic data with a multilayer perceptron, support vector machine, or hidden Markov (MSH) model. 19. The method of claim 18, wherein analyzing the transmitted physiologic data with the MSH model comprises: determining a change in a x-axis orientation of the plurality of parts of the user;determining a change in a y-axis orientation of the plurality of parts of the user;determining a change in a z-axis orientation of the plurality of parts of the user;determining a change in a three dimensional velocity of the plurality of parts of the user;determining a range of motion based on the physiologic data; anddetermining the strength of a muscle based on the physiologic data. 20. The method of claim 17, wherein generating selective feedback based on the comparison comprises: generating selective feedback based on a difference between the modeled analyzed physiologic data and an optimal performance data associated with an exercise routine, wherein the difference includes a quantitative comparison and a qualitative comparison.