Wearable system for monitoring strength training

• US 8,500,604 B2
• Filed: 10/17/2009
• Issued: 08/06/2013
• Est. Priority Date: 10/17/2009
• Status: Active Grant

• Current Assignee: Robert Bosch GmbH
• Original Assignee: Robert Bosch GmbH
• Inventors: Srinivasan, Soundararajan, Heit, Juergen, Gacic, Aca, Kapoor, Rahul, Andrews, Burton W
• Primary Examiner(s): Thanh, Loan
• Assistant Examiner(s): GANESAN, SUNDHARA M
• Application Number:
  US12/581,124
• Publication Number:
  US 20110092337A1
• Time in Patent Office: 1,389 Days
• Field of Search: 482 1- 9, 600/587, 600/595, 434/247, 738/654, 733/790.1
• US Class Current: 482/8
• CPC Class Codes: A63B 2024/0012 Comparing movements or motion sequences with a registered reference
• CPC Class Codes: A63B 2024/0068 Comparison to target or threshold, previous performance or not real time comparison to other individuals
• CPC Class Codes: A63B 2024/0071 Distinction between different activities, movements, or kind of sports performed
• CPC Class Codes: A63B 2024/0078 Exercise efforts programmed as a function of time
• CPC Class Codes: A63B 2071/0625 Emitting sound, noise or music
• CPC Class Codes: A63B 2071/0647 Visualisation of executed movements
• CPC Class Codes: A63B 2071/0655 Tactile feedback
• CPC Class Codes: A63B 2220/12 Absolute positions, e.g. by using GPS
• CPC Class Codes: A63B 2220/17 Counting, e.g. counting periodical movements, revolutions or cycles, or including further data processing to determine distances or speed
• CPC Class Codes: A63B 2220/18 Inclination, slope or curvature
• CPC Class Codes: A63B 2220/20 Distances or displacements
• CPC Class Codes: A63B 2220/30 Speed
• CPC Class Codes: A63B 2220/40 Acceleration
• CPC Class Codes: A63B 2220/51 Force
• CPC Class Codes: A63B 2220/58 Measurement of force related parameters by electric or magnetic means
• CPC Class Codes: A63B 2220/836 Sensors arranged on the body of the user
• CPC Class Codes: A63B 2225/20 with means for remote communication, e.g. internet or the like
• CPC Class Codes: A63B 2225/50 Wireless data transmission, e.g. by radio transmitters or telemetry
• CPC Class Codes: A63B 2230/00 Measuring physiological parameters of the user
• CPC Class Codes: A63B 2230/06 heartbeat rate only
• CPC Class Codes: A63B 2230/207 : P-O2, i.e. partial O2 value
• CPC Class Codes: A63B 2230/42 : rate
• CPC Class Codes: A63B 2230/50 : temperature
• CPC Class Codes: A63B 2230/75 : calorie expenditure
• CPC Class Codes: A63B 24/0006 : Computerised comparison for qualitative assessment of motion sequences or the course of a movement
• CPC Class Codes: A63B 24/0062 : Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: A63B 24/0075 : Means for generating exercise programs or schemes, e.g. computerized virtual trainer, e.g. using expert databases

Abstract

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.

Claims

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; and

a 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, and

generate 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; and

a 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; and

a 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; and

determine 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; and

determine 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; and

generating 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; and

recommending 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; and

generating 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; and

determining 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.