Method and system for monitoring sport related fitness by estimating muscle power and joint force of limbs

• US 8,246,555 B2
• Filed: 01/29/2010
• Issued: 08/21/2012
• Est. Priority Date: 10/07/2009
• Status: Active Grant

• Current Assignee: Industrial Technology Research Institute
• Original Assignee: Industrial Technology Research Institute
• Inventors: Chiu, Yu-Hsien, Hsia, Chi-Chun, Wang, Pei-Jung
• Primary Examiner(s): RICHMAN, GLENN E
• Application Number:
  US12/696,396
• Publication Number:
  US 20110082394A1
• Time in Patent Office: 935 Days
• Field of Search: 482 1- 9, 482900-902, 600/595, 434/247, 702/41
• US Class Current: 600/595
• CPC Class Codes: A61B 2503/10 Athletes
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/1122 of movement trajectories
• CPC Class Codes: A61B 5/1124 Determining motor skills
• CPC Class Codes: A61B 5/22 Ergometry; Measuring muscular strength or the force of a muscular blow exercising apparatus A63B21/00 ; measuring of work or force in general G01L
• CPC Class Codes: A61B 5/4528 Joints A61B5/4533, A61B5/4538 take precedence
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G09B 19/0038 Sports
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

The present invention relates to a method and system for monitoring sport related fitness by estimating muscle power and joint force of limbs, in which the system comprises a sensing module and a force/track detection module, wherein sensor values from the sensing module are fed to the force/track detection module to be used as base for estimating feature parameters and classifying a motion series relating to muscle power and joint force of limbs so as to obtain skill-related fitness parameters corresponding to the sensing of the sensor module.

Claims

1. A method for monitoring sport related fitness by estimating muscle power and joint force of limbs, comprising the steps of:

using a sensing module to obtain sensor values; and

using a force/track detection module to receive the sensor values from the sensing module to be used as base for estimating feature parameters and classifying a motion series relating to muscle power and joint force of the limbs so as to obtain fitness parameters corresponding to the sensing of the sensor module.

2. The method of claim 1, wherein the limbs include at least one type of limbs selected from the group consisting of:

upper limbs and lower limbs.

3. The method of claim 1, wherein the force/track detection module is enabled to tracking an exercise performed by the limbs, including upper limbs and lower limbs, while the detected track is resulting from at least one exercise selecting from the group consisting of:

the stretching exercise of the upper limbs and the stretching exercise of the lower limbs.

4. The method of claim 3, wherein the stretching exercise of the upper limbs includes at least one movement selected from the group consisting of:

lifting a heavy object, pushing/pulling, punching, racket swinging, and pitching.

5. The method of claim 3, wherein the stretching exercise of the lower limbs includes at least one movement selected from the group consisting of:

one foot lifting, one leg squatting, one leg standing, two legs standing.

6. The method of claim 1, wherein the sensing module uses at least one device selected from the group consisting of:

a pressure detecting unit, and a position detecting unit, for detecting the sensor values.

7. The method of claim 6, wherein the pressure detecting unit is a device selected from the group consisting of:

a pressure mat, a foot pad, and a force plate.

8. The method of claim 6, wherein the position detecting unit includes at least one device selected from the group consisting of:

accelerometers and gyroscopes.

9. The method of claim 8, wherein each device included in the position detecting unit is adapted to be arranged at a position selected from the group consisting of:

an arm, an elbow, waist, and a knee of a user.

10. The method of claim 6, wherein the sensing module further comprises:

a processing unit, for fetching and synchronizing the sensor values.

11. The method of claim 1, wherein the force/track detection module further comprises:

a parameter calculation module, for fetching the sensor values from the sensing module to be used in a calculation for obtaining a signal capable of defining the tracking of the motion series in the space, as well as in an estimation for obtaining feature parameters relating to muscle power and joint force of the limbs; and

a motion identification module, for performing a matching calculation upon the feature parameter resulting from the estimation of the parameter calculation module and at least one motion track model so as to perform a classification basing on the comparison and thus obtain the motion series to be used for estimating the fitness parameters.

12. The method of claim 11, wherein the parameter calculation module further comprises:

a feature capturing unit, for fetching the sensor values from the sensing module to be used in an estimation for obtaining the feature parameters relating to muscle power and joint force of the limbs.

13. The method of claim 11, wherein the parameter calculation module further comprises:

an ergonomic model database, for storing data relating to ergonomic models to be used as basis for estimating the feature parameters.

14. The method of claim 11, wherein the motion identification module further comprises:

a motion model database, having the at least one motion track model stored therein.

15. The method of claim 14, wherein each motion track model is at least one model selected from the group consisting of:

a motion track template, a motion track statistic model, a motion track probabilistic model.

16. The method of claim 11, wherein the motion identification module further comprises:

a match unit, for performing the matching calculation upon the feature parameters resulting from the estimation of the parameter calculation module and the at least one motion track model so as to obtain an optimal motion track corresponding to the feature parameters to be classified into the motion series that is used for estimating the fitness parameters.

17. The method of claim 11, wherein the matching calculation performed by the motion identification module is at least one calculation selected from the group consisting of:

a distance calculation and a similarity calculation.

18. The method of claim 11, wherein the motion identification module further comprises:

a fitness parameter database, for storing skill-related fitness parameters while providing the stored skill-related fitness parameters to be compared with the fitness parameters obtained from the estimation of the motion identification module.

19. The method of claim 18, wherein the motion identification module further comprises:

a comparison unit, for comparing the fitness parameters obtained from the estimation of the motion identification module with the skill-related fitness parameters stored in the fitness parameter database so as to output a comparison result accordingly.

20. The method of claim 19, wherein the motion identification module further comprises:

a notification unit, electrically connected to the comparison unit to be used for displaying the comparison result.

21. The method of claim 20, wherein the notification unit is a device selected from the group consisting of:

cellular phones, personal digital assistants (PDAs), computers, speakers, alarms, indication lamps, and other audio/video devices.

22. The method of claim 20, wherein the notification unit is connected to the comparison unit through a network system.

23. The method of claim 1, wherein each feature parameter is related to at least one value selected from the group consisting of:

gravity center of human body, biped center of mass, displacement of the center of gravity, direction, speed, distance traveled, relative position, coefficient of rigidity, joint angle, variation of joint angle, muscle power.

24. The method of claim 1, wherein the fitness parameter is related to at least one value selected from the group consisting of:

muscular strength, muscular endurance, agility, flexibility, muscle power, speed, and balance.