Muscular Strength Acquiring Method and Device Based on Musculoskeletal Model

• US 20070256494A1
• Filed: 06/10/2005
• Published: 11/08/2007
• Est. Priority Date: 06/16/2004
• Status: Active Grant

• Current Assignee: University of Tokyo
• Original Assignee: University of Tokyo
• Inventors: Yamane, Katsu, Nakamura, Yoshihiko, Murai, Akihiko, Fujita, Yusuke
• Granted Patent: US 7,308,826 B2
• Time in Patent Office: Days
• US Class Current: 73/379.10
• CPC Class Codes: A61B 5/224 Measuring muscular strength
• CPC Class Codes: A61B 5/4523 Tendons
• CPC Class Codes: A61B 5/4528 Joints A61B5/4533, A61B5/4538 take precedence
• CPC Class Codes: A61B 5/4533 Ligaments

Abstract

The present invention provides a method capable of acquiring a physically and physiologically valid muscular strength from motion data based on a musculoskeletal model. The present invention relates to a method for obtaining muscular tension by performing inverse dynamics calculation of a musculoskeletal model. The method comprises a step for optimizing a contact force τ_C received from an environment using acquired floor reaction force data and a step for optimizing the muscle tension ƒ using acquired motion data, acquired myogenic potential data, and optimized contact force. The motion data, reaction force data and myogenic potential data can be measured at the same time by a behavior capture system.

Claims

1. A method for obtaining muscular tension by performing inverse dynamics calculation of a musculoskeletal model, said method comprising:

obtaining muscular tension by providing reaction force data, motion data and myogenic potential data to the following equation and by optimizing a contact force τ

_C and muscle tension ƒ

.

τ

_G=J^Tƒ

+J_C^Tτ

_C

where τ

_G is generalized force, J is Jacobian of muscles, tendons and ligaments and J_C is Jacobian of contact point.

2. A method for obtaining muscular tension by performing inverse dynamics calculation of a musculoskeletal model by using the following equation

τ

_G=J^Tƒ

+J_C^Tτ

_C

where τ

_G is generalized force, J is Jacobian of muscles, tendons and ligaments and J_C is Jacobian of contact point,

said method comprising;

optimizing a contact force τ

_C received from an environment using acquired reaction force data; and

optimizing the muscle tension ƒ

using acquired motion data, acquired myogenic potential data, and optimized contact force.

3. The method of claim 2, wherein the motion data are obtained by a motion capture system.

4. The method of claim 2, wherein reaction force data are acquired with a force sensor.

5. The method of claim 2, wherein the myogenic potential data are acquired with an electromyograph.

6. The method of claim 2, wherein the motion data, reaction force data and myogenic potential data are measured at the same time.

7. The method of claim 2, wherein said optimizing a contact force τ

_C comprises;

calculating τ

_C that minimizes the objective function

$\begin{array}{c}Z=<br><br>w_H<br><br>{<br><br>E_{\mathrm{hip}}<br><br>{\mathrm{\tau <br><br>}}_G-E_{\mathrm{hip}}<br><br>J_C^T<br><br>{\mathrm{\tau <br><br>}}_C<br><br>}^2+\\ <br><br>w_C<br><br>{<br><br>K_C<br><br>{\mathrm{\tau <br><br>}}_C-{\mathrm{\tau <br><br>}}_C^{*}<br><br>}^2+{<br><br>{\mathrm{\tau <br><br>}}_C<br><br>}^2\end{array}$

subject to the inequality constraint condition

E_vertτ

_C≧

0

where E_vert is a matrix that extracts the vertical forces from τ

_C, τ

*_c is a measured contact force, E_hip is a matrix that extracts required rows, and w_H and w_C are weighting parameters.

8. The method of claim 2, wherein said optimizing a muscle tension ƒ

comprises;

calculating δ

_f, δ

_τ

and ƒ

that minimize the objective function

Z=a_ƒ

^Tδ

_ƒ

+a_τ

^Tδ

_τ

subject to the inequality constraint condition

$\begin{array}{c}-{\mathrm{\delta <br><br>}}_f\le <br><br>K_F<br><br>f-f^{*}\le <br><br>{\mathrm{\delta <br><br>}}_f\\ E_{\mathrm{mtl}}<br><br>f\le <br><br>0\\ {\mathrm{\delta <br><br>}}_f\ge <br><br>0\\ -{\mathrm{\delta <br><br>}}_{\mathrm{\tau <br><br>}}\le <br><br>{\mathrm{\tau <br><br>}}_G^{\mathrm{\prime <br><br>}}-J^T<br><br>f\le <br><br>{\mathrm{\delta <br><br>}}_{\mathrm{\tau <br><br>}}\\ {\mathrm{\delta <br><br>}}_{\mathrm{\tau <br><br>}}\ge <br><br>0\end{array}$

where ƒ

* are muscular strength values, K_F is a matrix that makes ƒ and

measured value correspond to each other, and a_f and a_τ

are constant vectors with positive components.

9. The method of claim 8, said inequality constraint condition further comprising

− δ

_m≦

E_Gƒ

≦

δ

_m
0≦

δ

_m

and said objective function being replaced with

Z=a_ƒ

^Tδ

_ƒ

+a_τ

^Tδ

_τ

+a_m^Tδ

_m

where a_m is a constant vector with positive components.

10. The method of claim 2, said method further comprising storing the obtained motion data and calculated muscle tension linking with each other as database.

11. An apparatus for obtaining muscular tension based on a musculoskeletal model comprising a processing unit and a memory device,

said memory device storing reaction force data, motion data, myogenic potential data and the following equation

τ

_G=J^Tƒ

+J_C^Tτ

_C

where τ

_G is generalized force, J is Jacobian of muscles, tendons and ligaments and J_C is Jacobian of contact point,

said processing unit obtaining muscular tension by providing reaction force data, motion data and myogenic potential data to the following equation and by optimizing a contact force τ

_C and muscle tension ƒ

.

12. The apparatus of claim 11, said processing unit is adapted to perform optimization of a contact force τ

_C received from the environment using acquired ground reaction force data and optimization of the muscle tension ƒ

using acquired motion data, acquired myogenic potential data, and optimized contact force.

13. The apparatus of claim 11, said memory device store inequality constraint condition and objective function that is used for optimization of a contact force τ

_C and muscle tension ƒ

.

14. The apparatus of claim 13, wherein said objective function for optimization of a contact force τ

_C is

15. The apparatus of claim 13, wherein said the objective function for optimization of a muscle tension f is

Z=a_ƒ

^Tδ

_ƒ

+a_τ

^Tδ

_τ

and said the inequality constraint condition is

$\begin{array}{c}-{\mathrm{\delta <br><br>}}_f\le <br><br>K_F<br><br>f-f^{*}\le <br><br>{\mathrm{\delta <br><br>}}_f\\ E_{\mathrm{mtl}}<br><br>f\le <br><br>0\\ {\mathrm{\delta <br><br>}}_f\ge <br><br>0\\ -{\mathrm{\delta <br><br>}}_{\mathrm{\tau <br><br>}}\le <br><br>{\mathrm{\tau <br><br>}}_G^{\mathrm{\prime <br><br>}}-J^T<br><br>f\le <br><br>{\mathrm{\delta <br><br>}}_{\mathrm{\tau <br><br>}}\\ {\mathrm{\delta <br><br>}}_{\mathrm{\tau <br><br>}}\ge <br><br>0\end{array}$

where ƒ

* are muscular strength values, K_F is a matrix that makes ƒ and

measured value correspond to each other, and a_f and a_τ

are constant vectors with positive components.

16. The apparatus of claim 15, said inequality constraint condition further comprising

−

δ

_m≦

E_Gƒ

≦

δ

_m
0≦

δ

_m

and said objective function being replaced with

Z=a_ƒ

^Tδ

_ƒ

+a_τ

^Tδ

_τ

+a_m^Tδ

_m

where a_m is a constant vector with positive components.

17. The apparatus of claim 11 further comprising a motion capture system for obtaining motion data.

18. The apparatus of claim 11 further comprising a force sensor for obtaining reaction force data.

19. The apparatus of claim 11 further comprising an electromyograph for obtaining myogenic potential data.

20. The apparatus of claim 11 further comprising means for linking the motion data and muscle tension and wherein linked motion data and muscle tension are stored in the memory device as database.

21. A computer program for causing a computer to operate as the following means to acquire muscular tension by performing inverse dynamics calculation of musculoskeletal model,

means for storing reaction force data, motion data, myogenic potential data and the following equation

τ

_G=J^Tƒ

+J_C^Tτ

_C

where τ

_G is generalized force, J is Jacobian of muscles, tendons and ligaments and J_C is Jacobian of contact point,

means for optimizing a contact force τ

_C received from an environment using reaction force data,

means for storing the optimized contact force,

means for optimizing a muscular tension ƒ

by using motion data, myogenic potential data and the optimized contact force

22. The program of claim 21 further causing a computer to operate as means for linking the motion data and muscle tension and wherein linked motion data and muscle tension are stored as database.