Muscle Strength and Mass Distribution Identification Toward Subject-Specific Musculoskeletal Modeling

Mitsuhiro Hayashibe; Gentiane Venture; Ko Ayusawa; Yoshihiko Nakamura

lirmm-00637754, version 1

Muscle Strength and Mass Distribution Identification Toward Subject-Specific Musculoskeletal Modeling

Mitsuhiro Hayashibe ( Author to contact preferably , ) ^1, 2, Gentiane Venture ³, Ko Ayusawa ⁴, Yoshihiko Nakamura ⁴

IROS'11: IEEE/RSJ International Conference on Intelligent Robots and Systems 3701-3707

Abstract: In current biomechanics approach, the assumptions are commonly used in body-segment parameters and muscle strength parameters due to the difficulty in accessing those subject-specific values. Especially in the rehabilitation and sports science where each subject can easily have quite different anthropometry and muscle condition due to disease, age or training history, it would be important to identify those parameters to take benefits correctly from the recent advances in computational musculoskeletal modeling. In this paper, Mass Distribution Identification to improve the joint torque estimation and Muscle Strength Identification to improve the muscle force estimation were performed combined with previously proposed methods in muscle tension optimization. This first result highlights that the reliable muscle force estimation could be extracted after these identifications. The proposed framework toward subject-specific musculoskeletal modeling would contribute to a patient-oriented computational rehabilitation.

1: DEMAR (INRIA Sophia Antipolis)
INRIA – CNRS : UMR5506 – Université Montpellier II - Sciences et techniques – Université Montpellier I
2: Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM)
CNRS : UMR5506 – Université Montpellier II - Sciences et techniques
3: Tokyo University of Agriculture and Technology
Tokyo University of Agriculture and Technology
4: Nakamura Laboratory (Department of Mechano-Informatics)
University of Tokyo

lirmm-00637754, version 1
http://hal-lirmm.ccsd.cnrs.fr/lirmm-00637754
oai:hal-lirmm.ccsd.cnrs.fr:lirmm-00637754
From: Mitsuhiro Hayashibe <>
Submitted on: Wednesday, 2 November 2011 18:15:52
Updated on: Friday, 4 November 2011 11:36:03

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%% lirmm-00637754, version 1
%% http://hal-lirmm.ccsd.cnrs.fr/lirmm-00637754
@inproceedings{hayashibe:lirmm-00637754,
    hal_id = {lirmm-00637754},
    url = {http://hal-lirmm.ccsd.cnrs.fr/lirmm-00637754},
    title = {{Muscle Strength and Mass Distribution Identification Toward Subject-Specific Musculoskeletal Modeling}},
    author = {Hayashibe, Mitsuhiro and Venture, Gentiane and Ayusawa, Ko and Nakamura, Yoshihiko},
    abstract = {{In current biomechanics approach, the assumptions are commonly used in body-segment parameters and muscle strength parameters due to the difficulty in accessing those subject-specific values. Especially in the rehabilitation and sports science where each subject can easily have quite different anthropometry and muscle condition due to disease, age or training history, it would be important to identify those parameters to take benefits correctly from the recent advances in computational musculoskeletal modeling. In this paper, Mass Distribution Identification to improve the joint torque estimation and Muscle Strength Identification to improve the muscle force estimation were performed combined with previously proposed methods in muscle tension optimization. This first result highlights that the reliable muscle force estimation could be extracted after these identifications. The proposed framework toward subject-specific musculoskeletal modeling would contribute to a patient-oriented computational rehabilitation.}},
    language = {English},
    affiliation = {DEMAR - INRIA Sophia Antipolis , Laboratoire d'Informatique de Robotique et de Micro{\'e}lectronique de Montpellier - LIRMM , Tokyo University of Agriculture and Technology , Nakamura Laboratory - Department of Mechano-Informatics},
    booktitle = {{IROS'11: IEEE/RSJ International Conference on Intelligent Robots and Systems}},
    pages = {3701-3707},
    address = {San Francisco, United States},
    audience = {international },
    year = {2011},
    month = Sep,
    pdf = {http://hal-lirmm.ccsd.cnrs.fr/lirmm-00637754/PDF/IROS11\_1559\_FI.pdf},
}

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