APPLICATION OF NON-LINEAR LEAST SQUARE METHOD TO ESTIMATE THE MUSCLE DYNAMICS OF THE ELBOW JOINT

Abstract This paper presents an original use of the non-linear least-squares method applied to the muscle dynamics of the human body. The human body dynamics is very complex because of the number of degrees of freedom and of the number of muscles, moreover the behavior of muscles is non-linear and subject specific. A dynamic model of muscle, commonly used by the biomechanics community, which is presented, gives a relation between muscle force, activity, length and velocity. An application to the flexion/extension of the joint elbow using four muscles is then proposed. The dynamic parameters of those four muscles are estimated experimentally by the non-linear least square method. The activity (input of the dynamic model of the muscle) is measured using electromyography. The human arm dynamics is analyzed in a motion capture studio which acquisition of movements allows to compute the inverse kinematics and the inverse dynamics. Finally the muscle force is estimated (input of the dynamic model of the muscle).

[1]  Sybert H. Stroeve,et al.  Learning combined feedback and feedforward control of a musculoskeletal system , 1996, Biological Cybernetics.

[2]  D. Lloyd,et al.  An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo. , 2003, Journal of biomechanics.

[3]  Gentiane Venture,et al.  Identifying musculo-tendon parameters of human body based on the musculo-skeletal dynamics computation and Hill-Stroeve muscle model , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

[4]  F. Zajac.  Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. , 1989, Critical reviews in biomedical engineering.

[5]  Rieko Osu,et al.  Short- and long-term changes in joint co-contraction associated with motor learning as revealed from surface EMG. , 2002, Journal of neurophysiology.

[6]  Katsu Yamane,et al.  High-precision and high-speed motion capture combining heterogeneous cameras , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[7]  Katsu Yamane,et al.  Estimation of Physically and Physiologically Valid Somatosensory Information , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[8]  Peter Teunissen,et al.  Nonlinear least squares , 1990 .

[9]  T. Ohtsuki,et al.  Sequential muscle activity and its functional role in the upper extremity and trunk during overarm throwing , 2002, Journal of sports sciences.