Identification of human musculo-tendon subject specific dynamics using musculo-skeletal computations and non linear least square
Identification of human musculo-tendon subject specific dynamics using musculo-skeletal computations and non linear least square
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DOI：
10.1109/biorob.2006.1639087
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发表时间：
2006-07
期刊：
影响因子：
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通讯作者：
G. Venture;K. Yamane;Y. Nakamura
中科院分区：
文献类型：
--
作者：
G. Venture;K. Yamane;Y. Nakamura
人体动力学是非常复杂的，因为有很多的自由度和肌肉。此外，肌肉的行为是非线性的和特定学科的。本文提出了一种估计人体肌肉特定动态参数的新颖方法。首先提出了生物力学界常用的肌肉动力学模型。然后给出了力-活度-长度-速度关系。一个应用与四个肌肉，屈伸肘关节提出。在动作捕捉工作室中分析人类手臂的动态，以获得激发肌肉动态的特定动作。运动的获取允许使用人体肌肉-骨骼模型计算逆运动学和逆动力学，最后估计肌肉力（肌肉动态模型的输入）。使用肌电图（emg）测量浅表肌肉的活动（也是肌肉动态模型的输入）。然后用非线性最小二乘法和牛顿-高斯算法估计这些肌肉的特定参数。给出了有效主体的实验结果
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. This paper presents an original method to estimate the subject specific dynamic parameters of the human muscles. A dynamic model of muscle, commonly used by the biomechanics community, is first presented. Then the force-activity-length-velocity relation is given. An application with four muscles, to the flexion/extension of the elbow joint is proposed. The human arm dynamics is analyzed in a motion capture studio for specific movements that excite the muscle dynamics. The acquisition of movements allows to compute the inverse kinematics and the inverse dynamics using a musculo-skeletal model of the human body, and finally the muscle force is estimated (input of the dynamic model of the muscle). The activity (also input of the dynamic model of the muscle) is measured using electromyography (EMGs) for the superficial muscles. The subject specific parameters of those muscles are then estimated by the non-linear least square method with Newton-Gauss algorithm. Experimental results obtain for valid subject are given