Recursive Filter Design for Estimating Time Varying Multijoint Human Arm Viscoelasticity
Recursive Filter Design for Estimating Time Varying Multijoint Human Arm Viscoelasticity
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发表时间：
2006
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通讯作者：
M. Deng;A. Inoue;H. Gomi;Y. Hirashima
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作者：
M. Deng;A. Inoue;H. Gomi;Y. Hirashima
时变的人体多关节手臂动力学模型可以由两个因素，简化的肌肉骨骼动力学和不确定性因素组成的测量噪声和建模误差的刚体动力学。在某些情况下，不确定性因子可能不是高斯的;卡尔曼滤波器不再是最佳滤波器。本文针对非高斯环境，提出了一种用于估计人体多关节手臂运动时粘弹性的递归滤波器设计方法。该方法是基于得分函数的方法与D U分解算法和等效噪声技术的多个新息过程。在非高斯噪声条件下，该方法在捕获人体手臂模型纹理信息时具有更高的准确性和鲁棒性，而标准卡尔曼滤波器的性能明显下降。
The time varying human multijoint arm dynamics can be modeled by two factors, simplified musculoskeletal dynamics and the uncertainty factor consisting of measurement noises and modeling error of a rigid body dynamics. In some cases, the uncertainty factor may not be Gaussian; the Kalman filter is no longer the optimal filter. In this paper, for the non-Gaussian environment, a recursive filter design method for estimating time varying human multijoint arm viscoelasticity during the arm is moving is presented. The method is based on a score function approach associated with D U factorization algorithm and equivalent noise technique for multiple innovations process. The proposed method for an experimentbased human arm model provides greater accuracy and robustness in capturing texture information of the model under the case of non-Gaussian noises, while the performance of standard Kalman filter degrades significantly.