Forward estimation of joint torque from EMG signal through muscle synergy combinations

Zhan Li, M. Hayashibe, D. Guiraud
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引用次数: 1

Abstract

Human movement is a result of synergetic combinations of multiple muscle contractions. The summation of motor unit action potentials can be measured through Electromyography (EMG), thus the processed EMG can be regarded as the muscle activation to be employed to estimate joint movement or torque production. Such forward relationship for representing the joint torque can be established and identified through associated EMG/activations of extension and flexion muscle groups. On the other hand, muscle synergy always exists indicating how quantitatively central nervous system (CNS) drives correlated muscle groups to accomplish the joint torque generation. In this paper, we investigate the approaches of estimating the ankle joint torque from EMG/activatons of associated muscle groups. The approaches discussed fall into two main categories: i) full utilization of both of extension and flexion EMG/activations for estimating the joint torque; ii) exploitation of muscle synergy extraction of EMG/actvations and consequent usage of extracted components in reduced space for estimating the joint torque. Comparison is made between the two methods with experimental data of five able-bodied subjects. From the results we conclude that, method ii) with muscle synergy extraction may not degrade the performance of method i) but meanwhile show the the muscle synergic ratios for generating the joint torque, and involvement of joint position and velocity information can improve the estimation for both methods.
通过肌肉协同组合从肌电信号中正演估计关节扭矩
人体运动是多种肌肉收缩协同组合的结果。通过肌电图(Electromyography, EMG)可以测量运动单元动作电位的总和,因此处理后的肌电图可以视为肌肉激活,用于估计关节运动或扭矩产生。这种表示关节扭矩的正向关系可以通过肌电图/伸展肌群和屈曲肌群的相关激活来建立和识别。另一方面,肌肉协同作用一直存在,这表明中枢神经系统如何定量地驱动相关肌肉群来完成关节扭矩的产生。在本文中,我们研究了通过肌电图/相关肌肉群的激活来估计踝关节扭矩的方法。所讨论的方法主要分为两大类:i)充分利用伸展和屈曲肌电图/激活来估计关节扭矩;ii)利用肌肉协同提取肌电图/激活,并随后在减少的空间中使用提取的成分来估计关节扭矩。并结合5名健全人的实验数据,对两种方法进行了比较。结果表明,采用肌肉协同提取的方法ii)不会降低方法i)的性能,但同时显示了产生关节扭矩的肌肉协同比率,并且关节位置和速度信息的参与可以改善两种方法的估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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