丁其川, 赵新刚, 韩建达. 基于肌电信号的上肢多关节连续运动估计[J]. 机器人, 2014, 36(4): 469-476. DOI: 10.13973/j.cnki.robot.2014.0469
引用本文: 丁其川, 赵新刚, 韩建达. 基于肌电信号的上肢多关节连续运动估计[J]. 机器人, 2014, 36(4): 469-476. DOI: 10.13973/j.cnki.robot.2014.0469
DING Qichuan, ZHAO Xingang, HAN Jianda. EMG-based Estimation for Multi-joint Continuous Movement of Human Upper Limb[J]. ROBOT, 2014, 36(4): 469-476. DOI: 10.13973/j.cnki.robot.2014.0469
Citation: DING Qichuan, ZHAO Xingang, HAN Jianda. EMG-based Estimation for Multi-joint Continuous Movement of Human Upper Limb[J]. ROBOT, 2014, 36(4): 469-476. DOI: 10.13973/j.cnki.robot.2014.0469

基于肌电信号的上肢多关节连续运动估计

EMG-based Estimation for Multi-joint Continuous Movement of Human Upper Limb

  • 摘要: 为了建立联系肌电信号与上肢关节连续运动量的一般化模型,首先利用Vicon视觉系统分析了人体上肢的运动特性,然后采集与上肢运动直接关联的肌肉表面肌电信号,并从中提取肌肉活跃度特征; 在此基础上,采用主元分析算法对关节运动解耦,并计算决定关节运动的肌肉活跃主元,最后通过拟合高阶多项式构建了映射肌肉活跃度到上肢多关节角度的运动模型. 大量的实验结果验证了利用本文建立的模型可以精确估计人体关节连续运动角度,而且本文方法的估计性能优于传统神经网络.

     

    Abstract: In order to build a generalized model to relate the electromyography (EMG) signals and the continuous movement variables of the upper limb joints, the motion of human's upper limb is firstly investigated by utilizing the Vicon visual system. Then, the surface EMG (sEMG) signals are sampled from the muscles directly concerned with the upper limb motion, and the muscle activities are extracted from the raw sEMG signals. On the basis, the principal component analysis algorithm is employed to decouple the joint motion and calculate the main elements of muscle activities which determine the joint motion. Finally, a motion model is constructed to map the muscle activities to the multijoint angles of the upper limb via fitting high-order polynomials. Extensive experiments are conducted to verify that the continuous joint angles of the upper limb motion can be accurately estimated by using the proposed model. Moreover, the proposed method is superior to a traditional neural network in estimation performance.

     

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