Discrimination of Forearm Motions from EMG Signals by Error Back Propagation Typed Neural Network Using Entropy
Discrimination of Forearm Motions from EMG Signals by Error Back Propagation Typed Neural Network Using Entropy
复制标题
利用熵的误差反向传播型神经网络从 EMG 信号中区分前臂运动
DOI：
10.9746/sicetr1965.29.1213
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发表时间：
1993
期刊：
影响因子：
--
通讯作者：
M. Nagamachi
中科院分区：
文献类型：
--
作者：
T. Tsuji;Hiroyuki Ichinobe;Koji Ito;M. Nagamachi
本文提出了一种利用误差反向传播型神经网络从被试的肌电信号中估计被试的运动意图的方法。肌电信号的运动估计在多功能动力假肢控制、机械手远程操作、虚拟现实等领域的人机界面手段中具有重要的应用价值。该方法中使用的神经网络可以从四对电极测量到受试者前臂和手部的六次运动的肌电图模式中学习映射。包括一名截肢者在内的几名受试者的实验结果如下：1)该方法对不同电极位置的6种运动进行识别，准确率约为90%；2)利用网络输出的熵来暂停识别，可以减少不良识别；3)利用在线学习，神经网络可以适应肌电模式的一些动态变化；4)利用肌电信号的频率特性和幅度特性，减少了学习收敛所需的迭代次数。
The present paper proposes a method to estimate the motion intended by a subject from his EMG signals using error back propagation typed neural networks. Estimation of the motion from the EMG signals is useful for means of human interface in such fields as control of multi-functional powered prosthesis, teleoperation of robot manipulators, virtual reality. The neural network used in the method can learn a mapping from the EMG patterns measured from four pairs of electrodes to six motions of forearm and hand intended by the subject. The experimental results for several subjects including an amputee show the following: 1) the method can discriminate six motions with the accuracy about 90 percent for several electrode locations, 2) ill-discrimination can be decreased by suspending discrimination using entropy of network output, 3) the neural network can adapt to some dynamic variations of the EMG patterns using on-line learning, and 4) utilizing frequency characteristics as well as amplitude characteristics of the EMG signals reduces the number of iterations required for learning convergence.