Control of an Externally Powered Prosthetic Forearm Using Raw-EMG Signals
Control of an Externally Powered Prosthetic Forearm Using Raw-EMG Signals
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使用原始肌电图信号控制外部供电的假肢前臂
DOI：
10.9746/sicetr1965.40.1124
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
T. Tsuji
中科院分区：
文献类型：
--
作者：
O. Fukuda;N. Bu;T. Tsuji
本文提出了一种新的运动识别方法，利用原始肌电信号，以提高控制性能的假肢前臂。该方法使用了一种新的递归神经网络的基础上，一个发达的隐马尔可夫模型。该网络可以使用递归连接对肌电信号的时间序列进行建模，并将滤波和模式识别等不同的两个过程统一在一个网络中实现。网络的权系数通过时间反向传播算法进行调整。在实验中，五名受试者，其中包括两名截肢者进行控制的假肢前臂。我们证实，所提出的方法可以科普时变特性的肌电信号，并可以实现相当高的判别精度与以前的方法相比。该方法还提高了识别结果对输入肌电信号模式的响应。
This paper proposes a new motion discrimination method using raw EMG signals to improve control performance of a prosthetic forearm. This method uses a novel recurrent neural network based on a well developed hidden Markov model. The proposed network can model a time sequence of EMG signals using recurrent connections, and different two processes such as filtering and a pattern discrimination are unified together and realized in a single network. Weight coefficients of the network are regulated by the back-propagation through time algorithm. In the experiments, five subjects which include two amputees performed control of the prosthetic forearm. We confirmed that the proposed method could cope with time-varying characteristics of EMG signals and could achieve considerably high discrimination accuracy compared with the previous methods. Response of the discrimination result to the input EMG pattern was also improved using the proposed method.