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首页> 外文期刊>Journal of Intelligent Information Systems >EMG-Based Motion Discrimination Using a Novel Recurrent Neural Network
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EMG-Based Motion Discrimination Using a Novel Recurrent Neural Network

机译：使用新型递归神经网络的基于EMG的运动识别

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摘要

This paper presents a pattern discrimination method for electromyogram (EMG) signals for application in the field of prosthetic control. The method uses a novel recurrent neural network based on the hidden Markov model. This network includes recurrent connections, which enable modeling time series, such as EMG signals. Weight coefficients in the network can be learned using a well-known back-propagation through time algorithm. Pattern discrimination experiments were conducted to demonstrate the feasibility and performance of the proposed method. We were able to successfully discriminate forearm motions using the EMG signals, and achieved considerably high discrimination performance compared with other discrimination methods.
机译：本文提出了一种用于肌电图（EMG）信号的模式判别方法，用于假体控制领域。该方法使用基于隐马尔可夫模型的新型递归神经网络。该网络包括循环连接，这些连接使得能够建模时间序列，例如EMG信号。可以使用众所周知的时间反向传播算法来学习网络中的权重系数。进行模式识别实验以证明该方法的可行性和性能。我们能够使用EMG信号成功判别前臂运动，并且与其他判别方法相比，获得了相当高的判别性能。

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