Evaluating the Performance of Neural Network and Kalman Filter Based Linear Model on Classification of Hand EMG Signals
Evaluating the Performance of Neural Network and Kalman Filter Based Linear Model on Classification of Hand EMG Signals
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DOI：
10.1109/jac-ecc48896.2019.9051106
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Abdullah Ahmed;M. Magdy;A. El-Assal;A. El-Betar;Hussein F. M. Ali
中科院分区：
文献类型：
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作者：
Abdullah Ahmed;M. Magdy;A. El-Assal;A. El-Betar;Hussein F. M. Ali
近年来，许多革命性的算法被设计用于提高神经网络分类的性能。本文旨在评估这些算法之一，直观的假肢手控制的效率。我们使用了一个神经网络和卡尔曼滤波器为基础的线性模型的分类的4个运动模式，通过招募一个单一的肌电通道电极的组合。识别准确率达到95.4%，均方误差为0.0473。实验结果表明，与传统的分类策略相比，该方法具有良好的应用前景和竞争力。
In recent years, many revolutionary algorithms were designed for enhancing the performance of the neural network classification. This paper aims at evaluating the efficiency of one of these algorithms in intuitive control of the prosthetic hands. We used a combination of a neural network and a Kalman filter based linear model for the classification of 4 movement patterns by recruiting a single electromyographic channel electrode. The resultant recognition accuracy reached 95.4% with a mean squared error of 0.0473. The results show that the proposed technique is promising and competitive compared to traditional classification strategies.