This work introduces a method for high-accuracy EMG based gesture identification. A newly developed deep learning method, namely, deep residual shrinkage network is applied to perform gesture identification. Based on the feature of EMG signal resulting from gestures, optimizations are made to improve the identification accuracy. Finally, three different algorithms are applied to compare the accuracy of EMG signal recognition with that of DRSN. The result shows that DRSN excel traditional neural networks in terms of EMG recognition accuracy. This paper provides a reliable way to classify EMG signals, as well as exploring possible applications of DRSN.

翻译：这项工作引入了一种基于高精度 EGM 的手势识别方法,一种新开发的深层学习方法,即运用深残余缩水网络进行手势识别;根据来自手势的EG信号的特点,优化了识别准确性;最后,采用了三种不同的算法,将EGM信号识别的准确性与DRSN信号识别的准确性进行比较;结果显示DRSN在EG的识别准确性方面优于传统神经网络;本文件为EMG信号分类以及探索DRSN的可能应用提供了可靠的方法。