论文标题
用于基于EMG的手势识别的深度残留收缩网络
Deep Residual Shrinkage Networks for EMG-based Gesture Identification
论文作者
论文摘要
这项工作介绍了一种基于高准确性EMG的手势识别的方法。一种新开发的深度学习方法，即深层残留收缩网络用于执行手势识别。基于手势引起的EMG信号的特征，进行了优化以提高识别精度。最后，应用三种不同的算法将EMG信号识别的准确性与DRSN的准确性进行比较。结果表明，Drsn在EMG识别准确性方面表现出传统的神经网络。本文提供了一种对EMG信号进行分类以及探索DRSN可能应用的可靠方法。
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.
