  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.

Signal Processing;Machine Learning