Deep Residual Shrinkage Networks for EMG-based Gesture Identification
This work addresses gesture identification for EMG signal processing, but it appears incremental as it adapts an existing method to a specific domain.
The paper tackled EMG-based gesture identification by applying a deep residual shrinkage network (DRSN) with optimizations for EMG signals, resulting in DRSN outperforming traditional neural networks in recognition accuracy.
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.