A multi-stream convolutional neural network for sEMG-based gesture recognition in muscle-computer interface

In muscle-computer interface (MCI), deep learning is a promising technology to build-up classifiers for recognizing gestures from surface electromyography (sEMG) signals. Motivated by the observation that a small group of muscles play significant roles in specific hand movements, we propose a multi-stream convolutional neural network (CNN) framework to improve the recognition accuracy of gestures by learning the correlation between individual muscles and specific gestures with a "divide-and-conquer" strategy. Its pipeline consists of two stages, namely the multi-stream decomposition stage and the fusion stage. During the multi-stream decomposition stage, it first decomposes the original sEMG image into equalsized patches (streams) by the layout of electrodes on muscles, and for each stream, it independently learns representative features by a CNN. Then during the fusion stage, it fuses the features learned from all streams into a unified feature map, which is subsequently fed into a fusion network to recognize gestures. Evaluations on three benchmark sEMG databases showed that our proposed multi-stream CNN framework outperformed the state-of-the-arts on sEMG-based gesture recognition. (C) 2017 Elsevier B.V. All rights reserved.