Surface EMG Pattern Recognition Using Long Short-Term Memory Combined with Multilayer Perceptron

Motion classification based on pattern recognition of surface EMG (sEMG) signals is a promising approach for prosthetic control. We present a pattern recognition model that combines long short-term memory (LSTM) network with multiplayer perceptron (MLP) for sEMG signals feature learning and classification. The LSTM network captures temporal dependencies of the sEMG signals while the MLP has no inherent temporal dynamics but focuses on the static characteristics. The combination of the two networks would learn a feature space that contains both the dynamic and static information of the sEMG signals, which helps to improve the motion classification accuracy. The architecture of the proposed network was optimized by investigating the appropriate width and depth of the neural network as well as the dropout to achieve the best classification results. The performance of the proposed pattern recognition model was evaluated using Ninapro database. The results show that the proposed model can produce better classification accuracy than most of the well-known recognition techniques.