sEMG-Based Gesture Recognition Via Multi-Feature Fusion Network.

The sparse surface electromyography-based gesture recognition suffers from the problems of feature information not richness and poor generalization to small sample data. Therefore, a multi-feature fusion network (MFF-Net) model is proposed in this paper. This network incorporates long short-term memory (LSTM) and the attention mechanism into the model, and three sub-networks are constructed for enhancement of features in the time, frequency and spatial domains, respectively.