Poster: Gesture Recognition Based On Convlstm-Attention Implementation Of Small Data Semg Signals

In this paper, we propose a system based on Convolutional Long Short Term Memory (ConvLSTM)-Attention Mechanism (AM) to preserve spatial features and time characteristics for surface electromyography (sEMG) signals. We assume that this method can perform more robustly under training with small data than a Convolutional Neural Network (CNN). To test the performance of this method, we measured sEMG signals for eight hand gestures. Whereas the pure CNN based model and ConvLSTM (not have AM) model, had accuracies of 88.2 and 89.7%, respectively, our proposed ConvLSTM-AM method achieved 92.8% accuracy. Thus, proposed method has a better recognition rate than the CNN, which only uses spatial features. Through the results of the experiment, we believe that the proposed method can effectively improve the robustness of Deep Learning to small sample sEMG signals.