Hand Gesture recognition using convolutional neural network and attention mechanism

Gesture recognition based on electromyography (sEMG) has garnered significant interest due to its immense potential in motor rehabilitation and auxiliary prosthesis movement. However, conventional deep learning models often struggle to fully capture the complex spatial and temporal dependencies in muscle activity, which can limit their performance in gesture recognition tasks. To address these challenges, we propose a novel CNN-AttST (Convolutional Neural Network with Spatiotemporal Attention) model that integrates a feature extraction module and a classification module. The feature extraction module employs a convolutional neural network to automatically learn relevant features from the raw sEMG data. In parallel, a spatiotemporal attention mechanism is implemented to emphasize critical temporal and spatial aspects of the signals, enhancing the model's ability to capture complex muscle activation patterns. Experimental results show that the CNN-AttST model was validated on both the Ninapro DB2 public dataset and our own collected dataset. On the Ninapro DB2 dataset, the model achieved recognition accuracies of 84.79% and 87.29% for time window lengths of 200ms and 300ms, respectively, outperforming similar works in the field.