A Residual Temporal Convolutional with Attention Neural Network for Electromyogram-Based Hand Gesture Recognition

Abstract

Electromyography (EMG)-based hand gesture classification is a developing core technology for designing intuitive and responsive human-computer interaction, notably for prosthetic control. EMG signals, which reflect muscle activity during contraction, offer a non-invasive and effective method for capturing user gestures. However, because of their natural variability, noise, and temporal richness pose significant hurdles to precise gesture recognition. In this paper, we investigate the use of causal convolutional layers, which are suitable for sequential data, to improve hand gesture recognition from raw EMG signals. We propose a deep neural network which bases on temporal convolutions and integrates residual connections and contextual attention in an end to end hand gesture recognition system. Furthermore, we apply multiple data augmentation techniques to mitigate intra-subject variability and enhance model generalization. Our approach is evaluated on the benchmark NinaProDB1 dataset. The proposed model show impressive classification performance with an average accuracy of 95.31% and where the majority of the gestures from various subjects were accurately recognized. These results demonstrate the effectiveness of causal convolutions and attention mechanisms for robust EMG-based gesture recognition.