SGRN: SEMG-based gesture recognition network with multi-dimensional feature extraction and multi-branch information fusion

Spatial-temporal feature extraction methods have been widely used for gesture classification using surface electromyography (sEMG) signals. Nevertheless, prevalent methodologies in this realm are constrained by limitations stemming from their simplistic single-branch architectures, which impose sequential constraints and facilitate unidirectional information flow during the feature extraction phase. To enhance the accuracy of gesture recognition by comprehensively capturing intricate spatial-temporal features in sEMG signals, this paper introduces SGRN, a novel multi-branch spatial-temporal feature extraction network. SGRN integrates a meticulously crafted Spatial Feature Extraction Network (SNet), Temporal Feature Extraction Network (TNet), and Spatial-Temporal Feature Fusion Network (STNet), enabling comprehensive multi-dimensional feature extraction and seamless multi-branch information fusion. Distinct from prior spatial-temporal fusion methods, SGRN concurrently performs spatial feature extraction and temporal modeling, followed by multi-branch fusion, thereby harnessing the full potential of the multi-branch architecture to boost model performance. Extensive experiments on four datasets conclusively demonstrate SGRN's efficacy and superiority over state-of-the-art models, presenting a promising avenue for prosthetic control and muscle-computer interaction.