DeepTPA-Net: A Deep Triple Attention Network for sEMG-based Hand Gesture Recognition

The use of hand gestures for human-computer interaction (HCI) has gained popularity due to its ability provide natural and intuitive communication in human dialogues. Hand gesture recognition (HGR) using surface electromyography (sEMG) signals is more reliable user-friendly than traditional computer vision-based methods. This study proposes a deep network named DeepTPA-Net that utilizes multi-channel sEMG recognize gestures. employs ResNet50 as an automated feature extractor novel triple attention (3Attn) block connects spatial, temporal, channel modules parallel signify important features HGR. We evaluated the performance five publicly available benchmark datasets, including CapgMyo DB-a, Csl-hdemg, NinaPro DB1, DB2, SeNic. effectiveness proposed 3Attn HGR demonstrated through comparison with other mechanisms. compared various baseline models, variations existing results show significantly outperforms models all indicating superiority sEMG-based