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

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

Blog Article

The use of hand gestures for human-computer interaction (HCI) has gained popularity due to its ability to provide natural and intuitive communication in human dialogues.Hand gesture recognition (HGR) using surface electromyography (sEMG) signals is more reliable and user-friendly sophie allport bee curtains than traditional computer vision-based methods.This study proposes a deep network named DeepTPA-Net that utilizes multi-channel sEMG signals to recognize hand gestures.DeepTPA-Net employs a ResNet50 network as an automated feature extractor and a novel triple attention (3Attn) block that connects spatial, temporal, and channel attention modules in parallel to signify important features for HGR.

We evaluated the performance of DeepTPA-Net using five publicly available benchmark sEMG hand gesture datasets, including CapgMyo DB-a, Csl-hdemg, NinaPro DB1, NinaPro DB2, and SeNic.The effectiveness of the proposed 3Attn block for HGR is demonstrated through a performance comparison with other attention mechanisms.We compared the performance of DeepTPA-Net majicontrast red with various baseline models, including its variations and other existing methods.The results show that DeepTPA-Net significantly outperforms the baseline models for all five benchmark datasets, indicating the superiority of DeepTPA-Net for sEMG-based HGR.