A Frequency-Based Attention Neural Network and Subject-Adaptive Transfer Learning for sEMG Hand Gesture Classification

This study introduces a novel approach for real-time hand gesture classification through the integration of a Frequency-based Attention Neural Network (FANN) with Subject-Adaptive Transfer Learning, specifically tailored for surface electromyography (sEMG) data. By utilizing the Fourier transform, the proposed methodology leverages the inherent frequency characteristics of sEMG signals to enhance the discriminative features for accurate gesture recognition. Additionally, the subject-adaptive transfer learning strategy is employed to improve model generalization across different individuals. The combination of these techniques results in an effective and versatile system for sEMG-based hand gesture classification, demonstrating promising performance in adapting individual variability and improving classification accuracy. The proposed method's performance is evaluated and compared with established approaches using the publicity available NinaPro DB5 dataset. Notably, the proposed simple model, coupled with frequency-based attention modules, achieves accuracies of 89.56% with a quick prediction time of 5ms, showcasing its potential for dexterous control of robots and bionic hands. The findings of this research contribute to the advancement of gesture recognition systems, particularly in the domains of human-computer interaction and prosthetic control.