EMG-TransNN-MHA: A Transformer-Based Model for Enhanced Motor Intent Recognition in Assistive Robotics

Abstract

In recent years, electromyography (EMG) has become a crucial tool in developing human-machine interfaces (HMIs) and assistive robotics by enabling intent recognition through muscle activity analysis. Analyzing EMG data allows us to develop control algorithms that respond to human intent, enabling context-aware responses and complex interactions. However, accurately classifying motor intent through EMG signals presents challenges. Hence, this study introduces EMG-TransNN-MHA, a transformer-based model to tackle the challenges and accurately classify EMG signals. While classifying the EMG signals, EMG-TransNN-MHA achieved an average training accuracy of 96.88% and a test accuracy of 96.39%, outperforming traditional deep learning and machine learning models. Implementation details and code are available at https://github.com/madibabaiasl/EMGIntentPaper.