A Hybrid Model for Muscle Fatigue Detection: Integrating Time and Frequency Domain Features of sEMG Signals With RPE Scale During Isometric Contraction

Muscle fatigue can result in decreased athletic performance and even muscle damage. Therefore, a deeper understanding of muscle fatigue is essential for enhancing athletic performance and injury prevention. This study collected surface electromyography (sEMG) signals from 40 participants, and their fatigue levels were quantified using the Borg rating of perceived exertion (RPE) scale. Subsequently, fatigue identification was performed using the model proposed in this study. The model leverages features extracted from both the time and frequency domains of sEMG signals. The time domain branch primarily consists of temporal convolutional networks (TCNs) and self-attention mechanisms, while the frequency domain branch comprises a series of 1-D convolutional neural networks (CNNs). Finally, the proposed model was employed to classify the collected sEMG signals, with results achieving an accuracy of 90.07%.