A fatigue assessment method based on attention mechanism and surface electromyography

Abstract

Surface electromyography (sEMG) signals can be used to quantitatively assess muscle fatigue, thereby directly and objectively reflecting the functional state of neuromuscular activity. Effective fatigue diagnosis can prevent muscle damage, thereby improving the safety of rehabilitation exercise. Traditional fatigue diagnosis has certain limitations, including strong subjectivity and poor accuracy. This paper designs a sEMG signals acquisition circuit and collects the sEMG signals of the upper limb biceps brachii and triceps brachii in the force-relaxation state in a dual-channel form. Muscle fatigue classification assessment using Dynamic Time Warping-K Nearest Neighbor (DTW-KNN) and three deep learning algorithms. The experimental results show that compared with traditional machine learning algorithms, deep learning algorithm can achieve higher accuracy and time efficiency. In addition, this study introduces an attention mechanism to dynamically and reasonably assign network weights to achieve high level feature learning. The Attention-Long Short-Term Memory (Attention Based LSTM) neural network achieves 93.5% assessment accuracy with a time overhead of only 3.73s, allowing for real-time assessment of muscle fatigue.

Keywords

• Attention mechanism
• Surface electromyography
• Long short-term memory
• Muscle fatigue
• Deep learning