Developing a Novel Muscle Fatigue Index for Wireless sEMG Sensors: Metrics and Regression Models for Real-Time Monitoring

Muscle fatigue impacts performance in sports, rehabilitation, and daily activities, with surface electromyography (sEMG) widely used for monitoring. In this study, we developed a wearable sEMG device and conducted experiments to create a dataset for fatigue analysis. The sEMG signals were analyzed through a multi-domain feature extraction pipeline, incorporating time-domain (e.g., RMS, ARV), frequency-domain (e.g., MNF), and hybrid-domain metrics (e.g., MNF/ARV ratio, Instantaneous Mean Amplitude Difference), to identify physiologically meaningful indicators of fatigue. To ensure inter-subject comparability, we applied a dynamic standardization strategy that normalized each feature based on the RMS value of the first active segment, establishing a consistent baseline across participants. Using these standardized features, we explored several fatigue index construction methods—such as weighted sums, t-SNE, and Principal Component Analysis (PCA)—to capture fatigue progression effectively. We then trained and evaluated multiple machine learning models such as LR, SVR, RF, GBM, LSTM, CNN, and kNN to predict fatigue levels, selecting the most effective approach for real-time monitoring. Integrated into a wireless BLE-enabled sensor platform, the system offers seamless body placement, mobility, and efficient data transmission. An initial calibration phase ensures adaptation to individual muscle profiles, enhancing accuracy. By balancing on-device processing with efficient wireless communication, this platform delivers scalable, real-time fatigue monitoring across diverse applications.