Adaptive Frequency Evolution Decomposition Combined With Improved Fluctuation-Based Dispersion Entropy for Muscle Fatigue Characterization

Muscle fatigue has a major negative impact on human bodily health. Surface electromyography (sEMG) is widely used for fatigue assessment because of its non-invasive nature. The current methods of signal decomposition are limited in extracting fatigue signals. Methods for feature extraction often processes leak information and lack sensitivity, making it difficult to evaluate fatigue. We propose a method to assess muscle fatigue using the combination of adaptive frequency evolution decomposition (AFED) and fractional fuzzy fluctuation-based dispersion entropy (F3DE). AFED dynamically adjusts bandwidth to track spectral changes, extracting fatigue signals from sEMG data. F3DE is a fluctuation-based dispersion entropy that utilizes fuzzy functions and fractional order to improve sensitivity and robustness in the characterization of fatigue. Based on data collected from various subjects adapting isometric contractions, we see that AFED can extract fatigue signals. In the feature fitting results, extracted features show improvements in both the slope andR² compared to the original sEMG signals. F3DE demonstrates better accuracy and stability in capturing individual fatigue processes. It outperforms traditional features in fatigue recognition. In classification, the AFED and F3DE model achieved 97.5% accuracy and excellent performance in all metrics. In conclusion, the combination of AFED and F3DE improves the accuracy and robustness of muscle fatigue detection.