DETECTION OF MUSCLE FATIGUE UNDER ISOMETRIC CONTRACTIONS USING SLOPE ENTROPY OF SURFACE ELECTROMYOGRAPHY SIGNALS

Muscle fatigue is a neuromuscular condition characterized by a decline in the force-generating capacity of skeletal muscles. Continuous monitoring of this condition plays a crucial role in fields such as sports science, human–machine interface, and ergonomics. Surface electromyography (sEMG) is a widely used non-invasive method for evaluating fatigue conditions. However, the random fluctuations in amplitude and the indeterministic nature of sEMG pose significant challenges for analysis. In this study, an analysis based on slope entropy (SlopEn) is proposed to differentiate the non-fatigue and fatigue conditions under isometric contractions. For this purpose, sEMG signals are acquired from 90 volunteers while they perform an isometric fatiguing task with a 3[Formula: see text]kg load in their dominant hand. The first and final two seconds of sEMG are considered to be non-fatigue and fatigue conditions, respectively, and are pre-processed to remove noise. These signals are then subjected to SlopEn to quantify the complexity associated with random fluctuations in the sEMG of muscle contractions. The results show that SlopEn is higher in the fatigue condition, and it is distinct between the two conditions ([Formula: see text]). Further, SlopEn is found to be superior to Shannon (SE) and Sample entropy (SampEn). The XGBoost achieved an F1 score of 84.1% using both SlopEn and SE. It appears that slope entropy-based models have the potential to detect the fatigue mechanisms in real-time applications.