A Computational System for Real-Time Muscle Fatigue Monitoring Using Synthetic EMG Signals from the Gastrocnemius

Early detection of muscle fatigue is crucial in fields such as athletic performance, physical rehabilitation, and occupational health. This study describes an advanced synthetic electromyographic (EMG) signal generator that simulates the progressive recruitment of motor units for real-time muscle fatigue monitoring, with specific focus on the gastrocnemius muscle. The system implements a controlled simulation that typically initiates with 500 active motor units, allowing dynamic adjustment according to force and fatigue levels, which is intended to reflect neuromuscular adaptation during sustained contractions. To detect fatigue, dynamic thresholds based on the root mean square (RMS) and median frequency (MDF) of synthetic EMG signals were applied. These thresholds are continuously updated by considering a historical baseline and simulated physiological conditions. A progressive decrease in median frequency and a corresponding increase in RMS amplitude were observed, consistent with established neuromuscular responses under fatigue conditions. EMG signal visualisation facilitated the interpretation of the fatigue process and the compensatory recruitment of additional motor units. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1199Dimensions.Open Alex.