Abstract
Athletes often hire personal trainers (PTs) for strength training, where training to volitional fatigue maximizes results but increases injury risk. This study proposes a volitional fatigue monitoring system to assist PTs in preventing fatigue-induced injuries. The system utilizes electromyography (EMG) with features derived from RMS and IEMG signals, with Random Forest classification method. Outputs are displayed on an OLED screen, LED lights, and a website via the WebSocket Protocol. EMG signal disturbances were mitigated with a filter, a battery, and a sport band. Testing involved five subjects aged 20-22 with various arm strength and no exercise background. The test results show that the EMG sensor acquires data within the appropriate range. The system achieved a 91.43% accuracy in muscle fatigue detection and a 1.0284 second average computation time, and produced the expected outputs with 100% accuracy. Therefore, the proposed monitoring system is feasible and reliable for volitional fatigue monitoring.