An Arduino-Based EMG and Motion Sensor System for Affordable Muscle Fatigue Monitoring

Muscle fatigue, characterized by a decline in muscle performance following sustained activity, affects not only athletic performance but also daily function and workplace productivity. Monitoring fatigue is essential for preventing injury, optimizing rehabilitation, and improving overall physical efficiency. However existing muscle fatigue biosensors are expensive or too complex, limiting accessibility. This project presents a low-cost Arduino-based biosensor designed to monitor muscle fatigue through surface electromyography (EMG) and motion sensing using an MPU9250 accelerometer. The system records electrical and mechanical activity from various muscle groups and applies Kalman and FFT-based filtering to remove noise and isolate meaningful fatigue indicators. Results demonstrated declines in EMG frequency data shown through the power spectrum and increases in tremor amplitude correlating with fatigue onset. These findings validate the Arduino-based biosensor as a practical and affordable alternative to commercial systems for muscle fatigue monitoring. Future developments could incorporate real-time feedback and predictive fatigue modeling using machine-learning algorithms, further enhancing accessibility and functionality.