A feature-based processing framework for real-time implementation of muscle fatigue measurement

Electromyographic signals (EMGs) are becoming important as a tool for muscle fatigue monitoring. EMGs measure the electric currents produced in muscle contractions providing information that can be analyzed and processed to evaluate the conditions of muscles. In this work, we proposed a real-time system that measures muscle fatigue levels based on Electromyographic signals. We used the Mean Frequency and the Power Spectral Density as features for muscle fatigue determination. A linear regression model determines the levels of muscle fatigue. Moreover, the system is composed of EMG wireless sensors allowing it to be used in common activities in the manufacturing industry as manual handling loads.