Assessment of Muscle Fatigue Progression Based on Surface Electromyograph Sensor: A Pilot Study

Abstract

Surface muscle fatigue (MF) is an important area to study especially in medicine and sport. One way to detect and process muscle fatigue is by the use of surface electromyography (sEMG). This study presents a real-time sEMG signal acquisition and processing system designed to detect muscle fatigue during. The study included four participants that performed isometric contractions until muscle fatigue was reached, during which sEMG signals were continually monitored. The acquired sEMG data underwent systematic processing, including filtering, rectification, and feature extraction. Four features were extracted: Root Mean Square (RMS), Mean Absolute Value (MAV), Mean Frequency (MNF), and Median Frequency (MDF). The results show that MNF is the clearest indicator of fatigue in the suggested system. Moreover, RMS and MAV can be helpful in indicating the early signs of fatigue. The selected method is useful for real-time muscle fatigue monitoring without the need for complex algorithms. These results offer a basis for the next research focusing on enhancing real-time sEMG signal processing techniques using industrial sensors.