A hybrid sEMG-FMG sensor fusion approach under muscle fatigue using cascade fuzzy forest

Muscle-driven Human-Machine Systems (HMSs) have advanced significantly in recent years, yet practical applications are often challenged by issues like muscle fatigue. This study introduces an innovative system that captures both surface electromyographic signals (sEMG) and muscle Force Myography (FMG) signals from the human arm simultaneously. The sEMG signals provide information about the electrical activity of muscle contractions, while the FMG signals monitor morphological changes in the muscles, all in a non-invasive manner. We developed a portable, wearable hybrid sEMG-FMG acquisition system, consisting of a signal acquisition module and an armband to gather both types of signals from the same skin area. Using this system, we examined the sensitivity of sEMG and FMG signals to muscle fatigue and assessed whether combining these two signals could mitigate the effects of muscle fatigue on gesture recognition accuracy. The sEMG and FMG signals were recorded during various hand gestures under both fatigued and non-fatigued conditions. Additionally, a cascade fuzzy forest (CFF) algorithm was developed to enhance hand motion recognition accuracy using the combined sEMG-FMG signals. Experimental results show that FMG signals are resilient to muscle fatigue, and the integration of sEMG with FMG signals significantly reduces the adverse effects of muscle fatigue. The CFF algorithm further improves recognition accuracy, demonstrating the effectiveness of the proposed approach.