Prediction of biceps muscle fatigue and force using electromyography signal analysis for repeated isokinetic dumbbell curl exercise

Abstract

In this study, a new way to predict the muscle fatigue and force from Electromyography (EMG) signal for repeated isokinetic exercise is demonstrated. The relationship between cumulative biceps fatigue and EMG signal during repetitive dumbbell curl tasks with constant velocity was investigated with respect to Maximum voluntary contraction (MVC) levels (20 %, 35 %, 50 % and 75 % MVC). The mean integrated EMG and mean frequency per cycle were obtained from the time domain and frequency domain, respectively. The mean IEMG value and mean frequency values were co-plotted in the global EMG index map. Finally, we developed a new algorithm to predict muscle fatigue and force based on a global EMG index map employing mean IEMG and MNF values. The proposed algorithm based on a global EMG index map can be used to simultaneously predict muscle fatigue and force from real-time EMG signals with arbitrary MVC levels.