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.
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Recommended by Associate Editor Won Gu Lee
Hyun-Jun Hwang received his B.S. degree in Mechanical Engineering from Hanyang University in 2012.
Hak-Sung Kim received his B.S. degree in mechanical engineering from Korea Advanced Institute of Science and Technology in 2001 and his M.S. degree from Korea Advanced Institute of Science and Technology in 2003. He received his Ph.D. degree in mechanical engineering from Korea Advanced Institute of Science and Technology in 2006.
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Hwang, HJ., Chung, WH., Song, JH. et al. Prediction of biceps muscle fatigue and force using electromyography signal analysis for repeated isokinetic dumbbell curl exercise. J Mech Sci Technol 30, 5329–5336 (2016). https://doi.org/10.1007/s12206-016-1053-1
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DOI: https://doi.org/10.1007/s12206-016-1053-1