| Penulis/Author |
TRIWIYANTO (1) ; Prof. Ir. Oyas Wahyunggoro, MT., Ph.D. (2); Prof. Ir. Hanung Adi Nugroho, S.T., M.Eng., Ph.D., IPM., SMIEEE. (3); Prof. Dr.Eng. Ir. Herianto, S.T., M.Eng., IPU., ASEAN Eng. (4) |
| Abstrak/Abstract |
Fatigue is a state that the muscle could not able to maintain the contraction. Electromyography signals can be used to determine the state of muscle fatigue. Quantization of muscle fatigue needs to be defined clearly so that it can be used as an indicator or a compensator in the control system. Electromyography signal which is produced during dynamic motion during muscle fatigue assessment is a non-stationary signal. In this study, the discrete wavelet transforms method was used to analyze electromyography signals. The electromyography signal was decomposed up to 5th scale. Features extraction, root mean square, mean average, standard deviation and power signal were used to observe the correlation coefficient and the slope of the regression line. Our finding from the results of the analysis with multi-resolution discrete wavelet transforms showed that the frequency ranged 62.5 Hz up to 125 Hz was the dominant frequency of the EMG signal and feature of power was better among other features to observe the muscle fatigue. In this study, the correlation coefficient was 0.852 and the best slope was 0.728 mV/s. These value can also be used as an indicator or an estimator of the state of the fatigue and used as a compensator in the system based myoelectric control
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