Adaptive threshold to compensate the effect of muscle fatigue on elbow-joint angle estimation based on electromyography

Triwiyanto .; O. Wahyunggoro1; H. A. Nugroho; Herianto .

doi:10.15282/jmes.12.3.2018.3.0334

Authors

Triwiyanto . Department of Electrical Engineering & Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
O. Wahyunggoro1 Department of Electrical Engineering & Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
H. A. Nugroho Department of Electrical Engineering & Information Technology, Universitas Gadjah Mada, Yogyakarta, Indonesia
Herianto . Department of Mechanical & Industrial Engineering Universitas Gadjah Mada, Yogyakarta, Indonesia

DOI:

https://doi.org/10.15282/jmes.12.3.2018.3.0334

Keywords:

Electromyography, feature extraction, elbow-joint angle estimation, muscle fatigue, low pass filter

Abstract

Muscle fatigue is a major problem in the study based on electromyography (EMG) signal. When the muscle in the fatigue condition, the power of the EMG signal increases significantly. This phenomenon can be a problem in the elbow-joint angle estimation. The purpose of this study is to develop a method in order to compensate the effect of the muscle fatigue on the elbow-joint angle estimation based on time domain features using EMG signal. The EMG signal was collected from biceps while the subjects performed a fatiguing motion of flexion and extension. The EMG was extracted using four time-domain features, namely zero crossing (ZC), sign slope change (SSC), Wilson amplitude (WAMP) and myopulse percentage rate (MYOP). The yielded features were filtered using second order Butterworth low pass filter. In the proposed method, to compensate the effect of the muscle fatigue, the RMS of the EMG signal was calculated for every cycle and used it as a threshold value of the features. The results show that the proposed method is able to compensate the effect of muscle fatigue with a consistent root mean square error (RMSE). The improvement of the performance ranges between 17.41% and 37.9% (for all adaptive features).

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