Muscle fatigue compensation of the electromyography signal for elbow joint angle estimation using adaptive feature. (October 2018)

• Record Type: Journal Article
• Authors: Triwiyanto, Triwiyanto; Wahyunggoro, Oyas; Nugroho, H.A.; Herianto, Herianto
• Is Part Of: Computers & electrical engineering. Volume 71(2018)
• Page Start: 284
• Page End: 293
• Publication Date: 2018-10
• Subjects: EMG -- WAMP -- Kalman filter -- Elbow joint angle estimation -- Muscle fatigue; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.07.026
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The purpose of this study is to develop and evaluate an adaptive feature to compensate the effect of muscle fatigue in the elbow joint angle estimation. In the experiment protocol, subjects are asked to move the elbow in flexion and extension for 15 min. The electromyography (EMG) signal collected from biceps was extracted using the Wilson amplitude (WAMP) and modified WAMP features. A modified WAMP feature consists of the standard WAMP feature and an adaptive threshold; to smooth the features, the Kalman filter was applied. The results prove that muscle fatigue affects the EMG signal. The RMSEs resulting from the standard WAMP feature in the non-fatigue and fatigue conditions are 12.41° ± 5.16° and 18.17° ± 5.53°, respectively. The RMSEs resulting from modified WAMP feature in the non-fatigue and fatigue conditions are 12.63° ± 5.22° and 13.86° ± 4.7°, respectively. This proposed method is able to compensate the effect of muscle fatigue on the elbow joint angle estimation.