Muscle fatigue compensation of the electromyography signal for elbow joint angle estimation using adaptive feature

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.

[1]  Oyas Wahyunggoro,et al.  An Investigation Into Time Domain Features of Surface Electromyography to Estimate the Elbow Joint Angle , 2017 .

[2]  Andrew J. Pullan,et al.  Neuromuscular Interfacing: Establishing an EMG-Driven Model for the Human Elbow Joint , 2012, IEEE Transactions on Biomedical Engineering.

[3]  Huosheng Hu,et al.  Myoelectric control systems - A survey , 2007, Biomed. Signal Process. Control..

[4]  Youngjin Na,et al.  Dynamic Elbow Flexion Force Estimation Through a Muscle Twitch Model and sEMG in a Fatigue Condition , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  Peng Shi,et al.  Deadbeat Dissipative FIR Filtering , 2016, IEEE Transactions on Circuits and Systems I: Regular Papers.

[6]  Fuchun Sun,et al.  sEMG-Based Joint Force Control for an Upper-Limb Power-Assist Exoskeleton Robot , 2014, IEEE Journal of Biomedical and Health Informatics.

[7]  Martin Colley,et al.  sEMG Techniques to Detect and Predict Localised Muscle Fatigue , 2012 .

[8]  S.G. Meek,et al.  Fatigue compensation of the electromyographic signal for prosthetic control and force estimation , 1993, IEEE Transactions on Biomedical Engineering.

[9]  Continuous Wavelet Transform Analysis of Surface Electromyography for Muscle Fatigue Assessment on the Elbow Joint Motion , 2017 .

[10]  D Stashuk,et al.  EMG signal decomposition: how can it be accomplished and used? , 2001, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[11]  T. Fukuda,et al.  Root Mean Square Value of the Electromyographic Signal in the Isometric Torque of the Quadriceps, Hamstrings and Brachial Biceps Muscles in Female Subjects , 2010 .

[12]  Oyas Wahyunggoro,et al.  Evaluating the performance of Kalman filter on elbow joint angle prediction based on electromyography , 2017 .

[13]  Nicola Vitiello,et al.  Intention-Based EMG Control for Powered Exoskeletons , 2012, IEEE Transactions on Biomedical Engineering.

[14]  Kejun Zhang,et al.  An Upper-Limb Power-Assist Exoskeleton Using Proportional Myoelectric Control , 2014, Sensors.

[15]  Toufik Bakir,et al.  Estimation of Muscular Fatigue Under Electromyostimulation Using CWT , 2012, IEEE Transactions on Biomedical Engineering.

[16]  A Malanda,et al.  EMG spectral indices and muscle power fatigue during dynamic contractions. , 2010, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[17]  Peng Shi,et al.  Fusion Kalman/UFIR Filter for State Estimation With Uncertain Parameters and Noise Statistics , 2017, IEEE Transactions on Industrial Electronics.

[18]  Youngjin Choi,et al.  Human elbow joint angle estimation using electromyogram signal processing , 2011 .

[19]  Kazuo Kiguchi,et al.  Evaluation of Fuzzy-Neuro Modifiers for Compensation of the Effects of Muscle Fatigue on EMG-Based Control to be Used in Upper-Limb Power-Assist Exoskeletons , 2013 .

[20]  W Laurig,et al.  Electromyographical study on surgeons in urology. II. Determination of muscular fatigue. , 1996, Ergonomics.

[21]  P. A. Karthick,et al.  Surface electromyography based muscle fatigue progression analysis using modified B distribution time-frequency features , 2016, Biomed. Signal Process. Control..