title: Muscle fatigue monitoring using wavelet decomposition of surface EMG.
abstract: Monitoring the local muscle fatigue in maximum voluntary isometric contraction is successful using median frequency and mean power frequency of the surface EMG. However, it has very limited success in maximum voluntary dynamic contraction. We showed that the relaxation electromechanical delay was a reliable fatigue indicator during the maximum voluntary isokinetic concentric contraction. In this paper, we use wavelet decomposition to generate fatigue indicators including contraction delay (WCD), relaxation delay (WRD), and the power ratio related to high frequency and low frequency regions (HF/LF) of the power spectrum of surface electromyogram (SEMG). Eight subjects were asked to perform right knee isokinetic contractions at two angular velocities (120 and 240 degrees/sec). Total work (TW), knee angle, and four channels of quadriceps SEMGs were sampled simultaneously. The results indicate that the initial slopes of WCD and WRD increase and are correlated with TW significantly, the initial slope of HF/LF declines and is correlated with TW significantly, and the WRD is more stable than the WCD. It concludes that wavelet decomposition enhances the features of surface EMG, and that power ratio (HF/LF) and contraction and relaxation delays obtained with the EMG wavelet decomposition can be used to monitor the muscle fatigue during the maximum voluntary isokinetic concentric contraction.
authors.affiliation: Rehabilitation Engineering Centre, Hong Kong Polytechnic University, Kowloon.
authors.forename: S
authors.initials: S
authors.lastname: Xiao
authors.forename: S C
authors.initials: SC
authors.lastname: Leung
mesh_terms: D004576:Electromyography; D006801:Humans; D007866:Leg; D009119:Muscle Contraction; D018763:Muscle Fatigue / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology; D012815:Signal Processing, Computer-Assisted