The progression of muscle fatigue during exercise estimation with the aid of high-frequency component parameters derived from ensemble empirical mode decomposition.

Abstract

Muscle fatigue is often monitored via the median frequency derived from the surface electromyography (sEMG) power spectrum during isometric contractions. The power spectrum of sEMG shifting toward lower frequencies can be used to quantify the electromanifestation of muscle fatigue. The dynamic sEMG belongs to a nonstationary signal, which will be affected by the electrode moving, the shift of the muscle, and the change of innervation zone. The goal of this study is to find a more sensitive and stable method in order to sense the progression of muscle fatigue in the local muscle during exercise in healthy people. Five male and five female volunteers participated. Each subject was asked to run on a multifunctional pedaled elliptical trainer for about 30 min, twice a week, and was recorded a total of six times. Three decomposed methods, discrete wavelet transform (DWT), empirical mode decomposition (EMD), and ensemble EMD (EEMD), were used to sense the progression of muscle fatigue. They compared with each other. Although the highest frequency components of sEMG by DWT, EMD, and EEMD have the better performance to sense the progression of muscle fatigue than the raw sEMG, the EEMD has the best performance to reduce nonstationary characteristics and noise of the dynamic sEMG.