Non-invasive detection of low-level muscle fatigue using surface EMG with wavelet decomposition.

Abstract

Median frequency (MDF) is widely used for detection and tracking of muscle fatigue using surface electromyography (sEMG). However, MDF does not behave consistently or accurately distinguish fatigued from non-fatigued states. In this paper, we study the concept of low-level fatigue and propose increasing average ratio (IAR) and trigger pattern index (TPI) based on discrete wavelet transform (DWT) for distinguishing low-level muscle fatigue. We recorded sEMG using an 8-electode linear monopolar array during isometric contractions from brachioradialis (BRD), biceps brachii long head (BBL), and biceps brachii short head (BBS) muscles of different subjects when performing force exertion. We then calculated the proposed parameters for characterizing low-level fatigue. The analysis indicated that the proposed approach is more consistent and stable when distinguishing low-level muscle fatigue and sheds light on the behavior of sEMG in frequency domain with respect to low-fatigue force exertion.