Assessment of neck and shoulder muscle fatigue using discrete wavelet transforms of surface electromyography

Abstract

Objective assessment of neuromuscular fatigue is important for the early detection and prevention of risks of work-related musculoskeletal disorders (MSDs). Although, in recent years, a number of researchers have used discrete wavelet transforms (DWT) of surface electromyography (SEMG) to evaluate muscle fatigue, its application to neck and shoulder muscle fatigue assessment is not well established. Therefore, the purpose of this study was to establish DWT analysis as a suitable method to conduct quantitative assessment of neck and shoulder muscle fatigue caused by dynamic exertions. Six human participants performed 40 minutes of fatiguing repetitive arm and neck exertions. SEMG data from the right upper trapezius and left sternocleidomastoid muscles were recorded. Six most commonly used orthogonal wavelet functions were used to conduct DWT analysis. With the development of fatigue for most of the wavelet functions a significant increase in the power in the lower frequency bands of 12-23Hz and 23-46Hz was observed. Bior 3.1 wavelet showed the highest power, statistically consistent and meaningful power trend and the highest overall power contrast compared to the remaining five wavelets tested in this study.