TY - JOUR
T1 - Discrete wavelet transform analysis of surface electromyography for the fatigue assessment of neck and shoulder muscles
AU - Chowdhury, Suman Kanti
AU - Nimbarte, Ashish D.
AU - Jaridi, Majid
AU - Creese, Robert C.
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013/10
Y1 - 2013/10
N2 - Assessment of neuromuscular fatigue is essential for early detection and prevention of risks associated with work-related musculoskeletal disorders. In recent years, discrete wavelet transform (DWT) of surface electromyography (SEMG) has been used to evaluate muscle fatigue, especially during dynamic contractions when the SEMG signal is non-stationary. However, its application to the assessment of work-related neck and shoulder muscle fatigue 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 under dynamic repetitive conditions. Ten human participants performed 40. min of fatiguing repetitive arm and neck exertions while SEMG data from the upper trapezius and sternocleidomastoid muscles were recorded. The ten of the most commonly used wavelet functions were used to conduct the DWT analysis. Spectral changes estimated using power of wavelet coefficients in the 12-23. Hz frequency band showed the highest sensitivity to fatigue induced by the dynamic repetitive exertions. Although most of the wavelet functions tested in this study reasonably demonstrated the expected power trend with fatigue development and recovery, the overall performance of the "Rbio3.1" wavelet in terms of power estimation and statistical significance was better than the remaining nine wavelets.
AB - Assessment of neuromuscular fatigue is essential for early detection and prevention of risks associated with work-related musculoskeletal disorders. In recent years, discrete wavelet transform (DWT) of surface electromyography (SEMG) has been used to evaluate muscle fatigue, especially during dynamic contractions when the SEMG signal is non-stationary. However, its application to the assessment of work-related neck and shoulder muscle fatigue 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 under dynamic repetitive conditions. Ten human participants performed 40. min of fatiguing repetitive arm and neck exertions while SEMG data from the upper trapezius and sternocleidomastoid muscles were recorded. The ten of the most commonly used wavelet functions were used to conduct the DWT analysis. Spectral changes estimated using power of wavelet coefficients in the 12-23. Hz frequency band showed the highest sensitivity to fatigue induced by the dynamic repetitive exertions. Although most of the wavelet functions tested in this study reasonably demonstrated the expected power trend with fatigue development and recovery, the overall performance of the "Rbio3.1" wavelet in terms of power estimation and statistical significance was better than the remaining nine wavelets.
KW - Discrete wavelet transform
KW - Dynamic repetitive exertions
KW - Fatigue
KW - Musculoskeletal disorders
KW - Neck
KW - Shoulder
UR - http://www.scopus.com/inward/record.url?scp=84882928458&partnerID=8YFLogxK
U2 - 10.1016/j.jelekin.2013.05.001
DO - 10.1016/j.jelekin.2013.05.001
M3 - Article
C2 - 23787059
AN - SCOPUS:84882928458
VL - 23
SP - 995
EP - 1003
JO - Journal of Electromyography and Kinesiology
JF - Journal of Electromyography and Kinesiology
SN - 1050-6411
IS - 5
ER -