Discrete wavelet transform analysis of surface electromyography for the fatigue assessment of neck and shoulder muscles

Suman Kanti Chowdhury, Ashish D. Nimbarte, Majid Jaridi, Robert C. Creese

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


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.

Original languageEnglish
Pages (from-to)995-1003
Number of pages9
JournalJournal of Electromyography and Kinesiology
Issue number5
StatePublished - Oct 2013


  • Discrete wavelet transform
  • Dynamic repetitive exertions
  • Fatigue
  • Musculoskeletal disorders
  • Neck
  • Shoulder


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