Surface electromyography (sEMG) feature extraction based on Daubechies wavelets

I. Elamvazuthi, G. A. Ling, K. A.R.Ku Nurhanim, P. Vasant, S. Parasuraman

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

17 Citations (Scopus)


Wavelet transform feature extraction has become one of the most powerful techniques to improve the classification accuracy. In this paper, we are investigating the multi-level Daubechies wavelet reconstruction parameters. The EMG signal after performing the Daubechies wavelet was further processed by using one of the most successful features which is MAV. RES index statistical measurement was used to evaluate the class reparability of the features. The optimal results are obtained by using the seventh order of Daubechies with the level 1 and level 2 details components after performing wavelet reconstruction.

Original languageEnglish
Title of host publicationProceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013
Number of pages4
Publication statusPublished - 2013
EventIEEE Conference on Industrial Electronics and Applications 2013 - Melbourne, Australia
Duration: 19 Jun 201321 Jun 2013
Conference number: 8th (Proceedings)


ConferenceIEEE Conference on Industrial Electronics and Applications 2013
Abbreviated titleICIEA 2013
Internet address


  • Daubechies wavelet
  • Electromyography signal
  • EMG
  • feature extraction

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