Surface electromyography (sEMG) feature extraction based on Daubechies wavelets

Author

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

Author_Institution

Dept. of Electr. & Electron. Eng., Univ. Technol. PETRONAS, Tronoh, Malaysia

fYear

2013

fDate

19-21 June 2013

Firstpage

1492

Lastpage

1495

Abstract

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.

Keywords

electromyography; feature extraction; medical signal detection; medical signal processing; signal reconstruction; statistical analysis; wavelet transforms; EMG signal processing; MAV features; RES index statistical measurement; classification accuracy improvement; feature class reparability evaluation; level-1; level-2; multilevel Daubechies wavelet reconstruction parameters; sEMG feature extraction; seventh-order Daubechies wavelet; surface electromyography feature extraction; wavelet transform feature extraction; Continuous wavelet transforms; Electromyography; Feature extraction; Indexes; Muscles; Daubechies wavelet; EMG; Electromyography signal; feature extraction;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics and Applications (ICIEA), 2013 8th IEEE Conference on

Conference_Location

Melbourne, VIC

Print_ISBN

978-1-4673-6320-4

Type

conf

DOI

10.1109/ICIEA.2013.6566603

Filename

6566603