Feature extraction of wavelet transform for sEMG pattern classification

Author

Tepe, C. ; Eminoglu, I. ; Senyer, Nurettin

Author_Institution

Elektrik ve Elektron. Muhendisligi Bolumu, Ondokuzmayis Univ., Samsun, Turkey

fYear

2014

fDate

23-25 April 2014

Firstpage

1098

Lastpage

1101

Abstract

In this study, we have investigated usefulness of extraction of the surface electromiyogram (sEMG) features from multi-level wavelet decomposition of the yEMG signal. The first step of this method is to analyze sEMG signal detected from the subject´s right upper forearm and extract features using the mean absolute value (MAV), MAV of wavelet approximation and details coefficients, MAV of wavelet approximation and details of sEMG which is calculated Inverse Wavelet Transform. The second step is to import the feature values into an ANN to identify the speed of hand open-çlose (SHOC). Finally, based on the results of experiments, feature vectors obtained by wavelet transform is effective in prediction of SHOC.

Keywords

electromyography; feature extraction; medical signal processing; neural nets; pattern classification; wavelet transforms; ANN; MAV; SHOC; details coefficients; feature vectors; inverse wavelet transform; mean absolute value; multilevel wavelet decomposition; right upper forearm; sEMG features extraction; sEMG pattern classification; speed of hand open-çlose; surface electromiyogram features extraction; wavelet approximation; yEMG signal; Conferences; Electromyography; Feature extraction; Signal processing; Wavelet analysis; Wavelet transforms; estimate of hand speed; neural networks; sEMG; wavelet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2014 22nd

Conference_Location

Trabzon

Type

conf

DOI

10.1109/SIU.2014.6830425

Filename

6830425