<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>Nowadays, analysis of electromyography (EMG) signal using wavelet transform is one of the most powerful signal processing tools. It is widely used in the EMG recognition system. In this study, we have investigated usefulness of extraction of the EMG features from multiple-level wavelet decomposition of the EMG signal. Different levels of various mother wavelets were used to obtain the useful resolution components from the EMG signal. Optimal EMG resolution component (sub-signal) was selected and then the reconstruction of the useful information signal was done. Noise and unwanted EMG parts were eliminated throughout this process. The estimated EMG signal that is an effective EMG part was extracted with the popular features, i.e. mean absolute value and root mean square, in order to improve quality of class separability. Two criteria used in the evaluation are the ratio of a Euclidean distance to a standard deviation and the scatter graph. The results show that only the EMG features extracted from reconstructed EMG signals of the first-level and the second-level detail coefficients yield the improvement of class separability in feature space. It will ensure that the result of pattern classification accuracy will be as high as possible. Optimal wavelet decomposition is obtained using the seventh order of Daubechies wavelet and the forth-level wavelet decomposition.</p></abstract>

Nowadays, analysis of electromyography (EMG) signal using wavelet transform is one of the most powerful signal processing tools. It is widely used in the EMG recognition system. In this study, we have investigated usefulness of extraction of the EMG features from multiple-level wavelet decomposition of the EMG signal. Different levels of various mother wavelets were used to obtain the useful resolution components from the EMG signal. Optimal EMG resolution component (sub-signal) was selected and then the reconstruction of the useful information signal was done. Noise and unwanted EMG parts were eliminated throughout this process. The estimated EMG signal that is an effective EMG part was extracted with the popular features, i.e. mean absolute value and root mean square, in order to improve quality of class separability. Two criteria used in the evaluation are the ratio of a Euclidean distance to a standard deviation and the scatter graph. The results show that only the EMG features extracted from reconstructed EMG signals of the first-level and the second-level detail coefficients yield the improvement of class separability in feature space. It will ensure that the result of pattern classification accuracy will be as high as possible. Optimal wavelet decomposition is obtained using the seventh order of Daubechies wavelet and the forth-level wavelet decomposition.

Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification

Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, 15 Kanjanavanich Road, Kho Hong, Hat Yai, 90112, Songkhla, Thailand

Measurement Science Review

Nowadays, analysis of electromyography (EMG) signal using wavelet transform is one of the most powerful signal processing tools. It is widely used in the EMG...

Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification

Publié en ligne: 03 juin 2011

Pages: 45 - 52

DOI: https://doi.org/10.2478/v10048-011-0009-y

Mots clés
Electromyography signal, EMG, feature extraction, wavelet transform, mean absolute value, root mean square, multi-resolution analysis

This content is open access.

Application of Wavelet Analysis in EMG Feature Extraction for Pattern Classification

Publié en ligne: 03 juin 2011

Pages: 45 - 52

DOI: https://doi.org/10.2478/v10048-011-0009-y

Mots clésElectromyography signal, EMG, feature extraction, wavelet transform, mean absolute value, root mean square, multi-resolution analysis

This content is open access.

Mots clés
Electromyography signal, EMG, feature extraction, wavelet transform, mean absolute value, root mean square, multi-resolution analysis