All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.


Comparative study of myoelectric pattern recognition using SVM and PNN classifiers based on wavelet analysis

Author(s): Firas AlOmari, Guohai Liu

The choice of a proper wavelet familywith a fast and robust classifier is an important step in the construction of a myoelectric control pattern recognition system for a prosthetic hand. In this study, five hand motions were classified by using sixwavelet functions extracted features fromsEMG signals. The selected wavelet families that were used to decompose the recorded sEMG signals are Biorthogonal (bior). Coiflet (coif), Daubechies (db), and Symmlet (sym). Two different recognitionmethodswere employed for classification procedure: support vector machine (SVM), probabilistic regression neural network (PNN). The results of our experiment demonstrate that the use of wavelet families at a high decomposition level increases the recognition rate of hand motions. The highest achieved classification rate was 96%, by using the PNN classifier based on coif4 at the sixth decomposition level.

Share this       

Share this Page.

Table of Contents

Scimago Journal Rank

izmir escort izmir escort bursa escort antalya escort izmir escort porno porno izle türk porno eskişehir escort bartın escort burdur escort izmir escort bursa escort porno indir izle escort izmir