Comparative study of myoelectric pattern recognition using SVM and PNN classifiers based on wavelet analysis
نویسندگان
چکیده
The choice of a proper wavelet family with 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 six wavelet functions extracted features from sEMG 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 recognition methods were 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. 2015 Trade Science Inc. INDIA
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