Comparative study of myoelectric pattern recognition based on wavelet analysis Online publication date: Sat, 25-Apr-2015
by Firas Al Omari; Guohai Liu; Congli Mei; Hui Jiang
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 16, No. 1, 2014
Abstract: The choice of an appropriate 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 using six wavelet functions and employing two different recognition methods. The selected classifiers are the support vector machine (SVM) and probabilistic regression neural network (PNN). The selected wavelet families used to decompose the recorded surface electromyographic (sEMG) signals are Biorthogonal (bior), Coiflet (coif), Daubechies (db), and Symmlet (sym). 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 classification rate achieved was 96%, which was accomplished using the PNN classifier based on coif4 at the sixth decomposition level.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook