By Topic

Classification of EMG signals using wavelet based autoregressive models and neural networks to control prothesis-bionic hand

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Yazici, I. ; Elektron. Muhendisligi Bolumu, Sakarya Univ., Sakarya ; Köklükaya, E. ; Baslo, B.

This work has aimed to contribute to the prothesis-bionic hand studies. Four hundred eighty signals used in this work correspond to position of adduction motion of thumb, flexion motion of thumb, abduction motion of fingers were collected by surface electrodes. Eight healthy has participated for collecting by surface electromyogram (SEMG). The wavelet based autoregressive models of collected signals are used as feature vector for artificial neural networks. Feed forward and back propagation network, radial basis network and linear vector quantization network are used for classification in this work. One hundred twenty samples of 160 samples collected correspond to all motion are used for training cluster and as for 40 samples for testing cluster. As a result maximum accuracy rate has occurred as % 90 for feed forward and back propagation network, % 92 for radial basis network and % 75,5 for learning vector quantization network.

Published in:

Biomedical Engineering Meeting, 2009. BIYOMUT 2009. 14th National

Date of Conference:

20-22 May 2009