By Topic

Classification of surface electromyographic signals for control of upper limb virtual prosthesis using time-domain features

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
$31 $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

6 Author(s)
Herle, S. ; Dept. of Autom., Tech. Univ. of Cluj-Napoca, Cluj-Napoca ; Raica, P. ; Lazea, G. ; Robotin, R.
more authors

The development of a training system in the field of rehabilitation has always been a challenge for scientists. Surface electromyographical signals are widely used as input signals for upper limb prosthetic devices. The great mental effort of patients fitted with myoelectric prostheses during the training stage, can be reduced by using a simulator of such device. This paper presents an architecture of a system able to assist the patient and a classification technique of surface electromyographical signals, based on neural networks. Four movements of the upper limb have been classified and a rate of recognition of 96.67% was obtained when a reduced number of features were used as inputs for a feed-forward neural network with two hidden layers.

Published in:

Automation, Quality and Testing, Robotics, 2008. AQTR 2008. IEEE International Conference on  (Volume:3 )

Date of Conference:

22-25 May 2008