By Topic

Classification of EMG signals using artificial neural networks for virtual hand prosthesis control

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

5 Author(s)
Mattioli, F.E.R. ; Virtual & Augmented Reality Res. Group, Fed. Univ. of Uberlandia, Uberlandia, Brazil ; Lamounier, E.A. ; Cardoso, A. ; Soares, A.B.
more authors

Computer-based training systems have been widely studied in the field of human rehabilitation. In health applications, Virtual Reality presents itself as an appropriate tool to simulate training environments without exposing the patients to risks. In particular, virtual prosthetic devices have been used to reduce the great mental effort needed by patients fitted with myoelectric prosthesis, during the training stage. In this paper, the application of Virtual Reality in a hand prosthesis training system is presented. To achieve this, the possibility of exploring Neural Networks in a real-time classification system is discussed. The classification technique used in this work resulted in a 95% success rate when discriminating 4 different hand movements.

Published in:

Engineering in Medicine and Biology Society,EMBC, 2011 Annual International Conference of the IEEE

Date of Conference:

Aug. 30 2011-Sept. 3 2011