By Topic

Discrimination of intended motions for prosthetic hands using nonstationary EMG

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)
Koji Ito ; Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan ; Ayuko Ibe ; Manabu Gouko

The present paper proposes a multiple neural network to determine the movement intended by an amputee from electromyogram (EMG) signals. Most previous approaches to the discrimination of movements using EMG signals have required measurement data with a relative long period exceeding 200 ms. Our approach is possible to identify the amputee's intended movement based on EMG signals of 70 ms long in an initial rise zone. Experiments with four subjects and four electrode locations demonstrated that our proposal determines six forearm movements at a discrimination rate exceeding than 90%.

Published in:

Industrial Electronics, 2009. IECON '09. 35th Annual Conference of IEEE

Date of Conference:

3-5 Nov. 2009