By Topic

Real-Time Hand Motion Estimation Using EMG Signals with Support Vector Machines

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

3 Author(s)
Yoshikawa, M. ; Tsukuba Univ., Ibaraki ; Mikawa, M. ; Tanaka, K.

Various interfaces using Electromyogram (EMG) signals for controlling a robot hand have been developed. However, there are few researches that apply support vector machines (SVMs) to EMG signal classification for estimating operator's hand motions. There is a possibility that the SVMs are effective classifiers. This paper proposes a real-time hand motion estimation method using the EMG signals with the SVMs. This method consists of two phases for the hand motion estimation. The first phase is the hand motion classification of EMG signal patterns with the SVMs. In addition to amplitude features in the EMG signals, cepstrum coefficients are extracted as frequency features for robust classification. The second phase is the estimation of operator's joint angles. The joint angles are estimated from EMG signals based on simple linear models between the joint angles and the EMG signals. These two phases are designed so that they can be processed in real-time. Experimental results of seven hand motion estimation show the effectiveness of our proposed method

Published in:

SICE-ICASE, 2006. International Joint Conference

Date of Conference:

18-21 Oct. 2006