By Topic

Robot motion governing using upper limb EMG signal based on empirical mode decomposition

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

2 Author(s)
Hsiu-Jen Liu ; Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan ; Kuu-young Young

This paper presents a simple and effective approach to govern robot arm motion in real time using upper limb EMG signals. Considering the non-stationary and nonlinear characteristics of the EMG signals, in the design for feature extraction, we introduce the empirical mode decomposition (EMD) to decompose the EMG signals into intrinsic mode functions (IMFs). Each IMF represents different physical characteristic, so that the muscular movement can be recognized. We then integrate it with a so-called initial point detection method previously proposed to establish the mapping between the upper limb EMG signals and corresponding robot arm movements in real time. In addition, for each individual user, we adopt a fuzzy approach to select proper system parameters for motion classification. The experimental results show the feasibility of the proposed approach with accurate motion recognition.

Published in:

Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on

Date of Conference:

10-13 Oct. 2010