Cart (Loading....) | Create Account
Close category search window
 

Research on the Surface EMG Signal for Human Body Motion Recognizing Based on Arm Wrestling Robot

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)
Zhen Gao ; State Key Lab. of Robot Sensing Syst., Chinese Acad. of Sci., Anhui ; Jianhe Lei ; Quanjun Song ; Yong Yu
more authors

In this paper, the surface electromyographic (EMG) signals is acquired from the upper limb when the experimenter competes with the arm wrestling robot (AWR) which is integrated with mechanical arm, elbow/wrist force sensors, servo motor, encoder, 3D MEMS accelerometer, and USB camera. The arm wrestling robot (AWR) is intended to play arm wrestling game with human on a table with pegs for entertainment and human upper limbs muscle modeling. As the EMG signal is a measurement of the anatomical and physiological characteristic of the given muscle, the macroscopical movement patterns of the human body can be classified and recognized. By using the method of wavelet packet transformation (WPT), the high-frequency noises can be eliminated effectively and the characteristics of EMG signals can be extracted. Auto-regressive (AR) model is adopted to effectively simulate the stochastic and non-stationary time sequences using a series of AR coefficients with a typical order. Artificial neural network (ANN) is utilized to distinguish the different force levels and game grades in the scenario of arm-wrestling. To advance the training speed and accurate rate of the motion pattern classification, back-propagation (BP) neural network based on adaptive learning rate algorithm (ALR) is introduced. The advantage of ALR algorithm compared with standard BP algorithm is confirmed by experiments

Published in:

Information Acquisition, 2006 IEEE International Conference on

Date of Conference:

20-23 Aug. 2006

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.