NDLI: Extraction of elbow joint intention from sEMG signals in horizontal plane using cosine tuning functions

Content Provider	IEEE Xplore Digital Library
Author	Quanjun Song Ming Liu Lina Tong Yong Yu Yunjian Ge
Copyright Year	2007
Description	Author affiliation: Robot Sensing Syst. Lab., Chinese Acad. of Sci., Hefei (Quanjun Song)
Abstract	A human-robot interaction (HRI) system could be more effective if it is intelligent enough to accurately measure and predict humans' moving intention and adapt their behaviors accordingly. By means of measuring biomechanical signals of human body to make such predication has been a challenging research topic. This paper presents results of our investigation to extract human's elbow joint motion intention from surface electromyographic (sEMG) measurement. While a human subject's limb is kept still on a horizontal table but subject to certain force applied to the hand, by changing the direction of the force applied, the biomechanical signals are simultaneously recorded from three sEMGs mounted on superficial elbow and shoulder muscles in real-time. Based on the measurement obtained, we study the relation between static sEMG signals and movement trends of the elbow in eight directions using cosine tuning functions. Genetic algorithm (GA) is applied to determine multivariate nonlinear regression coefficient to improve the prediction accuracy.
Starting Page	2206
Ending Page	2211
File Size	238942
Page Count	6
File Format	PDF
ISBN	9781424417612
DOI	10.1109/ROBIO.2007.4522512
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Elbow Electromyography Humans Muscles Rehabilitation robotics Intelligent robots Motion control Robot sensing systems Robotics and automation Control systems Cosine tuning functions EMG signal Motion intention
Content Type	Text
Resource Type	Article

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