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Joint Torque Estimation of Elbow joint using Neural Network Back Propagation Theory  

Jang, Hye-Youn (Department of Mechanical Engineering, Hanyang Univ.)
Kim, Wan-Soo (Department of Mechanical Engineering, Hanyang Univ.)
Han, Jung-Soo (Department of Mechanical System Engineering, Hansung Univ.)
Han, Chang-Soo (Department of Mechanical Engineering, Hanyang Univ.)
Publication Information
Journal of the Korean Society for Precision Engineering / v.28, no.6, 2011 , pp. 670-677 More about this Journal
Abstract
This study is to estimate the joint torques without torque sensor using the EMG (Electromyogram) signal of agonist/antagonist muscle with Neural Network Back Propagation Algorithm during the elbow motion. Command Signal can be guessed by EMG signal. But it cannot calculate the joint torque. There are many kinds of field utilizing Back Propagation Learning Method. It is generally used as a virtual sensor estimated physical information in the system functioning through the sensor. In this study applied the algorithm to obtain the virtual senor values estimated joint torque. During various elbow movement (Biceps isometric contraction, Biceps/Triceps Concentric Contraction (isotonic), Biceps/Triceps Concentric Contraction/Eccentric Contraction (isokinetic)), exact joint torque was measured by KINCOM equipment. It is input to the (BP)algorithm with EMG signal simultaneously and have trained in a variety of situations. As a result, Only using the EMG sensor, this study distinguished a variety of elbow motion and verified a virtual torque value which is approximately(about 90%) the same as joint torque measured by KINCOM equipment.
Keywords
Neural Network; Back Propagation; Electromyogram; Joint Torque;
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Times Cited By KSCI : 3  (Citation Analysis)
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