Joint torque estimation model of sEMG signal for arm rehabilitation device using artificial neural network technique

UNSPECIFIED (2015) Joint torque estimation model of sEMG signal for arm rehabilitation device using artificial neural network technique. Advanced Computer and Communication Engineering Technology, 315. pp. 671-682. ISSN 1876-1100

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Abstract

Rehabilitation device is used as an exoskeleton for peoples who had failure of their limb. Arm rehabilitation device may help the rehab program to whom suffered with arm disability. The device is used to facilitate the tasks of the program and minimize the mental effort of the user. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. To minimize the used of mental forced for disable patients, the rehabilitation device can be utilize by analyzing the surface EMG signal of normal people that can be implemented to the device. The objective of this work is to model the muscle EMG signal to torque for a motor control of the arm rehabilitation device using Artificial Neural Network (ANN) technique. The EMG signal is collected from Biceps Brachii muscles to estimate the elbow joint torque. A two layer feed-forward network is trained using Back Propagation Neural Network (BPNN) to model the EMG signal to torque value. The performance result of the network is measured based on the Mean Squared Error (MSE) of the training data and Regression (R) between the target outputs and the network outputs. The experimental results show that ANN can well represent EMG-torque relationship for arm rehabilitation device control

Item Type:	Article
Uncontrolled Keywords:	Artificial neural network, Mean square error, Artificial neural network model, Hide neuron, Biceps Brachii
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Electrical Engineering > Department of Diploma Studies
Depositing User:	MOHD HAFIZ JALI
Date Deposited:	14 Dec 2015 03:35
Last Modified:	21 Jul 2023 16:34
URI:	http://eprints.utem.edu.my/id/eprint/15381
Statistic Details:	View Download Statistic

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