Joint torque estimation model of SEMG signal for arm rehabilitation device using artificial neural network techniques

Jali, Mohd Hafiz and Ahmad Izzuddin, Tarmizi and Bohari, Zul Hasrizal and Hafez, Sarkawi and Sulaima, Mohamad Fani and Baharom, Mohamad Faizal and Wan Daud, Wan Mohd Bukhari (2014) Joint torque estimation model of SEMG signal for arm rehabilitation device using artificial neural network techniques. ARPN Journal of Engineering and Applied Sciences, 9 (7). pp. 1170-1176. ISSN 1819-6608

Text ARPN_JOINT_TORQUE_ESTIMATION_MODEL_OF_SEMG_SIGNAL_FOR_ARM_REHABILITATION_DEVICE_USING_ARTIFICIAL_NEURAL_NETWORK_TECHNIQUES.pdf Download (658kB)

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 who 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:	electromyography, torque, artificial neural network.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Electrical Engineering > Department of Control, Instrumentation & Automation
Depositing User:	MOHAMAD FANI SULAIMA
Date Deposited:	25 Jul 2014 09:37
Last Modified:	24 Jul 2023 08:04
URI:	http://eprints.utem.edu.my/id/eprint/12997
Statistic Details:	View Download Statistic

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