Neural network classifier for hand motion detection from EMG signal

Ibrahimy, Muhammad Ibn and Khalifa, Othman Omran (2011) Neural network classifier for hand motion detection from EMG signal. In: IFMBE Proceedings. Springer Berlin Heidelberg, pp. 536-541.

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Abstract

EMG signal based research is ongoing for the development of simple, robust, user friendly, efficient interfacing devices/systems for the disabled. The advancement can be observed in the area of robotic devices, prosthesis limb, exoskeleton, wearable computer, I/O for virtual reality games and physical exercise equipments. Additionally, electromyography (EMG) signals can also be applied in the field of human computer interaction (HCI) system. This paper represents the detection of different predefined hand motions (left, right, up and down) using artificial neural network (ANN). A backpropagation (BP) network with Levenberg-Marquardt training algorithm has been utilized for the classification of EMG signals. The conventional and most effective time and timefrequency based feature set is utilized for the training of neural network. The obtained results show that the designed network is able to recognize hand movements with satisfied classification efficiency in average of 88.4%. Furthermore, when the trained network tested on unknown data set, it successfully identify the movement types.

Item Type:	Book Chapter
Additional Information:	/5998
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering
Depositing User:	Dr Muhammad Ibrahimy
Date Deposited:	03 Jan 2012 15:47
Last Modified:	04 Nov 2020 14:20
URI:	http://irep.iium.edu.my/id/eprint/5998

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