Hand motion detection from EMG signals by using ANN based classifier for human computer interaction

Ahsan, Md. Rezwanul and Ibrahimy, Muhammad Ibn and Khalifa, Othman Omran (2011) Hand motion detection from EMG signals by using ANN based classifier for human computer interaction. In: 4th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO 2011), 19-21 April 2011, Kuala Lumpur, Malaysia.

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Abstract

Today's advanced muscular sensing and processing technologies have made the acquisition of electromyography (EMG) signal which is valuable. EMG signal is the measurement of electrical potentials generated by muscle cells which is an indicator of muscle activity. Other than rehabilitation engineering and clinical applications, EMG signals can also be employed in the field of human computer interaction (HCI) system. In this work, the detection of different hand movements (left, right, up and down) was obtained using artificial neural network (ANN). A back-propagation (BP) network with Levenberg-Marquardt training algorithm was utilized. The conventional time and time-frequency based feature sets have been chosen to train the neural network. The simulation results show that the designed network is able to recognize hand movements with satisfied classification efficiency in average of 88.4%.

Item Type:	Conference or Workshop Item (Full Paper)
Additional Information:	4637/5898 ISBN : 978-1-4577-0003-3
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr Othman O. Khalifa
Date Deposited:	22 Nov 2011 14:47
Last Modified:	14 May 2012 12:01
URI:	http://irep.iium.edu.my/id/eprint/5898

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