VHDL modeling of EMG signal classification using artificial neural network

Electromyography (EMG) signal based research is ongoing for the development of simple, robust, user friendly, efficient interfacing devices/systems. An EMG signal based reliable and efficient hand gesture identification system has been developed for human computer interaction which in turn will incr...

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Main Authors:	Ahsan, Md. Rezwanul, Ibrahimy, Muhammad Ibn, Khalifa, Othman Omran, Ullah, Mohammad Habib
Format:	Article
Language:	English
Published:	Asian Network for Scientific Information 2012
Subjects:	Q Science (General)
Online Access:	http://irep.iium.edu.my/23618/ http://irep.iium.edu.my/23618/1/VHDL_Modeling_of_EMG_Signal_Classification_using_Artificial_Neural.pdf

id	iium-23618
recordtype	eprints
spelling	iium-236182013-01-31T06:57:21Z http://irep.iium.edu.my/23618/ VHDL modeling of EMG signal classification using artificial neural network Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran Ullah, Mohammad Habib Q Science (General) Electromyography (EMG) signal based research is ongoing for the development of simple, robust, user friendly, efficient interfacing devices/systems. An EMG signal based reliable and efficient hand gesture identification system has been developed for human computer interaction which in turn will increase the quality of life of the disabled or aged people. The acquired and processed EMG signal requires classification before utilizing it in the development of interfacing which is the most difficult part of the development process. A back-propagation neural network with Levenberg-Marquardt training algorithm has been used for the classification of EMG signals. This study presents the neural network based classifier modeling using Hardware Description Language (HDL) for hardware realization. VHDL (Very High Speed Integrated Circuit Hardware Description Language) has been used to model the algorithm implemented into the target device FPGA (Field Programmable Gate Array). The designed model has been synthesized and fitted into Altera’s Stratix III, chipset EP3SE50F780I4L using the Quartus II version 9.1 Web Edition. Asian Network for Scientific Information 2012 Article PeerReviewed application/pdf en http://irep.iium.edu.my/23618/1/VHDL_Modeling_of_EMG_Signal_Classification_using_Artificial_Neural.pdf Ahsan, Md. Rezwanul and Ibrahimy, Muhammad Ibn and Khalifa, Othman Omran and Ullah, Mohammad Habib (2012) VHDL modeling of EMG signal classification using artificial neural network. Journal of Applied Sciences, 12 (3). pp. 244-253. ISSN 1812-5662 (O), 1812-5654 (P)
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	Q Science (General)
spellingShingle	Q Science (General) Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran Ullah, Mohammad Habib VHDL modeling of EMG signal classification using artificial neural network
description	Electromyography (EMG) signal based research is ongoing for the development of simple, robust, user friendly, efficient interfacing devices/systems. An EMG signal based reliable and efficient hand gesture identification system has been developed for human computer interaction which in turn will increase the quality of life of the disabled or aged people. The acquired and processed EMG signal requires classification before utilizing it in the development of interfacing which is the most difficult part of the development process. A back-propagation neural network with Levenberg-Marquardt training algorithm has been used for the classification of EMG signals. This study presents the neural network based classifier modeling using Hardware Description Language (HDL) for hardware realization. VHDL (Very High Speed Integrated Circuit Hardware Description Language) has been used to model the algorithm implemented into the target device FPGA (Field Programmable Gate Array). The designed model has been synthesized and fitted into Altera’s Stratix III, chipset EP3SE50F780I4L using the Quartus II version 9.1 Web Edition.
format	Article
author	Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran Ullah, Mohammad Habib
author_facet	Ahsan, Md. Rezwanul Ibrahimy, Muhammad Ibn Khalifa, Othman Omran Ullah, Mohammad Habib
author_sort	Ahsan, Md. Rezwanul
title	VHDL modeling of EMG signal classification using artificial neural network
title_short	VHDL modeling of EMG signal classification using artificial neural network
title_full	VHDL modeling of EMG signal classification using artificial neural network
title_fullStr	VHDL modeling of EMG signal classification using artificial neural network
title_full_unstemmed	VHDL modeling of EMG signal classification using artificial neural network
title_sort	vhdl modeling of emg signal classification using artificial neural network
publisher	Asian Network for Scientific Information
publishDate	2012
url	http://irep.iium.edu.my/23618/ http://irep.iium.edu.my/23618/1/VHDL_Modeling_of_EMG_Signal_Classification_using_Artificial_Neural.pdf
first_indexed	2023-09-18T20:35:42Z
last_indexed	2023-09-18T20:35:42Z
_version_	1777409030636961792

VHDL modeling of EMG signal classification using artificial neural network

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