Surface Electromyographic signal based finger  prosthesis control using ANN

dc.contributor.author	Islam, Jahedul
dc.contributor.author	Sarker, Dhiman Kumar
dc.contributor.author	Das, Piyas
dc.date.accessioned	2024-03-12T05:29:33Z
dc.date.available	2024-03-12T05:29:33Z
dc.date.issued	2019-09-26
dc.identifier.uri	http://103.99.128.19:8080/xmlui/handle/123456789/390
dc.description.abstract	This paper represents the development of surface Electromyographic (sEMG) signal-based finger prosthesis control. A filter & amplifier circuit captures the EMG signal from the surface of the human hand that can be recorded using ATmega-2560 micro-controller. The analysis of the output signal is done to study time domain features. In this paper, standard deviation, mean, a variance is taken as time domain feature. The signal is then trained using simple Artificial Neural Network to classify accurately two finger motion i.e. grip motion and thumb index finger motion.	en_US
dc.description.sponsorship	IEEE	en_US
dc.language.iso	en_US	en_US
dc.publisher	Department of Electrical and Electronics Engineering, IUB	en_US
dc.relation.ispartofseries	ICECE;
dc.subject	ATmega-2560	en_US
dc.subject	sEMG Signal	en_US
dc.subject	Electrode	en_US
dc.subject	Servo	en_US
dc.subject	Instrumentation Amplifier	en_US
dc.subject	Moving Average	en_US
dc.subject	ANN	en_US
dc.title	Surface Electromyographic signal based finger prosthesis control using ANN	en_US
dc.title.alternative	5th International Conference on Advances in Electrical Engineering (ICAEE) 2019	en_US
dc.title.alternative	ICAEE 2019	en_US
dc.type	Article	en_US