Please use this identifier to cite or link to this item: http://103.99.128.19:8080/xmlui/handle/123456789/390
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dc.contributor.authorIslam, Jahedul-
dc.contributor.authorSarker, Dhiman Kumar-
dc.contributor.authorDas, Piyas-
dc.date.accessioned2024-03-12T05:29:33Z-
dc.date.available2024-03-12T05:29:33Z-
dc.date.issued2019-09-26-
dc.identifier.urihttp://103.99.128.19:8080/xmlui/handle/123456789/390-
dc.description.abstractThis 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.sponsorshipIEEEen_US
dc.language.isoen_USen_US
dc.publisherDepartment of Electrical and Electronics Engineering, IUBen_US
dc.relation.ispartofseriesICECE;-
dc.subjectATmega-2560en_US
dc.subjectsEMG Signalen_US
dc.subjectElectrodeen_US
dc.subjectServoen_US
dc.subjectInstrumentation Amplifieren_US
dc.subjectMoving Averageen_US
dc.subjectANNen_US
dc.titleSurface Electromyographic signal based finger prosthesis control using ANNen_US
dc.title.alternative5th International Conference on Advances in Electrical Engineering (ICAEE) 2019en_US
dc.title.alternativeICAEE 2019en_US
dc.typeArticleen_US
Appears in Collections:proceedings in EEE

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