Real-time Classification of Finger Movements using Two-channel Surface Electromyography

dc.contributor.author	Anam, Khairul
dc.contributor.author	Al-Jumaily, Adel
dc.date.accessioned	2017-10-11T08:08:35Z
dc.date.available	2017-10-11T08:08:35Z
dc.date.issued	2017-10-11
dc.identifier.isbn	978-989-8565-80-8
dc.identifier.uri	http://repository.unej.ac.id/handle/123456789/82072
dc.description	Proceedings NEUROTECHNIX 2013	en_US
dc.description.abstract	The use of a small number of Electromyography (EMG) channels for classifying the finger movement is a challenging task. This paper proposes the recognition system for decoding the individual and combined finger movements using two channels surface EMG. The proposed system utilizes Spectral Regression Discriminant Analysis (SRDA) for dimensionality reduction, Extreme Learning Machine (ELM) for classification and the majority vote for the classification smoothness. The experimental results show that the proposed system was able to classify ten classes of individual and combined finger movements, offline and online with accuracy 97.96 % and 97.07% respectively.	en_US
dc.language.iso	en	en_US
dc.subject	Surface EMG	en_US
dc.subject	Extreme Learning Machine	en_US
dc.subject	Finger Movements	en_US
dc.title	Real-time Classification of Finger Movements using Two-channel Surface Electromyography	en_US
dc.type	Prosiding	en_US