Algorithms for classification of a single channel EMG signal for human-computer interaction

One of the most accurate and effective ways to control gestures is to control muscle activity, which occurs with any movement. Electromyography (EMG) is used to record such activity. This article compares SVM classification algorithms, perceptron, random trees and the method of density of probabilit...

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Main Authors:	Lukyanchikov Andrei, Melnikov Alexei, Lukyanchikov Oleg
Format:	Article
Language:	English
Published:	EDP Sciences 2018-01-01
Series:	ITM Web of Conferences
Online Access:	https://doi.org/10.1051/itmconf/20181802001

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spelling	doaj-9e1d192d1af547418248ea396ee6b4202021-04-02T10:51:20ZengEDP SciencesITM Web of Conferences2271-20972018-01-01180200110.1051/itmconf/20181802001itmconf_ics2018_02001Algorithms for classification of a single channel EMG signal for human-computer interactionLukyanchikov AndreiMelnikov AlexeiLukyanchikov OlegOne of the most accurate and effective ways to control gestures is to control muscle activity, which occurs with any movement. Electromyography (EMG) is used to record such activity. This article compares SVM classification algorithms, perceptron, random trees and the method of density of probability in relation to the EMG signal. Arduino Leonardo with a single-channel Shield EMG is used to record the signal. The aim of this paper is to prove the possibility of creating a cheap and accessible biointerface based on EMG signal.https://doi.org/10.1051/itmconf/20181802001
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Lukyanchikov Andrei Melnikov Alexei Lukyanchikov Oleg
spellingShingle	Lukyanchikov Andrei Melnikov Alexei Lukyanchikov Oleg Algorithms for classification of a single channel EMG signal for human-computer interaction ITM Web of Conferences
author_facet	Lukyanchikov Andrei Melnikov Alexei Lukyanchikov Oleg
author_sort	Lukyanchikov Andrei
title	Algorithms for classification of a single channel EMG signal for human-computer interaction
title_short	Algorithms for classification of a single channel EMG signal for human-computer interaction
title_full	Algorithms for classification of a single channel EMG signal for human-computer interaction
title_fullStr	Algorithms for classification of a single channel EMG signal for human-computer interaction
title_full_unstemmed	Algorithms for classification of a single channel EMG signal for human-computer interaction
title_sort	algorithms for classification of a single channel emg signal for human-computer interaction
publisher	EDP Sciences
series	ITM Web of Conferences
issn	2271-2097
publishDate	2018-01-01
description	One of the most accurate and effective ways to control gestures is to control muscle activity, which occurs with any movement. Electromyography (EMG) is used to record such activity. This article compares SVM classification algorithms, perceptron, random trees and the method of density of probability in relation to the EMG signal. Arduino Leonardo with a single-channel Shield EMG is used to record the signal. The aim of this paper is to prove the possibility of creating a cheap and accessible biointerface based on EMG signal.
url	https://doi.org/10.1051/itmconf/20181802001
work_keys_str_mv	AT lukyanchikovandrei algorithmsforclassificationofasinglechannelemgsignalforhumancomputerinteraction AT melnikovalexei algorithmsforclassificationofasinglechannelemgsignalforhumancomputerinteraction AT lukyanchikovoleg algorithmsforclassificationofasinglechannelemgsignalforhumancomputerinteraction
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Algorithms for classification of a single channel EMG signal for human-computer interaction

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