sEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups

Surface electromyography (sEMG)-based gesture recognition systems provide the intuitive and accurate recognition of various gestures in human-computer interaction. In this study, an sEMG-based hand posture recognition algorithm was developed, considering three main problems: electrode shift, feature...

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Autores principales:	Jongman Kim, Bummo Koo, Yejin Nam, Youngho Kim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	surface electromyography pattern recognition artificial neural network electrode shift hand posture feature vector Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/ab70a0bdbb8b486bb9bddca3c0da4443
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id	oai:doaj.org-article:ab70a0bdbb8b486bb9bddca3c0da4443
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spelling	oai:doaj.org-article:ab70a0bdbb8b486bb9bddca3c0da44432021-11-25T18:58:26ZsEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups10.3390/s212276811424-8220https://doaj.org/article/ab70a0bdbb8b486bb9bddca3c0da44432021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7681https://doaj.org/toc/1424-8220Surface electromyography (sEMG)-based gesture recognition systems provide the intuitive and accurate recognition of various gestures in human-computer interaction. In this study, an sEMG-based hand posture recognition algorithm was developed, considering three main problems: electrode shift, feature vectors, and posture groups. The sEMG signal was measured using an armband sensor with the electrode shift. An artificial neural network classifier was trained using 21 feature vectors for seven different posture groups. The inter-session and inter-feature Pearson correlation coefficients (PCCs) were calculated. The results indicate that the classification performance improved with the number of training sessions of the electrode shift. The number of sessions necessary for efficient training was four, and the feature vectors with a high inter-session PCC (<i>r</i> > 0.7) exhibited high classification accuracy. Similarities between postures in a posture group decreased the classification accuracy. Our results indicate that the classification accuracy could be improved with the addition of more electrode shift training sessions and that the PCC is useful for selecting the feature vector. Furthermore, hand posture selection was as important as feature vector selection. These findings will help in optimizing the sEMG-based pattern recognition algorithm more easily and quickly.Jongman KimBummo KooYejin NamYoungho KimMDPI AGarticlesurface electromyographypattern recognitionartificial neural networkelectrode shifthand posturefeature vectorChemical technologyTP1-1185ENSensors, Vol 21, Iss 7681, p 7681 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	surface electromyography pattern recognition artificial neural network electrode shift hand posture feature vector Chemical technology TP1-1185
spellingShingle	surface electromyography pattern recognition artificial neural network electrode shift hand posture feature vector Chemical technology TP1-1185 Jongman Kim Bummo Koo Yejin Nam Youngho Kim sEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups
description	Surface electromyography (sEMG)-based gesture recognition systems provide the intuitive and accurate recognition of various gestures in human-computer interaction. In this study, an sEMG-based hand posture recognition algorithm was developed, considering three main problems: electrode shift, feature vectors, and posture groups. The sEMG signal was measured using an armband sensor with the electrode shift. An artificial neural network classifier was trained using 21 feature vectors for seven different posture groups. The inter-session and inter-feature Pearson correlation coefficients (PCCs) were calculated. The results indicate that the classification performance improved with the number of training sessions of the electrode shift. The number of sessions necessary for efficient training was four, and the feature vectors with a high inter-session PCC (<i>r</i> > 0.7) exhibited high classification accuracy. Similarities between postures in a posture group decreased the classification accuracy. Our results indicate that the classification accuracy could be improved with the addition of more electrode shift training sessions and that the PCC is useful for selecting the feature vector. Furthermore, hand posture selection was as important as feature vector selection. These findings will help in optimizing the sEMG-based pattern recognition algorithm more easily and quickly.
format	article
author	Jongman Kim Bummo Koo Yejin Nam Youngho Kim
author_facet	Jongman Kim Bummo Koo Yejin Nam Youngho Kim
author_sort	Jongman Kim
title	sEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups
title_short	sEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups
title_full	sEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups
title_fullStr	sEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups
title_full_unstemmed	sEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups
title_sort	semg-based hand posture recognition considering electrode shift, feature vectors, and posture groups
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ab70a0bdbb8b486bb9bddca3c0da4443
work_keys_str_mv	AT jongmankim semgbasedhandposturerecognitionconsideringelectrodeshiftfeaturevectorsandposturegroups AT bummokoo semgbasedhandposturerecognitionconsideringelectrodeshiftfeaturevectorsandposturegroups AT yejinnam semgbasedhandposturerecognitionconsideringelectrodeshiftfeaturevectorsandposturegroups AT younghokim semgbasedhandposturerecognitionconsideringelectrodeshiftfeaturevectorsandposturegroups
_version_	1718410481735041024

sEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups

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