A Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition

Surface electromyography (sEMG) is a kind of biological signal that records muscle activity noninvasively, which is of great significance in advanced human-computer interaction, prosthetic control, clinical therapy, and biomechanics. However, the number of hand gestures that can be recognized is lim...

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Autores principales:	Kun Yang, Manjin Xu, Xiaotong Yang, Runhuai Yang, Yueming Chen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sEMG MVMD separable convolution neural network hand gesture recognition two-stage framework Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/8e6f7afe8d1346c99716328529116b03
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id	oai:doaj.org-article:8e6f7afe8d1346c99716328529116b03
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spelling	oai:doaj.org-article:8e6f7afe8d1346c99716328529116b032021-11-11T19:02:37ZA Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition10.3390/s212170021424-8220https://doaj.org/article/8e6f7afe8d1346c99716328529116b032021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7002https://doaj.org/toc/1424-8220Surface electromyography (sEMG) is a kind of biological signal that records muscle activity noninvasively, which is of great significance in advanced human-computer interaction, prosthetic control, clinical therapy, and biomechanics. However, the number of hand gestures that can be recognized is limited and the recognition accuracy needs to be further improved. These factors lead to the fact that sEMG products are not widely used in practice. The main contributions of this paper are as follows. Firstly, considering the increasing number of gestures to be recognized and the complexity of gestures, an extensible two-stage machine learning lightweight framework was innovatively proposed for multi-gesture task recognition. Secondly, the multivariate variational mode decomposition (MVMD) is applied to extract the spatial–temporal features from the multiple channels to the EMG signals, and the separable convolutional neural network is used for modelling. In this work, the experimental results for 52 hand gestures recognition task show that the average accuracy on each stage is about 90%. The potential movement information is mainly contained in the low-frequency oscillator of the sEMG signal, and the model performs better with the low-frequency oscillation from the MVMD algorithm on the second stage classification than that of other decomposition methods.Kun YangManjin XuXiaotong YangRunhuai YangYueming ChenMDPI AGarticlesEMGMVMDseparable convolution neural networkhand gesture recognitiontwo-stage frameworkChemical technologyTP1-1185ENSensors, Vol 21, Iss 7002, p 7002 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sEMG MVMD separable convolution neural network hand gesture recognition two-stage framework Chemical technology TP1-1185
spellingShingle	sEMG MVMD separable convolution neural network hand gesture recognition two-stage framework Chemical technology TP1-1185 Kun Yang Manjin Xu Xiaotong Yang Runhuai Yang Yueming Chen A Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition
description	Surface electromyography (sEMG) is a kind of biological signal that records muscle activity noninvasively, which is of great significance in advanced human-computer interaction, prosthetic control, clinical therapy, and biomechanics. However, the number of hand gestures that can be recognized is limited and the recognition accuracy needs to be further improved. These factors lead to the fact that sEMG products are not widely used in practice. The main contributions of this paper are as follows. Firstly, considering the increasing number of gestures to be recognized and the complexity of gestures, an extensible two-stage machine learning lightweight framework was innovatively proposed for multi-gesture task recognition. Secondly, the multivariate variational mode decomposition (MVMD) is applied to extract the spatial–temporal features from the multiple channels to the EMG signals, and the separable convolutional neural network is used for modelling. In this work, the experimental results for 52 hand gestures recognition task show that the average accuracy on each stage is about 90%. The potential movement information is mainly contained in the low-frequency oscillator of the sEMG signal, and the model performs better with the low-frequency oscillation from the MVMD algorithm on the second stage classification than that of other decomposition methods.
format	article
author	Kun Yang Manjin Xu Xiaotong Yang Runhuai Yang Yueming Chen
author_facet	Kun Yang Manjin Xu Xiaotong Yang Runhuai Yang Yueming Chen
author_sort	Kun Yang
title	A Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition
title_short	A Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition
title_full	A Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition
title_fullStr	A Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition
title_full_unstemmed	A Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition
title_sort	novel emg-based hand gesture recognition framework based on multivariate variational mode decomposition
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8e6f7afe8d1346c99716328529116b03
work_keys_str_mv	AT kunyang anovelemgbasedhandgesturerecognitionframeworkbasedonmultivariatevariationalmodedecomposition AT manjinxu anovelemgbasedhandgesturerecognitionframeworkbasedonmultivariatevariationalmodedecomposition AT xiaotongyang anovelemgbasedhandgesturerecognitionframeworkbasedonmultivariatevariationalmodedecomposition AT runhuaiyang anovelemgbasedhandgesturerecognitionframeworkbasedonmultivariatevariationalmodedecomposition AT yuemingchen anovelemgbasedhandgesturerecognitionframeworkbasedonmultivariatevariationalmodedecomposition AT kunyang novelemgbasedhandgesturerecognitionframeworkbasedonmultivariatevariationalmodedecomposition AT manjinxu novelemgbasedhandgesturerecognitionframeworkbasedonmultivariatevariationalmodedecomposition AT xiaotongyang novelemgbasedhandgesturerecognitionframeworkbasedonmultivariatevariationalmodedecomposition AT runhuaiyang novelemgbasedhandgesturerecognitionframeworkbasedonmultivariatevariationalmodedecomposition AT yuemingchen novelemgbasedhandgesturerecognitionframeworkbasedonmultivariatevariationalmodedecomposition
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A Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition

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