Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model

The deep learning gesture recognition based on surface electromyography plays an increasingly important role in human-computer interaction. In order to ensure the high accuracy of deep learning in multistate muscle action recognition and ensure that the training model can be applied in the embedded...

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Autores principales:	Dianchun Bai, Tie Liu, Xinghua Han, Hongyu Yi
Formato:	article
Lenguaje:	EN
Publicado:	American Association for the Advancement of Science (AAAS) 2021
Materias:	Cybernetics Q300-390
Acceso en línea:	https://doaj.org/article/801366f59fad4fb3b10248f293a51173
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id	oai:doaj.org-article:801366f59fad4fb3b10248f293a51173
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spelling	oai:doaj.org-article:801366f59fad4fb3b10248f293a511732021-11-22T08:31:09ZApplication Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model2692-763210.34133/2021/9794610https://doaj.org/article/801366f59fad4fb3b10248f293a511732021-01-01T00:00:00Zhttp://dx.doi.org/10.34133/2021/9794610https://doaj.org/toc/2692-7632The deep learning gesture recognition based on surface electromyography plays an increasingly important role in human-computer interaction. In order to ensure the high accuracy of deep learning in multistate muscle action recognition and ensure that the training model can be applied in the embedded chip with small storage space, this paper presents a feature model construction and optimization method based on multichannel sEMG amplification unit. The feature model is established by using multidimensional sequential sEMG images by combining convolutional neural network and long-term memory network to solve the problem of multistate sEMG signal recognition. The experimental results show that under the same network structure, the sEMG signal with fast Fourier transform and root mean square as feature data processing has a good recognition rate, and the recognition accuracy of complex gestures is 91.40%, with the size of 1 MB. The model can still control the artificial hand accurately when the model is small and the precision is high.Dianchun BaiTie LiuXinghua HanHongyu YiAmerican Association for the Advancement of Science (AAAS)articleCyberneticsQ300-390ENCyborg and Bionic Systems, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Cybernetics Q300-390
spellingShingle	Cybernetics Q300-390 Dianchun Bai Tie Liu Xinghua Han Hongyu Yi Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model
description	The deep learning gesture recognition based on surface electromyography plays an increasingly important role in human-computer interaction. In order to ensure the high accuracy of deep learning in multistate muscle action recognition and ensure that the training model can be applied in the embedded chip with small storage space, this paper presents a feature model construction and optimization method based on multichannel sEMG amplification unit. The feature model is established by using multidimensional sequential sEMG images by combining convolutional neural network and long-term memory network to solve the problem of multistate sEMG signal recognition. The experimental results show that under the same network structure, the sEMG signal with fast Fourier transform and root mean square as feature data processing has a good recognition rate, and the recognition accuracy of complex gestures is 91.40%, with the size of 1 MB. The model can still control the artificial hand accurately when the model is small and the precision is high.
format	article
author	Dianchun Bai Tie Liu Xinghua Han Hongyu Yi
author_facet	Dianchun Bai Tie Liu Xinghua Han Hongyu Yi
author_sort	Dianchun Bai
title	Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model
title_short	Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model
title_full	Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model
title_fullStr	Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model
title_full_unstemmed	Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model
title_sort	application research on optimization algorithm of semg gesture recognition based on light cnn+lstm model
publisher	American Association for the Advancement of Science (AAAS)
publishDate	2021
url	https://doaj.org/article/801366f59fad4fb3b10248f293a51173
work_keys_str_mv	AT dianchunbai applicationresearchonoptimizationalgorithmofsemggesturerecognitionbasedonlightcnnlstmmodel AT tieliu applicationresearchonoptimizationalgorithmofsemggesturerecognitionbasedonlightcnnlstmmodel AT xinghuahan applicationresearchonoptimizationalgorithmofsemggesturerecognitionbasedonlightcnnlstmmodel AT hongyuyi applicationresearchonoptimizationalgorithmofsemggesturerecognitionbasedonlightcnnlstmmodel
_version_	1718417806249164800

Application Research on Optimization Algorithm of sEMG Gesture Recognition Based on Light CNN+LSTM Model

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