Musculoskeletal Rehabilitation Status Monitoring Based on sEMG

The reduction and improper movements in people’s modern life will lead to physical discomfort, pain, and inflammation, which have generally affected the quality of people’s daily life and work efficiency. The pain caused by improper movements are called musculoskeletal pain, which can be relieved or...

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Autores principales:	Xue Han, Yan Zhao, Feng Wang, Zun Liu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/45a68b85ba824348b8f29f7775cccfb4
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spelling	oai:doaj.org-article:45a68b85ba824348b8f29f7775cccfb42021-11-15T01:19:55ZMusculoskeletal Rehabilitation Status Monitoring Based on sEMG1875-905X10.1155/2021/4482578https://doaj.org/article/45a68b85ba824348b8f29f7775cccfb42021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4482578https://doaj.org/toc/1875-905XThe reduction and improper movements in people’s modern life will lead to physical discomfort, pain, and inflammation, which have generally affected the quality of people’s daily life and work efficiency. The pain caused by improper movements are called musculoskeletal pain, which can be relieved or eliminated with treatment. Musculoskeletal disorders are actually one of the most common medical conditions, which affects approximately one quarter of all adults in the world. Although surface electromyography (sEMG) is an acknowledged technology in musculoskeletal rehabilitation study, it is considerably significant to monitor the musculoskeletal rehabilitation status based on sEMG. In order to monitor the musculoskeletal rehabilitation status, we combine fuzzy theory with neural network. This article proposes variable size, sliding window-based, generalized, dynamic, fuzzy neural network (GD-FNN), musculoskeletal rehabilitation status monitoring, that is, the window length of sliding window of sample data changes with the size of sample period. Finally, this study made a simulation on subjects, and the experimental results show that the proposed variable size, sliding window-based GD-FNN, musculoskeletal rehabilitation status monitoring method not only has good monitoring effect but also put on a good performance in root-mean-squared error (RMSE) and mean absolute percentage error (MAPE).Xue HanYan ZhaoFeng WangZun LiuHindawi LimitedarticleTelecommunicationTK5101-6720ENMobile Information Systems, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Xue Han Yan Zhao Feng Wang Zun Liu Musculoskeletal Rehabilitation Status Monitoring Based on sEMG
description	The reduction and improper movements in people’s modern life will lead to physical discomfort, pain, and inflammation, which have generally affected the quality of people’s daily life and work efficiency. The pain caused by improper movements are called musculoskeletal pain, which can be relieved or eliminated with treatment. Musculoskeletal disorders are actually one of the most common medical conditions, which affects approximately one quarter of all adults in the world. Although surface electromyography (sEMG) is an acknowledged technology in musculoskeletal rehabilitation study, it is considerably significant to monitor the musculoskeletal rehabilitation status based on sEMG. In order to monitor the musculoskeletal rehabilitation status, we combine fuzzy theory with neural network. This article proposes variable size, sliding window-based, generalized, dynamic, fuzzy neural network (GD-FNN), musculoskeletal rehabilitation status monitoring, that is, the window length of sliding window of sample data changes with the size of sample period. Finally, this study made a simulation on subjects, and the experimental results show that the proposed variable size, sliding window-based GD-FNN, musculoskeletal rehabilitation status monitoring method not only has good monitoring effect but also put on a good performance in root-mean-squared error (RMSE) and mean absolute percentage error (MAPE).
format	article
author	Xue Han Yan Zhao Feng Wang Zun Liu
author_facet	Xue Han Yan Zhao Feng Wang Zun Liu
author_sort	Xue Han
title	Musculoskeletal Rehabilitation Status Monitoring Based on sEMG
title_short	Musculoskeletal Rehabilitation Status Monitoring Based on sEMG
title_full	Musculoskeletal Rehabilitation Status Monitoring Based on sEMG
title_fullStr	Musculoskeletal Rehabilitation Status Monitoring Based on sEMG
title_full_unstemmed	Musculoskeletal Rehabilitation Status Monitoring Based on sEMG
title_sort	musculoskeletal rehabilitation status monitoring based on semg
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/45a68b85ba824348b8f29f7775cccfb4
work_keys_str_mv	AT xuehan musculoskeletalrehabilitationstatusmonitoringbasedonsemg AT yanzhao musculoskeletalrehabilitationstatusmonitoringbasedonsemg AT fengwang musculoskeletalrehabilitationstatusmonitoringbasedonsemg AT zunliu musculoskeletalrehabilitationstatusmonitoringbasedonsemg
_version_	1718428918151643136

Musculoskeletal Rehabilitation Status Monitoring Based on sEMG

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