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Muscle fatigue detection and treatment system driven by internet of things
BACKGROUND: Internet of things is fast becoming the norm in everyday life, and integrating the Internet into medical treatment, which is increasing day by day, is of high utility to both clinical doctors and patients. While there are a number of different health-related problems encountered in daily...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927109/ https://www.ncbi.nlm.nih.gov/pubmed/31865898 http://dx.doi.org/10.1186/s12911-019-0982-x |
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author | Ma, Bin Li, Chunxiao Wu, Zhaolong Huang, Yulong van der Zijp-Tan, Ada Chaeli Tan, Shaobo Li, Dongqi Fong, Ada Basetty, Chandan Borchert, Glen M. Benton, Ryan Wu, Bin Huang, Jingshan |
author_facet | Ma, Bin Li, Chunxiao Wu, Zhaolong Huang, Yulong van der Zijp-Tan, Ada Chaeli Tan, Shaobo Li, Dongqi Fong, Ada Basetty, Chandan Borchert, Glen M. Benton, Ryan Wu, Bin Huang, Jingshan |
author_sort | Ma, Bin |
collection | PubMed |
description | BACKGROUND: Internet of things is fast becoming the norm in everyday life, and integrating the Internet into medical treatment, which is increasing day by day, is of high utility to both clinical doctors and patients. While there are a number of different health-related problems encountered in daily life, muscle fatigue is a common problem encountered by many. METHODS: To facilitate muscle fatigue detection, a pulse width modulation (PWM) and ESP8266-based fatigue detection and recovery system is introduced in this paper to help alleviate muscle fatigue. The ESP8266 is employed as the main controller and communicator, and PWM technology is employed to achieve adaptive muscle recovery. Muscle fatigue can be detected by surface electromyography signals and monitored in real-time via a wireless network. RESULTS: With the help of the proposed system, human muscle fatigue status can be monitored in real-time, and the recovery vibration motor status can be optimized according to muscle activity state. DISCUSSION: Environmental factors had little effect on the response time and accuracy of the system, and the response time was stable between 1 and 2 s. As indicated by the consistent change of digital value, muscle fatigue was clearly diminished using this system. CONCLUSIONS: Experiments show that environmental factors have little effect on the response time and accuracy of the system. The response time is stably between 1 and 2 s, and, as indicated by the consistent change of digital value, our systems clearly diminishes muscle fatigue. Additionally, the experimental results show that the proposed system requires minimal power and is both sensitive and stable. |
format | Online Article Text |
id | pubmed-6927109 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69271092019-12-30 Muscle fatigue detection and treatment system driven by internet of things Ma, Bin Li, Chunxiao Wu, Zhaolong Huang, Yulong van der Zijp-Tan, Ada Chaeli Tan, Shaobo Li, Dongqi Fong, Ada Basetty, Chandan Borchert, Glen M. Benton, Ryan Wu, Bin Huang, Jingshan BMC Med Inform Decis Mak Research BACKGROUND: Internet of things is fast becoming the norm in everyday life, and integrating the Internet into medical treatment, which is increasing day by day, is of high utility to both clinical doctors and patients. While there are a number of different health-related problems encountered in daily life, muscle fatigue is a common problem encountered by many. METHODS: To facilitate muscle fatigue detection, a pulse width modulation (PWM) and ESP8266-based fatigue detection and recovery system is introduced in this paper to help alleviate muscle fatigue. The ESP8266 is employed as the main controller and communicator, and PWM technology is employed to achieve adaptive muscle recovery. Muscle fatigue can be detected by surface electromyography signals and monitored in real-time via a wireless network. RESULTS: With the help of the proposed system, human muscle fatigue status can be monitored in real-time, and the recovery vibration motor status can be optimized according to muscle activity state. DISCUSSION: Environmental factors had little effect on the response time and accuracy of the system, and the response time was stable between 1 and 2 s. As indicated by the consistent change of digital value, muscle fatigue was clearly diminished using this system. CONCLUSIONS: Experiments show that environmental factors have little effect on the response time and accuracy of the system. The response time is stably between 1 and 2 s, and, as indicated by the consistent change of digital value, our systems clearly diminishes muscle fatigue. Additionally, the experimental results show that the proposed system requires minimal power and is both sensitive and stable. BioMed Central 2019-12-23 /pmc/articles/PMC6927109/ /pubmed/31865898 http://dx.doi.org/10.1186/s12911-019-0982-x Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Ma, Bin Li, Chunxiao Wu, Zhaolong Huang, Yulong van der Zijp-Tan, Ada Chaeli Tan, Shaobo Li, Dongqi Fong, Ada Basetty, Chandan Borchert, Glen M. Benton, Ryan Wu, Bin Huang, Jingshan Muscle fatigue detection and treatment system driven by internet of things |
title | Muscle fatigue detection and treatment system driven by internet of things |
title_full | Muscle fatigue detection and treatment system driven by internet of things |
title_fullStr | Muscle fatigue detection and treatment system driven by internet of things |
title_full_unstemmed | Muscle fatigue detection and treatment system driven by internet of things |
title_short | Muscle fatigue detection and treatment system driven by internet of things |
title_sort | muscle fatigue detection and treatment system driven by internet of things |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927109/ https://www.ncbi.nlm.nih.gov/pubmed/31865898 http://dx.doi.org/10.1186/s12911-019-0982-x |
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