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A comprehensive review of approaches to detect fatigue using machine learning techniques
In the past decades, there have been numerous advancements in the field of technology. This has led to many scientific breakthroughs in the field of medical sciences. In this, rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent. So, this study a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9128560/ https://www.ncbi.nlm.nih.gov/pubmed/35620159 http://dx.doi.org/10.1016/j.cdtm.2021.07.002 |
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author | Hooda, Rohit Joshi, Vedant Shah, Manan |
author_facet | Hooda, Rohit Joshi, Vedant Shah, Manan |
author_sort | Hooda, Rohit |
collection | PubMed |
description | In the past decades, there have been numerous advancements in the field of technology. This has led to many scientific breakthroughs in the field of medical sciences. In this, rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent. So, this study aimed to understand what is fatigue, its repercussions, and techniques to detect it using machine learning (ML) approaches. This paper introduces, discusses methods and recent advancements in the field of fatigue detection. Further, we categorized the methods that can be used to detect fatigue into four diverse groups, that is, mathematical models, rule‐based implementation, ML, and deep learning. This study presents, compares, and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue. Finally, the paper discusses the possible areas for improvement. |
format | Online Article Text |
id | pubmed-9128560 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91285602022-05-25 A comprehensive review of approaches to detect fatigue using machine learning techniques Hooda, Rohit Joshi, Vedant Shah, Manan Chronic Dis Transl Med Reviews In the past decades, there have been numerous advancements in the field of technology. This has led to many scientific breakthroughs in the field of medical sciences. In this, rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent. So, this study aimed to understand what is fatigue, its repercussions, and techniques to detect it using machine learning (ML) approaches. This paper introduces, discusses methods and recent advancements in the field of fatigue detection. Further, we categorized the methods that can be used to detect fatigue into four diverse groups, that is, mathematical models, rule‐based implementation, ML, and deep learning. This study presents, compares, and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue. Finally, the paper discusses the possible areas for improvement. John Wiley and Sons Inc. 2022-02-24 /pmc/articles/PMC9128560/ /pubmed/35620159 http://dx.doi.org/10.1016/j.cdtm.2021.07.002 Text en © 2022 The Authors. Chronic Diseases and Translational Medicine published by John Wiley & Sons Ltd on behalf of Chinese Medical Association https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Reviews Hooda, Rohit Joshi, Vedant Shah, Manan A comprehensive review of approaches to detect fatigue using machine learning techniques |
title | A comprehensive review of approaches to detect fatigue using machine learning techniques |
title_full | A comprehensive review of approaches to detect fatigue using machine learning techniques |
title_fullStr | A comprehensive review of approaches to detect fatigue using machine learning techniques |
title_full_unstemmed | A comprehensive review of approaches to detect fatigue using machine learning techniques |
title_short | A comprehensive review of approaches to detect fatigue using machine learning techniques |
title_sort | comprehensive review of approaches to detect fatigue using machine learning techniques |
topic | Reviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9128560/ https://www.ncbi.nlm.nih.gov/pubmed/35620159 http://dx.doi.org/10.1016/j.cdtm.2021.07.002 |
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