A comprehensive review of approaches to detect fatigue using machine learning techniques

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...

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Bibliographic Details
Published in:Chronic diseases and translational medicine Vol. 8; no. 1; pp. 26 - 35
Main Authors: Hooda, Rohit, Joshi, Vedant, Shah, Manan
Format: Journal Article
Language:English
Published: United States Elsevier B.V 01-03-2022
John Wiley & Sons, Inc
John Wiley and Sons Inc
Wiley
Subjects:
Algorithms
Artificial intelligence
Brain research
Chronic fatigue syndrome
Comparative analysis
Deep learning
Driver monitoring
Electroencephalography
Fatigue detection
Healthcare
Machine learning
Mathematical models
Methods
Multiple sclerosis
Neural networks
Psychobiology
Review
Reviews
Roads & highways
Sleep
Deep learning
Fatigue detection
Healthcare
Driver monitoring
Machine learning
deep learning
driver monitoring
fatigue detection
machine learning
healthcare
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Summary: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, i.e., 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.
Bibliography:Edited by Yi Cui
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-3
content type line 23
ObjectType-Review-1
ISSN:2095-882X
2589-0514
2589-0514
DOI:10.1016/j.cdtm.2021.07.002