A comprehensive review of approaches to detect fatigue using machine learning techniques. Issue 1 (24th February 2022)

• Record Type: Journal Article
• Authors: Hooda, Rohit; Joshi, Vedant; Shah, Manan
• Other Names: Hong Tianpei guestEditor.; Wei Rui guestEditor.
• Is Part Of: Chronic diseases and translational medicine. Volume 8:Issue 1(2022)
• Page Start: 26
• Page End: 35
• Publication Date: 2022-02-24
• Subjects: deep learning -- driver monitoring -- fatigue detection -- healthcare -- machine learning; Chronic diseases -- Periodicals; Medicine -- Research -- Periodicals; Chronic diseases; Medicine -- Research; Chronic Disease; Translational Medical Research; Periodicals; Electronic journals; Fulltext; Internet Resources; Periodicals; Periodical; 616.044
• DOI: 10.1016/j.cdtm.2021.07.002
• Languages: English
• ISSNs: 2095-882X

Abstract

Abstract: 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.