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

Rohit Hooda; Vedant Joshi; Manan Shah

doi:10.1016/j.cdtm.2021.07.002

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

Chronic Dis Transl Med. 2022 Feb 24;8(1):26-35. doi: 10.1016/j.cdtm.2021.07.002. eCollection 2022 Mar.

Authors

Rohit Hooda¹, Vedant Joshi², Manan Shah³

Affiliations

¹ Gandhinagar Institute of Technology, Gujarat Technological University Gandhinagar Gujarat India.
² LJ Institute of Engineering and Technology, Gujarat Technological University Ahmedabad Gujarat India.
³ Department of Chemical Engineering School of Technology, Pandit Deendayal Energy University Gandhinagar Gujarat India.

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.

Keywords: deep learning; driver monitoring; fatigue detection; healthcare; machine learning.

Publication types

Review