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

被引:2
|
作者
Hooda Rohit
Joshi Vedant
Shah Manan
机构
[1] School of Technology
[2] Gandhinagar Institute of Technology
[3] LJ Institute of Engineering and Technology
[4] Pandit Deendayal Energy University
[5] Gandhinagar
[6] Ahmedabad
[7] Department of Chemical Engineering
[8] Gujarat Technological University
[9] India
关键词
deep learning; driver monitoring; fatigue detection; healthcare; machine learning;
D O I
暂无
中图分类号
TP181 [自动推理、机器学习];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:26 / 35
页数:10
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