文章基本信息

标题：Modern drowsiness detection techniques: a review
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作者：Sarah Saadoon Jasim ; Alia Karim Abdul Hassan
期刊名称：International Journal of Electrical and Computer Engineering
电子版ISSN：2088-8708
出版年度：2022
卷号：12
期号：3
页码：2986-2995
DOI：10.11591/ijece.v12i3.pp2986-2995
语种：English
出版社：Institute of Advanced Engineering and Science (IAES)
摘要：According to recent statistics, drowsiness, rather than alcohol, is now responsible for one-quarter of all automobile accidents. As a result, many monitoring systems have been created to reduce and prevent such accidents. However, despite the huge amount of state-of-the-art drowsiness detection systems, it is not clear which one is the most appropriate. The following points will be discussed in this paper: Initial consideration should be given to the many sorts of existing supervised detecting techniques that are now in use and grouped into four types of categories (behavioral, physiological, automobile and hybrid), Second, the supervised machine learning classifiers that are used for drowsiness detection will be described, followed by a discussion of the advantages and disadvantages of each technique that has been evaluated, and lastly the recommendation of a new strategy for detecting drowsiness.
关键词：identification of fatigue classification;machine learning classifiers;optical image processing driver drowsiness sensors