A Survey on Drowsiness Detection -- Modern Applications and Methods
Biying Fu, Fadi Boutros, Chin-Teng Lin, Naser Damer

TL;DR
This survey comprehensively reviews modern methods and applications of drowsiness detection, highlighting challenges, technological advancements, and practical recommendations to improve accuracy, real-time performance, and bias mitigation across various sectors.
Contribution
It provides an extensive overview of drowsiness detection techniques beyond driving, identifying research gaps and proposing solutions like synthetic data and model fusion for enhanced performance.
Findings
Current algorithms face accuracy and real-time detection challenges
Bias in datasets limits system fairness and effectiveness
Fusion techniques can improve detection performance
Abstract
Drowsiness detection holds paramount importance in ensuring safety in workplaces or behind the wheel, enhancing productivity, and healthcare across diverse domains. Therefore accurate and real-time drowsiness detection plays a critical role in preventing accidents, enhancing safety, and ultimately saving lives across various sectors and scenarios. This comprehensive review explores the significance of drowsiness detection in various areas of application, transcending the conventional focus solely on driver drowsiness detection. We delve into the current methodologies, challenges, and technological advancements in drowsiness detection schemes, considering diverse contexts such as public transportation, healthcare, workplace safety, and beyond. By examining the multifaceted implications of drowsiness, this work contributes to a holistic understanding of its impact and the crucial role of…
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Taxonomy
TopicsSleep and Work-Related Fatigue
MethodsFocus
