Electroencephalogram-Based Approaches for Driver Drowsiness Detection and Management: A Review [PDF]
Sensors, 2022 Drowsiness is not only a core challenge to safe driving in traditional driving conditions but also a serious obstacle for the wide acceptance of added services of self-driving cars (because drowsiness is, in fact, one of the most representative early ...
Gang Li, Wan-Young Chung
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A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems [PDF]
Sensors, 2021 Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system.
Igor Stancin, Mario Cifrek, Alan Jovic
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Trends and Future Prospects of the Drowsiness Detection and Estimation Technology [PDF]
Sensors, 2021 Drowsiness is among the important factors that cause traffic accidents; therefore, a monitoring system is necessary to detect the state of a driver’s drowsiness.
Toshiya Arakawa
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A Systemic Review of Available Low-Cost EEG Headsets Used for Drowsiness Detection [PDF]
Frontiers in Neuroinformatics, 2020 Drowsiness is a leading cause of traffic and industrial accidents, costing lives and productivity. Electroencephalography (EEG) signals can reflect awareness and attentiveness, and low-cost consumer EEG headsets are available on the market.
John LaRocco +2 more
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A Novel Hybrid Approach for Drowsiness Detection Using EEG Scalograms to Overcome Inter-Subject Variability [PDF]
SensorsDrowsiness constitutes a significant risk factor in diverse occupational settings, including healthcare, industry, construction, and transportation, contributing to accidents, injuries, and fatalities.
Aymen Zayed +4 more
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Annotated drowsiness detection dataset captured using Raspberry Pi 5Mendeley Data [PDF]
Data in BriefDrowsiness-related accidents represent a critical safety concern in transportation and workplace environments, necessitating real-time monitoring solutions deployable on affordable hardware.
Suryadiputra Liawatimena, Nugro Isworo
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Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety [PDF]
Sensors, 2023 Drowsy driving can significantly affect driving performance and overall road safety. Statistically, the main causes are decreased alertness and attention of the drivers. The combination of deep learning and computer-vision algorithm applications has been
Furkat Safarov +4 more
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Drowsiness Detection System Based on PERCLOS and Facial Physiological Signal [PDF]
Sensors, 2022 Accidents caused by fatigue occur frequently, and numerous scholars have devoted tremendous efforts to investigate methods to reduce accidents caused by fatigued driving.
Robert Chen-Hao Chang +3 more
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Efficient Generalized Electroencephalography-Based Drowsiness Detection Approach with Minimal Electrodes [PDF]
SensorsDrowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas.
Aymen Zayed +4 more
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Review on Drowsiness Detection [PDF]
EAI Endorsed Transactions on Smart Cities, 2020 This paper relates the street mishaps that happen because of driver's drowsiness. Recent studies state that more disastersare caused due to doziness. Drivers can feel drowsiness due to sleep deprivation, continuously driving, drugs andmedicines, and
Apoorva Apoorva, D Vali, Rakesh R
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