Journal ArticleDOI
Real-time physiological and vision monitoring of vehicle driver for non-intrusive drowsiness detection
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TLDR
A non-intrusive drowsy-monitoring system is developed to alert the driver if driver falls into low arousal state where the driver's health and mental states can be monitored in real-time without constraints.Abstract:
This study presents a novel approach to detect driver's drowsiness by applying two distinct methods in computer vision and image processing. The objective of this study is to combine both methods under one single profile instead of relied solely on a detection method to enhance the driver's drowsiness detection resolution. Therefore a non-intrusive drowsy-monitoring system is developed to alert the driver if driver falls into low arousal state. In physiological part, photoplethysmography (PPG) is analysed for its changes in signals waveform from awake to drowsy state. Meanwhile, eyes pattern or motion in image processing is addressed to detect driver fatigue. Genetic algorithm with template-matching approach is designed to detect eye region and estimate the drowsiness in different metric standard based on eyes behaviour. Moreover, PPG drowsy signals are integrated with eyes motion to derive the final probability model for delivering valid and reliable drowsiness detection system. Indeed, the proposed system provides high competitive edge over existing arbitrary drowsiness detection system where the driver's health and mental states can be monitored in real-time without constraints.read more
Citations
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Journal ArticleDOI
Detecting driver drowsiness based on sensors: a review.
TL;DR: It is concluded that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsy level of a driver.
Journal ArticleDOI
Heart Rate Variability-Based Driver Drowsiness Detection and Its Validation With EEG
Koichi Fujiwara,Erika Abe,Keisuke Kamata,Chikao Nakayama,Yoko Suzuki,Toshitaka Yamakawa,Toshihiro Hiraoka,Manabu Kano,Yukiyoshi Sumi,Fumi Masuda,Masahiro Matsuo,Hiroshi Kadotani +11 more
TL;DR: A driver drowsiness detection algorithm based on heart rate variability (HRV) analysis is proposed and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring and demonstrates the usefulness of the framework of HRV-based anomaly detection.
Journal ArticleDOI
A Hybrid Scheme for Drowsiness Detection Using Wearable Sensors
TL;DR: The results show that the accuracy of drowsiness detection is improved by using a hybrid feature vector acquired from data using multiple sensors.
Journal ArticleDOI
Review on computer vision techniques in emergency situations
Laura Lopez-Fuentes,Joost van de Weijer,Manuel González-Hidalgo,Harald Skinnemoen,Andrew D. Bagdanov +4 more
TL;DR: In this article, the authors provide a broad overview of the progress of computer vision covering all sorts of emergencies, focusing on state-of-the-art systems that cover the same emergency as they are studying, obviating important research in other fields.
Journal ArticleDOI
Heart Rate and Heart Rate Variability From Single-Channel Video and ICA Integration of Multiple Signals
TL;DR: The results support the conclusion that proposed ICA pre-processing can effectively improve the HR and HRV assessment from iPPG.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Reference EntryDOI
Principal Component Analysis
TL;DR: Principal component analysis (PCA) as discussed by the authors replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables.
Journal ArticleDOI
A critical review of the psychophysiology of driver fatigue.
Saroj K.L. Lal,Ashley Craig +1 more
TL;DR: Monitoring electroencephalography during driver fatigue may be a promising variable for use in fatigue countermeasure devices and understanding the psychology of fatigue may lead to better fatigue management.
Journal ArticleDOI
Real-time nonintrusive monitoring and prediction of driver fatigue
Qiang Ji,Zhiwei Zhu,P. Lan +2 more
TL;DR: A probabilistic model is developed to model human fatigue and to predict fatigue based on the visual cues obtained, and it was found to be reasonably robust, reliable, and accurate in fatigue characterization.