Real-time nonintrusive monitoring and prediction of driver fatigue

By Qiang Ji, Zhiwei Zhu, Peilin Lan. IEEE Transactions on Vehicular Technology, 2004

803 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/857a63f62f00620f0f04b2f4092bb5d142dfdf9b

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In Biomedical Engineering

In Experimental and Cognitive Psychology

In Human-Computer Interaction

Citations in the corpus

Driver Fatigue Detection Systems: A Review (2019, background): “incorporated in [63] to predict fatigue however, SBN offers relatively lesser suitability for systems that change over time.”
A multimodal approach to estimating vigilance using EEG and forehead EOG (2016, background): “Various approaches for estimating vigilance levels have been proposed in the literature (Ji, Zhu & Lan 2004, Dong, Hu, Uchimura & Murayama 2011, Sahayadhas, Sundaraj & Murugappan 2012).”
Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm (2011, background): “Most of these studies reported that the physiological approach to drowsiness detection can provide very accurate results as strong correlation between these signals and the driver’s cognitive state was found in many studies [9], [13].”
End-to-End Multimodal Emotion Recognition Using Deep Neural Networks (2017, background): “For instance, emotion states can be used to monitor and predict fatigue state [1].”
Online Driver Distraction Detection Using Long Short-Term Memory (2011, background): “A considerable number of studies concentrate on the detection and modeling of fatigue as an important cause for inattention (e. g. [15]–[17]).”
Eyeblink-based Anti-Spoofing in Face Recognition from a Generic Webcamera (2007, neutral): “Ji et al [18] have attempted to use an active IR camera to detect eyeblinks for prediction of driver fatigue.”
EEG-based drowsiness estimation for safety driving using independent component analysis (2005, background): “One focuses on detecting physical changes during drowsiness by image processing techniques, such as average of eye-closure speed, percentage of eye-closure over time, eye tracking as quantization of drowsiness level, and driver’s head movements [8]–[16].”