A driver fatigue recognition model based on information fusion and dynamic Bayesian network. Yang, G., Lin, Y., & Bhattacharya, P. Information Sciences, 180(10):1942-1954, 5, 2010. ![A driver fatigue recognition model based on information fusion and dynamic Bayesian network [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex We propose a driver fatigue recognition model based on the dynamic Bayesian network, information fusion and multiple contextual and physiological features. We include features such as the contact physiological features (e.g., ECG and EEG), and apply the first-order Hidden Markov Model to compute the dynamics of the Bayesian network at different time slices. The experimental validation shows the effectiveness of the proposed system; also it indicates that the contact physiological features (especially ECG and EEG) are significant factors for inferring the fatigue state of a driver.
@article{
title = {A driver fatigue recognition model based on information fusion and dynamic Bayesian network},
type = {article},
year = {2010},
keywords = {Contextual features,Driver fatigue recognition,Dynamic Bayesian network,Information fusion,Physiological features},
pages = {1942-1954},
volume = {180},
websites = {http://www.sciencedirect.com/science/article/pii/S0020025510000253},
month = {5},
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abstract = {We propose a driver fatigue recognition model based on the dynamic Bayesian network, information fusion and multiple contextual and physiological features. We include features such as the contact physiological features (e.g., ECG and EEG), and apply the first-order Hidden Markov Model to compute the dynamics of the Bayesian network at different time slices. The experimental validation shows the effectiveness of the proposed system; also it indicates that the contact physiological features (especially ECG and EEG) are significant factors for inferring the fatigue state of a driver.},
bibtype = {article},
author = {Yang, Guosheng and Lin, Yingzi and Bhattacharya, Prabir},
doi = {10.1016/j.ins.2010.01.011},
journal = {Information Sciences},
number = {10}
}
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