Abstract
In this chapter, our objective is to detect the driver fatigue state. To this end, we have integrated the most relevant causes and effects of fatigue in a dynamic Bayesian network. We used the following as the main causes of drowsiness: sleep quality, road environment, and driving duration. On the other hand, we added as consequences real-time facial expressions, such as blinking, yawning, gaze, and head position. The result obtained changes over time and it is repeatedly included in the model to calculate fatigue level. In comparison with a realistic simulation, this model is very effective at detecting driver fatigue.
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Bani, I., Akrout, B., Mahdi, W. (2019). Real-Time Driver Fatigue Monitoring with a Dynamic Bayesian Network Model. In: Chaari, L. (eds) Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine. Advances in Predictive, Preventive and Personalised Medicine, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-11800-6_8
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DOI: https://doi.org/10.1007/978-3-030-11800-6_8
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