Monitoring fatigue and drowsiness in motor vehicle occupants using electrocardiogram and heart rate − A systematic review
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Published:2024-05
Issue:
Volume:103
Page:586-607
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ISSN:1369-8478
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Container-title:Transportation Research Part F: Traffic Psychology and Behaviour
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language:en
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Short-container-title:Transportation Research Part F: Traffic Psychology and Behaviour
Author:
Freitas Alícia,
Almeida RuteORCID,
Gonçalves Hernâni,
Conceição Glória,
Freitas Alberto
Reference110 articles.
1. Abbas (2020a). FatigueAlert: A real-time fatigue detection system using hybrid features and Pre-train mCNN model. International Journal of Computer Science and Network Security.
2. Abbas. (2020b). HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning HybridFatigue: Driver Fatigue detection by Abbas Q. International Journal of Advanced Computer Science and Applications.
3. Abtahi, F., Anund, A., Fors, C., Seoane, F., & Lindecrantz, K. (2018). Association of Drivers’ sleepiness with heart rate variability: A Pilot Study with Drivers on Real Roads. In Embec & Nbc 2017 (pp. 149-152). doi: 10.1007/978-981-10-5122-7_38.
4. Investigating the effects of sleepiness in truck drivers on their headway: An instrumental variable model with grouped random parameters and heterogeneity in their means;Afghari;Analytic Methods in Accident Research,2022
5. Aghajarian, M., Darzi, A., McInroy, J. E., & Novak, D. (2019). A New Method for Classification of Hazardous Driver States Based on Vehicle Kinematics and Physiological Signals. In Intelligent Human Systems Integration 2019 (pp. 63-68). doi: 10.1007/978-3-030-11051-2_10.