Early Detection of Driver Drowsiness Utilizing Machine Learning based on Physiological Signals, Behavioral Measures, and Driving Performance-Reference-Cited by-同舟云学术

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Early Detection of Driver Drowsiness Utilizing Machine Learning based on Physiological Signals, Behavioral Measures, and Driving Performance

Published:2018-11 Issue: Volume: Page:
ISSN:
Container-title:2018 21st International Conference on Intelligent Transportation Systems (ITSC)
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Author:

Gwak Jongseong,Shino Motoki,Hirao Akinari

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8543039/8569013/08569493.pdf?arnumber=8569493

Cited by 46 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

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2. Monitoring fatigue and drowsiness in motor vehicle occupants using electrocardiogram and heart rate − A systematic review;Transportation Research Part F: Traffic Psychology and Behaviour;2024-05

3. Comparative Study: Physiological-based Driver Drowsiness Detection Utilizing Traditional and Hybrid Methods;2024 8th International Conference on Image and Signal Processing and their Applications (ISPA);2024-04-21

4. Role of machine learning and deep learning techniques in EEG-based BCI emotion recognition system: a review;Artificial Intelligence Review;2024-02-13

5. Studying Drowsiness Detection Performance While Driving Through Scalable Machine Learning Models Using Electroencephalography;Cognitive Computation;2024-01-11

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