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EOG-based drowsiness detection: Comparison between a fuzzy system and two supervised learning classifiers

  • Published:2011-01 Issue:1 Volume:44 Page:14283-14288
  • ISSN:1474-6670
  • Container-title:IFAC Proceedings Volumes
  • language:en
  • Short-container-title:IFAC Proceedings Volumes

Author:

Picot Antoine,Charbonnier Sylvie,Caplier Alice

Publisher

Elsevier BV

Subject

General Medicine

Reference15 articles.

1. Real-time system for monitoring driver vigilance;Bergasa;IEEE Transactions on Intelligent Transportation Systems,2006

2. Experimental evaluation of eyeblink parameters as a drowsiness measure;Caffier;European Journal of Applied Physiology,2003

3. A fuzzy expert system for the early warning of accidents due to driver hypo-vigilance;Damousis;Personal and Ubiquitous Computing,2009

4. Blink parameter as indicators of driver's sleepiness-possibilities and limitations;Galley;Vision in Vehicles,2004

5. Sleepiness and performance of professional drivers in a truck simulator-comparisons between day and night driving;Gillberg;Journal of Sleep Research,1996

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1. A Fatigue Detection Method Based on Bi-LSTM With Dual Channel EEG;2024 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA);2024-06-14

2. 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

3. Analysis of Parameters that Characterize Drowsiness Based on EEG, ECG and EOG Records;IFMBE Proceedings;2024

4. EEG-Based Multimodal Emotion Recognition: A Machine Learning Perspective;IEEE Transactions on Instrumentation and Measurement;2024

5. An Electro-Oculogram (EOG) Sensor’s Ability to Detect Driver Hypovigilance Using Machine Learning;Sensors;2023-03-08
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