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Narcolepsy01:07

Narcolepsy

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Narcolepsy is a chronic sleep disorder characterized by pervasive, uncontrolled sleepiness and other sleep disturbances. One of its hallmark symptoms is an abrupt transition to REM sleep upon falling asleep, which causes symptoms typically associated with this phase to occur unexpectedly during wakefulness. These include the following symptoms, which typically last from a minute or two to half an hour.
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Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features.

Yaman Albadawi1, Aneesa AlRedhaei2, Maen Takruri3

  • 1Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates.

Journal of Imaging
|May 26, 2023
PubMed
Summary
This summary is machine-generated.

This study presents a non-invasive system for real-time driver drowsiness detection using visual features. The system achieves up to 99% accuracy in identifying drowsy drivers, enhancing road safety.

Keywords:
driver drowsiness detectioneye aspect ratiohead pose estimationmouth aspect ratio

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Road Safety

Background:

  • Drowsiness-related car accidents pose a significant threat to road safety.
  • Early detection and alerting of drowsy drivers can prevent numerous accidents.

Purpose of the Study:

  • To develop a non-invasive system for real-time driver drowsiness detection.
  • To utilize visual features extracted from facial landmarks and head pose for accurate detection.

Main Methods:

  • A system employing facial landmarks and face mesh detectors to extract visual features.
  • Extraction of mouth aspect ratio, eye aspect ratio, and head pose features.
  • Classification using random forest, sequential neural network, and linear support vector machine models.

Main Results:

  • The proposed system achieved high accuracy in detecting drowsy drivers.
  • Evaluations on the National Tsing Hua University dataset demonstrated up to 99% accuracy.
  • Successful real-time detection and alarming of drowsy drivers.

Conclusions:

  • The developed system offers an effective solution for real-time driver drowsiness detection.
  • Visual feature-based analysis combined with machine learning classifiers can significantly improve road safety.
  • The non-invasive approach provides a practical method for driver monitoring.