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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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Automatic Driver Drowsiness Detection Using Artificial Neural Network Based on Visual Facial Descriptors: Pilot

Papangkorn Inkeaw1, Pimwarat Srikummoon2,3, Jeerayut Chaijaruwanich1,4

  • 1Data Science Research Center, Department of Computer Science, Faculty of Science, Chiang Mai University, Chiang Mai, 50200, Thailand.

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This study developed a novel drowsiness detection system using artificial neural networks (ANN) and facial features. The system shows promise for improving driver alertness and reducing accidents.

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Transportation Safety

Background:

  • Drowsy driving is a significant global cause of traffic accidents.
  • Current drowsiness detection technologies face limitations in reliability, practicality, and clinical validation.

Purpose of the Study:

  • To develop an early drowsiness detection algorithm and device.
  • To utilize brain biophysiological signals and facial expression data for drowsiness detection.

Main Methods:

  • Employed artificial neural networks (ANN) with composite facial features: eye aspect ratio (EAR), mouth aspect ratio (MAR), face length (FL), and face width balance (FWB).
  • Investigated feature extraction from two-second video frames and applied discrete Fourier transform (DFT) to composite features.

Main Results:

  • The ANN model combined with EAR and MAR showed the highest sensitivity (70.12%).
  • The ANN model with EAR, MAR, and FL achieved the highest accuracy (60.76%) and specificity (58.71%).
  • Applying DFT improved sensitivity (72.25% with EAR and MAR) and accuracy/specificity (60.40%/54.10% with EAR, MAR, and FL).

Conclusions:

  • The ANN model integrating DFT, EAR, MAR, and FL demonstrated optimal performance for drowsiness detection.
  • The developed system offers a valuable algorithm for monitoring driver alertness levels.