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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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Multi-body sensor based drowsiness detection using convolutional programmed transfer VGG-16 neural network with

Meenakshi Malik1, Preeti Sharma2, Gurpreet Kaur Punj3

  • 1Department of CSE, BML Munjal University, Gurugram, India.

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|March 15, 2025
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Summary
This summary is machine-generated.

Driver sleepiness causes accidents. This study introduces a machine learning method using EEG signals and VGG-16 neural networks to detect drowsiness, enabling automatic driving mode changes to enhance road safety.

Keywords:
Automatic drivingDrowsiness detectionEEG signalFrequency transform modelMachine learning modelMulti-body sensor

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Area of Science:

  • Neuroscience
  • Computer Science
  • Automotive Engineering

Background:

  • Driver sleepiness is a major cause of traffic accidents.
  • Advanced Driver Assistance Systems (ADAS) increasingly utilize Artificial Intelligence (AI) and Machine Learning (ML).
  • Internet-of-Things (IoT) technology is crucial for real-time driver monitoring.

Purpose of the Study:

  • To develop an automated drowsiness detection system for drivers.
  • To enable automatic transition to driving mode based on fatigue detection.
  • To improve road safety and reduce fatigue-related incidents.

Main Methods:

  • Utilized multi-body sensors to capture electroencephalogram (EEG) signals for brain activity analysis.
  • Applied wavelet time-frequency transform to analyze EEG signals and classify brain activity patterns.
  • Employed a VGG-16 neural network for classifying the analyzed brain activity patterns.

Main Results:

  • Experimental analysis performed on various EEG signal datasets.
  • Evaluated prediction accuracy, sensitivity, specificity, Root Mean Square Error (RMSE), and Receiver Operating Characteristic (ROC) curves.
  • Demonstrated the feasibility of machine learning for automatic driving mode changes based on drowsiness detection.

Conclusions:

  • The proposed method effectively detects driver drowsiness using EEG signals.
  • Automatic driving mode transition can mitigate risks associated with drowsy driving.
  • This technology has the potential to significantly improve overall road safety.