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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.

Scientific Reports
|March 15, 2025
PubMed
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.