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An Electro-Oculogram (EOG) Sensor's Ability to Detect Driver Hypovigilance Using Machine Learning.

Suganiya Murugan1, Pradeep Kumar Sivakumar2, C Kavitha3

  • 1Department of Computing Technologies, SRM Institute of Science and Technology-KTR, Chennai 603203, India.

Sensors (Basel, Switzerland)
|March 30, 2023
PubMed
Summary
This summary is machine-generated.

Monitoring driver electrooculography (EOG) signals can detect hypovigilance, enhancing driving safety. Machine learning models achieved up to 98.7% accuracy in identifying drowsiness and inattention, improving accident prevention.

Keywords:
drowsinessdrowsiness detectionmachine learningsignalsvisual inattention

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Machine Learning

Background:

  • Driver safety is paramount, necessitating proactive monitoring of physical state over vehicle-based metrics.
  • Physiological signals like electroencephalography (EEG) and electrooculography (EOG) offer reliable insights into driver alertness.

Purpose of the Study:

  • To detect driver hypovigilance, encompassing drowsiness, fatigue, and inattention, using electrooculography (EOG) signals.
  • To evaluate the efficacy of machine learning algorithms in classifying driver states.

Main Methods:

  • EOG signals from 10 drivers were collected during driving, preprocessed, and 17 features were extracted.
  • Feature selection using ANOVA and dimensionality reduction via Principal Component Analysis (PCA).
  • Classification using Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and ensemble methods.

Main Results:

  • A maximum accuracy of 98.7% was achieved for two-class detection (normal vs. cognitive states).
  • For five-class hypovigilance detection, the maximum accuracy was 90.9%, with increased classes reducing overall accuracy.
  • The ensemble classifier demonstrated superior performance compared to SVM and KNN.

Conclusions:

  • EOG signal analysis combined with machine learning is a viable method for detecting driver hypovigilance.
  • The ensemble classifier shows promise for real-time driver monitoring systems.
  • Accurate detection of hypovigilance states can significantly contribute to road safety and accident prevention.