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A hybrid approach for driver drowsiness detection utilizing practical data to improve performance system and

Farin Khanehshenas1, Adel Mazloumi1,2, Ali Nahvi3

  • 1Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.

Work (Reading, Mass.)
|November 26, 2023
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Summary

This study developed a practical driver drowsiness detection system using respiratory signals and vehicle data. The system achieved high accuracy, offering a viable solution for real-world applications.

Keywords:
Sleepinessautomobile drivingclassificationmachine learningreaction timerespiration

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Area of Science:

  • Human-Computer Interaction
  • Biomedical Engineering
  • Transportation Safety

Background:

  • Existing driver drowsiness detection systems lack real-time applicability.
  • There is a need for improved, practical solutions for monitoring driver alertness.

Purpose of the Study:

  • To investigate the feasibility of a driver drowsiness detection system.
  • To integrate respiratory signals, vehicle lateral position, and reaction time for enhanced performance.
  • To explore out-of-vehicle data collection methods for real-world applicability.

Main Methods:

  • Collected data from 25 volunteers in a driving simulator.
  • Utilized wearable sensors for respiratory activity and simulator tests for reaction time and vehicle position.
  • Applied statistical tests to identify significant features differentiating alert and drowsy states.
  • Employed machine learning classifiers including Support Vector Machines (SVM) and Long Short-Term Memory (LSTM).

Main Results:

  • The Support Vector Machine (SVM) classifier achieved 88% accuracy.
  • Key performance metrics included 85% precision, 83% recall, and 84% F1 score.
  • Selected features effectively differentiated between alert and drowsy driver states.

Conclusions:

  • The study demonstrates the potential for highly accurate driver drowsiness detection.
  • A practical driver drowsiness system is feasible using combined, less-intrusive measurements.
  • Out-of-vehicle data collection methods enhance system applicability.