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Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance.

Andrea Amidei1, Susanna Spinsante2, Grazia Iadarola2

  • 1Dipartimento di Ingegneria "Enzo Ferrari", Università di Modena e Reggio Emilia, Via Pietro Vivarelli 10, 41125 Modena, Italy.

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|April 28, 2023
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Summary

Drowsy driving causes many car accidents. This study shows a wrist device analyzing skin conductance (SC) can detect driver drowsiness with 89.4% accuracy, paving the way for real-time safety alerts.

Keywords:
active assisted livingdriver monitoringdrowsiness detectiongalvanic skin responsemachine learningskin conductancewearable devices

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Area of Science:

  • Biomedical Engineering
  • Transportation Safety
  • Wearable Technology

Background:

  • Drowsy driving is a major cause of global car accidents.
  • Drivers may not recognize their own drowsiness, necessitating objective detection methods.
  • Previous drowsiness detection systems were often large and intrusive.

Purpose of the Study:

  • To investigate the efficacy of a single, comfortable wrist-worn device for detecting driver drowsiness.
  • To analyze the physiological skin conductance (SC) signal for drowsiness indicators.
  • To compare the performance of different ensemble algorithms for drowsiness detection.

Main Methods:

  • Utilized a single wrist-worn device to collect skin conductance (SC) data.
  • Applied signal processing techniques to the SC data.
  • Tested three ensemble algorithms, including Boosting, to classify drowsiness states.

Main Results:

  • The Boosting algorithm achieved the highest accuracy (89.4%) in detecting driver drowsiness.
  • Drowsiness detection was feasible using only the skin conductance signal from the wrist.
  • The study demonstrates the potential of a non-intrusive, single-sensor approach.

Conclusions:

  • A single wrist-worn device analyzing skin conductance is a viable method for detecting driver drowsiness.
  • This approach offers a comfortable and less intrusive alternative to existing systems.
  • Further research is warranted to develop a real-time drowsiness warning system.