Non-Invasive Driver Drowsiness Detection System

Non-Invasive Driver Drowsiness Detection System

  1. Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan
  2. School of Engineering, London South Bank University, London SE1 0AA, UK
  3. Department of Computer Science, Broward College, Broward County, FL 33332, USA
  4. Department of Business Administration, Baker College, Owosso, MI 48867, USA

* Authors to whom correspondence should be addressed.

Academic Editor: Ahmed Toaha Mobashsher
Sensors 2021, 21(14), 4833; https://doi.org/10.3390/s21144833 (registering DOI)
Received: 31 May 2021 / Revised: 12 July 2021 / Accepted: 13 July 2021 / Published: 15 July 2021

Abstract

Drowsiness when in command of a vehicle leads to a decline in cognitive performance that affects driver behavior, potentially causing accidents. Drowsiness-related road accidents lead to severe trauma, economic consequences, impact on others, physical injury and/or even death.

Real-time and accurate driver drowsiness detection and warnings systems are necessary schemes to reduce tiredness-related driving accident rates. The research presented here aims at the classification of drowsy and non-drowsy driver states based on respiration rate detection by non-invasive, non-touch, impulsive radio ultra-wideband (IR-UWB) radar.

Chest movements of 40 subjects were acquired for 5 m using a lab-placed IR-UWB radar system, and respiration per minute was extracted from the resulting signals. A structured dataset was obtained comprising respiration per minute, age and label (drowsy/non-drowsy).

Different machine learning models, namely, Support Vector Machine, Decision Tree, Logistic regression, Gradient Boosting Machine, Extra Tree Classifier and Multilayer Perceptron were trained on the dataset, amongst which the Support Vector Machine shows the best accuracy of 87%.

This research provides a ground truth for verification and assessment of UWB to be used effectively for driver drowsiness detection based on respiration.

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July 23, 2021
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