Non-Invasive Driver Drowsiness Detection System

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...

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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 21; no. 14; p. 4833
Main Authors Siddiqui, Hafeez Ur Rehman, Saleem, Adil Ali, Brown, Robert, Bademci, Bahattin, Lee, Ernesto, Rustam, Furqan, Dudley, Sandra
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 15.07.2021
MDPI
Subjects
Accuracy
Bandwidths
Data collection
Datasets
drowsiness detection
Electrocardiography
Fatigue
Fourier transforms
Heart rate
machine learning
Noise
physiological signals
Physiology
Respiration
respiration rate
Skin
Sleep
Spectrum allocation
Standard deviation
ultra-wideband
United States > US
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Summary: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s21144833