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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MDPI AG
2021-07-01
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doaj-d224fe06767348fc95a74ec0d458dc122021-07-23T14:05:54ZengMDPI AGSensors1424-82202021-07-01214833483310.3390/s21144833Non-Invasive Driver Drowsiness Detection SystemHafeez Ur Rehman Siddiqui0Adil Ali Saleem1Robert Brown2Bahattin Bademci3Ernesto Lee4Furqan Rustam5Sandra Dudley6Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, PakistanFaculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, PakistanSchool of Engineering, London South Bank University, London SE1 0AA, UKSchool of Engineering, London South Bank University, London SE1 0AA, UKDepartment of Computer Science, Broward College, Broward County, FL 33332, USAFaculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, PakistanSchool of Engineering, London South Bank University, London SE1 0AA, UKDrowsiness 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.https://www.mdpi.com/1424-8220/21/14/4833drowsiness detectionrespiration ratephysiological signalsmachine learningultra-wideband |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hafeez Ur Rehman Siddiqui Adil Ali Saleem Robert Brown Bahattin Bademci Ernesto Lee Furqan Rustam Sandra Dudley |
spellingShingle |
Hafeez Ur Rehman Siddiqui Adil Ali Saleem Robert Brown Bahattin Bademci Ernesto Lee Furqan Rustam Sandra Dudley Non-Invasive Driver Drowsiness Detection System Sensors drowsiness detection respiration rate physiological signals machine learning ultra-wideband |
author_facet |
Hafeez Ur Rehman Siddiqui Adil Ali Saleem Robert Brown Bahattin Bademci Ernesto Lee Furqan Rustam Sandra Dudley |
author_sort |
Hafeez Ur Rehman Siddiqui |
title |
Non-Invasive Driver Drowsiness Detection System |
title_short |
Non-Invasive Driver Drowsiness Detection System |
title_full |
Non-Invasive Driver Drowsiness Detection System |
title_fullStr |
Non-Invasive Driver Drowsiness Detection System |
title_full_unstemmed |
Non-Invasive Driver Drowsiness Detection System |
title_sort |
non-invasive driver drowsiness detection system |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-07-01 |
description |
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. |
topic |
drowsiness detection respiration rate physiological signals machine learning ultra-wideband |
url |
https://www.mdpi.com/1424-8220/21/14/4833 |
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