Non-Invasive Driver Drowsiness Detection System

Sensors

Ernesto Lee¹  Furqan Rustam²  Hafeez Ur Rehman Siddiqui²  Adil Ali Saleem²  Sandra Dudley³  Bahattin Bademci³  Robert Brown³ 

[1] Department of Computer Science, Broward College, Broward County, FL 33332, USA;Faculty of Computer Science and Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan;School of Engineering, London South Bank University, London SE1 0AA, UK;

关键词: drowsiness detection;    respiration rate;    physiological signals;    machine learning;    ultra-wideband;   

DOI  :  10.3390/s21144833

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown