Novel Driver's Drowsiness Detection System and its Evaluation in a Driving Simulator Environment

被引：0

作者：

Soleymanpour, Rahim ^{[1
]}

Shishavan, Hossein Hamidi ^{[1
]}

Heo, Jae-Sang ^{[2
]}

Kim, Insoo ^{[3
]}

机构：

[1] Univ Connecticut, Dept Biomed Engn, Storrs, CT 06269 USA

[2] Sungkyunkwan Univ, Sch Adv Mat Sci & Engn, Suwon 16419, South Korea

[3] Univ Connecticut, Dept Med, Farmington, CT 06001 USA

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2021年

关键词：

SLEEPINESS;

D O I：

10.1109/SMC52423.2021.9659264

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Driver's drowsiness is one of the leading causes of traffic accidents. Drowsiness can be detected using brain waves (EEG) because it reflects the cognitive state of the brain. However, using EEG for drowsiness detection in vehicles is still impractical. The non-contact brain signal sensor NBM (Neuro-Bio Monitor), introduced by Freer Logic, has a potential to detect the driver's drowsiness while driving due to its unobtrusive nature of brain signal sensing. In this study, a novel driver drowsiness detection algorithm was introduced using NBM signals. In addition, the feasibility of the NBM-based drowsiness detection algorithm in a driving simulator environment was evaluated by comparison with conventional methods including the eyelid closure ratio (PERCLOS) and the self-reported Karolinska Sleepiness Scale (KSS). The study recruited 14 healthy adults and asked them to drive about 70 minutes on a night drive mode. NBM signals were recorded simultaneously with PERCLOS and KSS. The algorithm shows an accuracy of 78.79% and a detection rate of 95% by comparison with KSS based drowsiness. It also shows a correlation of more than 70% with PERCLOS. This study demonstrated that NBM can be used in vehicle to detect driver drowsiness during driving.

引用

页码：1204 / 1208

页数：5