چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده | نفر چندم مقاله |
|---|---|
| احسان سربازی | دوم |
| صنوبر نادریان | سوم |
| حسن سلیمانپور | پنجم |
| عنوان | متن |
|---|---|
| کلمات کلیدی | Accidents, Machine Learning, Deep Learning, Fatigue, Epidemiology |
| خلاصه مقاله | Driver drowsiness detection is crucial for improving road safety and reducing accidents caused by driver exhaustion. It has been identified as a crucial element in safety of crashes, which can result in fatalities, serious injuries, and significant financial losses. New advancements in Machine Learning (ML) have aided in tracking drivers and alerting them if they aren't focused on driving. This study aims to discover all empirical research relevant to predicting driver drowsiness using behavioral measurements by ML approaches. The present research thoroughly reviewed the literature for driver drowsiness detection published in English and included in PubMed, Scopus, Web of Science, and IEEE databases up to March 2023. We picked 82 publications out of 562 employing the Preferred Reporting Items for Reviews. In order to warn a driver before a collision, this analysis will concentrate on what happens while driving and the advancement of technological methods that are intended to detect and, ideally, forecast driver drowsiness. For upcoming researchers to do baseline assessment in the particular field, this thorough review will provide a better understanding. We have presented comprehensive challenges and future recommendations from the gaps identified in discussions. It is impossible to compare the results because different authors utilized different standards for signal capture, feature extraction, target labeling, and classification. |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| Drivers.pdf | 1404/10/14 | 55491 | دانلود |
| Certificate.pdf | 1404/10/14 | 325957 | دانلود |
| Plan.pdf | 1404/10/14 | 3520263 | دانلود |
