Tehran University of Medical Sciences Iranian Journal of Public Health 2251-6085 47 9 2018 08 29 1370 1377 Mohsen POURSADEGHIYAN Research Center in Emergency and Disaster Health, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran AND Psychosis Research Center, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran Adel MAZLOUMI Dept. of Occupational Health, School of Public Health, International Campus, Tehran University of Medical Sciences, Tehran, Iran Gebraeil NASL SARAJI Dept. of Occupational Health, School of Public Health, International Campus, Tehran University of Medical Sciences, Tehran, Iran Mohammad Mehdi BANESHI Social Determinants of Health Research Center, Yasuj University of Medical Sciences, Yasuj, Iran Alireza KHAMMAR Dept. of Occupational Health Engineering, School of Health, Zabol University of Medical Sciences, Zabol, Iran Mohammad Hossein EBRAHIMI Occupational and Environmental Health Research Center, Shahroud University of Medical Sciences, Shahroud, Iran 2018 08 29 2018 08 29 Background: Drowsiness is one of the underlying causes of driving accidents, which contribute, to many road fatalities annually. Although numerous methods have been developed to detect the level of drowsiness, techniques based on image processing are quicker and more accurate in comparison with the other methods. The aim of this study was to use image-processing techniques to detect the levels of drowsiness in a driving simulator. Methods: This study was conducted on five suburban drivers using a driving simulator based on virtual reality laboratory of Khaje-Nasir Toosi University of Technology in 2015 Tehran, Iran. The facial expressions, as well as location of the eyes, were detected by Violla-Jones algorithm. Criteria for detecting drivers’ levels of drowsiness by eyes tracking included eye blink duration blink frequency and PERCLOS that was used to confirm the results. Results: Eye closure duration and blink frequency have a direct ratio of drivers’ levels of drowsiness. The mean of squares of errors for data trained by the network and data into the network for testing, were 0.0623 and 0.0700, respectively. Meanwhile, the percentage of accuracy of detecting system was 93. Conclusion: The results showed several dynamic changes of the eyes during the periods of drowsiness. The present study proposes a fast and accurate method for detecting the levels of drivers’ drowsiness by considering the dynamic changes of the eyes.   https://ijph.tums.ac.ir/index.php/ijph/article/view/14593 https://ijph.tums.ac.ir/index.php/ijph/article/download/14593/6119