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Computers have become to play an important role in people's lives throughout the world thus increasing the number of patients with ocular complaints. Also known as computer vision syndrome, accommodative asthenopia might be considered as a neglected occupational condition, but there is no consensus regarding diagnostic criteria, which leads to under-recognition and under-reporting. The aim of the study was to investigate the prevalence, early symptoms and specific preventive measures of accommodative asthenopia among computer-using medical students (n = 420), in a cross-sectional study. The most common early symptoms were: tired eyes/eye strain (86.1%), burning eyes (46.8%), migraines/headaches (46.8%), red-swollen eyes (43%), photophobia (41.8%), temporary blurred vision (35.4%), and eyelid spasms (32.9%). Our study identifies the most frequent early symptoms of accommodative asthenopia as a possible occupational disease, which might serve as a fundament for a future consensus strategy. [ABSTRACT FROM AUTHOR]

This paper presents a nonintrusive prototype computer vision system for real-time fatigue driving detection. First, we use Haar-like features to detect a driver’s face and conduct tracking by introducing an improved Camshift algorithm. Second, we propose a new eye-detection algorithm that combines the Adaboost algorithm with template matching to reduce computational costs and add an eye-validation process to increase the accuracy of the detection rate. Third, and different from other methods focusing on detecting eyes using the ‘bright pupil’ effect, which only works well only for certain constrained lighting conditions, our method detects and estimates the iris center in the hue (H) channel of the hue, saturation, value color space and fits the iris with an ellipse. After extracting the eye fatigue features, we calculate the PERCLOS measurement for fatigue evaluation. This system has been tested on the IMM Face Database, which contains more than 200 faces, and in a real-time test. The experimental results show that the system possesses good accuracy and robustness. [ABSTRACT FROM AUTHOR]