Abstract
As the use of electronic displays increases rapidly, visual fatigue problems are also increasing. The subjective evaluation methods used for visual fatigue measurement have individual difference problems, while objective methods based on bio-signal measurement have problems regarding motion artifacts. Conventional eye image analysis-based visual fatigue measurement methods do not accurately characterize the complex changes in the appearance of the eye. To solve this problem, in this paper, an objective visual fatigue measurement method based on infrared eye image analysis is proposed. For accurate pupil detection, a convolutional neural network-based semantic segmentation method was used. Three features are calculated based on the pupil detection results: (1) pupil accommodation speed, (2) blink frequency, and (3) eye-closed duration. In order to verify the calculated features, differences in fatigue caused by changes in content color components such as gamma, color temperature, and brightness were compared with a reference video. The pupil detection accuracy was confirmed to be 96.63% based on the mean intersection over union. In addition, it was confirmed that all three features showed significant differences from the reference group; thus, it was verified that the proposed analysis method can be used for the objective measurement of visual fatigue.
Highlights
Many people spend a significant amount of time viewing electronic displays
Based on previous studies and our experimental results, we can conclude that the pupil accommodation speed, the blink frequency, and the eye-closed duration are all appropriate metrics for visual fatigue
We investigated the pupil features suitable for visual fatigue measurement
Summary
Many people spend a significant amount of time viewing electronic displays. With the development of computers and electronic display devices, electronic displays can be seen everywhere. As screen sizes and resolution increase, the amount of information reflected in the eyes, and the movement of the eyes increases. This contributes to the prevalence of eye fatigue. Digital eye fatigue that accompanies computer vision syndrome is related to the use of computers (desktops, laptops, and tablets) and other electronic displays (smartphones and electronic devices) and is experienced by over 40% of adults and over 80% of teens. Previous studies have shown that preschoolers spend up to 2.4 h a day watching electronic screens, which is an indication of the ubiquity of electronic display devices. Eye fatigue problems are increasing and are a central problem in the electronic display field [1]
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