Abstract
This study evaluates the progression of visual fatigue induced by visual display terminal (VDT) using automatically detected blink features. A total of 23 subjects were recruited to participate in a VDT task, during which they were required to watch a 120-min video on a laptop and answer a questionnaire every 30 min. Face video recordings were captured by a camera. The blinking and incomplete blinking images were recognized by automatic detection of the parameters of the eyes. Then, the blink features were extracted including blink number (BN), mean blink interval (Mean_BI), mean blink duration (Mean_BD), group blink number (GBN), mean group blink interval (Mean_GBI), incomplete blink number (IBN), and mean incomplete blink interval (Mean_IBI). The results showed that BN and GBN increased significantly, and that Mean_BI and Mean_GBI decreased significantly over time. Mean_BD and Mean_IBI increased and IBN decreased significantly only in the last 30 min. The blink features automatically detected in this study can be used to evaluate the progression of visual fatigue.
Highlights
The present study aims to evaluate the progression of visual fatigue with blink features
We propose an algorithm to identify blink and incomplete blink and extract the blink features at different stages of the visual display terminal (VDT) task
group blink number (GBN) is the number of group blinks whose blink interval is less than one second
Summary
The experiment was set up to record the face video during a VDT task. The eyes were located to extract blinking image frames and recognize complete and incomplete blinks. The blink features were extracted and their changes with visual fatigue were analyzed statistically. The process of blink features extraction was summarized in Algorithm 1, where n is the total video frames during an experiment.
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