自动检测眨眼特征评估视频显示终端诱发的视觉疲劳。
Evaluation of VDT-Induced Visual Fatigue by Automatic Detection of Blink Features.
机构信息
Faculty of Environment and Life, Beijing University of Technology, and Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China.
Ophthalmology Department, The University Hospital of Beijing University of Technology, Beijing 100124, China.
出版信息
Sensors (Basel). 2022 Jan 25;22(3):916. doi: 10.3390/s22030916.
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.
本研究使用自动检测的眨眼特征来评估视屏显示终端（VDT）引起的视觉疲劳进展。共有 23 名受试者被招募参与 VDT 任务，在此期间，他们需要在笔记本电脑上观看 120 分钟的视频，并每隔 30 分钟回答一次问卷。面部视频记录由摄像机拍摄。眨眼和不完全眨眼的图像通过眼睛参数的自动检测来识别。然后，提取眨眼特征，包括眨眼次数（BN）、平均眨眼间隔（Mean_BI）、平均眨眼持续时间（Mean_BD）、组眨眼次数（GBN）、平均组眨眼间隔（Mean_GBI）、不完全眨眼次数（IBN）和平均不完全眨眼间隔（Mean_IBI）。结果表明，BN 和 GBN 随时间显著增加，而 Mean_BI 和 Mean_GBI 显著降低。仅在最后 30 分钟，Mean_BD 和 Mean_IBI 增加，IBN 减少。本研究中自动检测到的眨眼特征可用于评估视觉疲劳的进展。
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