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面向计算机用户的智能独立式眨眼监测系统。

Intelligent Standalone Eye Blinking Monitoring System for Computer Users.

作者信息

Jiman Ahmad A, Abdullateef Amjad J, Almelawi Alaa M, Yunus Khan M, Kadah Yasser M, Turki Ahmad F, Abdulaal Mohammed J, Sobahi Nebras M, Attar Eyad T, Milyani Ahmad H

机构信息

Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, Saudi Arabia.

Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah, Saudi Arabia.

出版信息

J Eye Mov Res. 2024 Dec 6;17(5). doi: 10.16910/jemr.17.5.1. eCollection 2024.

DOI:10.16910/jemr.17.5.1
PMID:39877121
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11744486/
Abstract

: Working on computers for long hours has become a regular task for millions of people around the world. This has led to the increase of eye and vision issues related to prolonged computer use, known as computer vision syndrome (CVS). A main contributor to CVS caused by dry eyes is the reduction of blinking rates. In this pilot study, an intelligent, standalone eye blinking monitoring system to promote healthier blinking behaviors for computer users was developed using components that are affordable and easily available in the market. : The developed eye blinking monitoring system used a camera to track blinking rates and operated audible, visual and tactile alarm modes to induce blinks. The hypothesis in this study is that the developed eye blinking monitoring system would increase eye blinks for a computer user. To test this hypothesis, the developed system was evaluated on 20 subjects. : The eye blinking monitoring system detected blinks with high accuracy (95.9%). The observed spontaneous eye blinking rate was 43.1 ± 14.7 blinks/min (mean ± standard deviation). Eye blinking rates significantly decreased when the subjects were watching movie trailers (25.2 ± 11.9 blinks/min; Wilcoxon signed rank test; p<0.001) and reading articles (24.2 ± 12.1 blinks/min; p<0.001) on a computer. The blinking monitoring system with the alarm function turned on showed an increase in blinking rates (28.2 ± 12.1 blinks/min) compared to blinking rates without the alarm function (25.2 ± 11.9 blinks/min; p=0.09; Cohen's effect size d=0.25) when the subjects were watching movie trailers. : The developed blinking monitoring system was able to detect blinking with high accuracy and induce blinking with a personalized alarm function. Further work is needed to refine the study design and evaluate the clinical impact of the system. This work is an advancement towards the development of a profound technological solution for preventing CVS.

摘要

长时间使用电脑已成为全球数百万人的日常工作。这导致了与长时间使用电脑相关的眼睛和视力问题的增加,即所谓的电脑视觉综合征(CVS)。由干眼引起的CVS的一个主要因素是眨眼率的降低。在这项初步研究中,使用市场上价格实惠且易于获得的组件,开发了一种智能、独立的眨眼监测系统,以促进电脑用户更健康的眨眼行为。

所开发的眨眼监测系统使用摄像头跟踪眨眼率,并运行声音、视觉和触觉报警模式来诱导眨眼。本研究的假设是,所开发的眨眼监测系统将增加电脑用户的眨眼次数。为了验证这一假设,对20名受试者进行了该系统的评估。

眨眼监测系统检测眨眼的准确率很高(95.9%)。观察到的自发眨眼率为43.1±14.7次/分钟(平均值±标准差)。当受试者在电脑上观看电影预告片(25.2±11.9次/分钟;Wilcoxon符号秩检验;p<0.001)和阅读文章(24.2±12.1次/分钟;p<0.001)时,眨眼率显著下降。当受试者观看电影预告片时,开启报警功能的眨眼监测系统的眨眼率(28.2±12.1次/分钟)与未开启报警功能时的眨眼率(25.2±11.9次/分钟;p=0.09;Cohen效应量d=0.25)相比有所增加。

所开发的眨眼监测系统能够高精度地检测眨眼,并通过个性化报警功能诱导眨眼。需要进一步开展工作来完善研究设计并评估该系统的临床影响。这项工作是朝着开发预防CVS的深度技术解决方案迈出的一步。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f202/11744486/a6d13264ae0c/jemr-17-05-a-figure-05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f202/11744486/2bb15828be45/jemr-17-05-a-figure-01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f202/11744486/fae659168441/jemr-17-05-a-figure-02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f202/11744486/3452bd135765/jemr-17-05-a-figure-03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f202/11744486/b147c3ec4c83/jemr-17-05-a-figure-04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f202/11744486/a6d13264ae0c/jemr-17-05-a-figure-05.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f202/11744486/2bb15828be45/jemr-17-05-a-figure-01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f202/11744486/fae659168441/jemr-17-05-a-figure-02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f202/11744486/3452bd135765/jemr-17-05-a-figure-03.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f202/11744486/b147c3ec4c83/jemr-17-05-a-figure-04.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f202/11744486/a6d13264ae0c/jemr-17-05-a-figure-05.jpg

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本文引用的文献

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Real-Time Blink Detection as an Indicator of Computer Vision Syndrome in Real-Life Settings: An Exploratory Study.实时眨眼检测作为现实生活环境中计算机视觉综合征的指标:一项探索性研究。
Int J Environ Res Public Health. 2023 Mar 4;20(5):4569. doi: 10.3390/ijerph20054569.
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Blinking kinematics characterization during digital displays use.在数字显示使用过程中的眨眼运动学特征描述。
Graefes Arch Clin Exp Ophthalmol. 2022 Apr;260(4):1183-1193. doi: 10.1007/s00417-021-05490-9. Epub 2021 Nov 15.
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Eye Movements during Dynamic Scene Viewing are Affected by Visual Attention Skills and Events of the Scene: Evidence from First-Person Shooter Gameplay Videos.
动态场景观看过程中的眼动受视觉注意力技能和场景事件影响:来自第一人称射击游戏视频的证据。
J Eye Mov Res. 2021 Oct 21;14(2). doi: 10.16910/jemr.14.2.3. eCollection 2021.
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How Do Different Digital Displays Affect the Ocular Surface?不同的数字显示屏如何影响眼表面?
Optom Vis Sci. 2020 Dec;97(12):1070-1079. doi: 10.1097/OPX.0000000000001616.
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Clin Optom (Auckl). 2017 Nov 20;9:133-138. doi: 10.2147/OPTO.S142718. eCollection 2017.
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