Intelligent Standalone Eye Blinking Monitoring System for Computer Users

Abstract

Purpose: 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. Methods: 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. Results: 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. Conclusions: 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.

Keywords: attention; blink; computer; computer vision syndrome; eye; gaze; monitor; new media; reading; screen.

Figure 1.

Block diagram of the eye blinking monitoring system.

Figure 2.

Eye blinking monitoring system and evaluation study setup. a) Design of the eye blinking monitoring system device. b) Experimental setup for the evaluation study.

Figure 3.

Bland-Altman plot for the blinking monitoring (BM) system and actual blink counts. The mean difference (bias) was −1.7 blinks. The negative sign indicates that on average, the blink counts of the blinking monitoring system was lower than the actual blink counts. The 95% limits of agreement (mean ± 1.96 standard deviation) were −5.3 and 1.9 blinks.

Figure 4.

Eye blinking rates for all the subjects in a spontaneous (conversational) condition and tasks of watching movie trailers and reading articles without the alarm. Eye blinking rates for the subjects during the tasks of watching movie trailers and reading articles were significantly lower than spontaneous eye blinking rates (Wilcoxon signed rank test, α = 0.017 after Bonferroni correction, * = p < 0.001). The data points connected by a line are the blinking rates from a single subject.

Figure 5.

Effect of blinking monitoring system alarm on eye blinking rates. Eye blinking rates during the task of a) watching movie trailers, b) reading articles and c) both tasks without and with the alarm function. The data points connected by a line are the blinking rates from a single subject.