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Real-Time Blink Detection as an Indicator of Computer Vision Syndrome in Real-Life Settings: An Exploratory Study

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  • Inês Lapa

    (Center for Translational Health and Medical Biotechnology Research, School of Health of Polytechnic Institute of Porto, 4200-465 Porto, Portugal
    These authors contributed equally to this work.)

  • Simão Ferreira

    (Center for Translational Health and Medical Biotechnology Research, School of Health of Polytechnic Institute of Porto, 4200-465 Porto, Portugal
    These authors contributed equally to this work.)

  • Catarina Mateus

    (Center for Translational Health and Medical Biotechnology Research, School of Health of Polytechnic Institute of Porto, 4200-465 Porto, Portugal)

  • Nuno Rocha

    (Center for Translational Health and Medical Biotechnology Research, School of Health of Polytechnic Institute of Porto, 4200-465 Porto, Portugal)

  • Matilde A. Rodrigues

    (Center for Translational Health and Medical Biotechnology Research, School of Health of Polytechnic Institute of Porto, 4200-465 Porto, Portugal)

Abstract

With the increase in the number of people using digital devices, complaints about eye and vision problems have been increasing, making the problem of computer vision syndrome (CVS) more serious. Accompanying the increase in CVS in occupational settings, new and unobstructive solutions to assess the risk of this syndrome are of paramount importance. This study aims, through an exploratory approach, to determine if blinking data, collected using a computer webcam, can be used as a reliable indicator for predicting CVS on a real-time basis, considering real-life settings. A total of 13 students participated in the data collection. A software that collected and recorded users’ physiological data through the computer’s camera was installed on the participants’ computers. The CVS-Q was applied to determine the subjects with CVS and its severity. The results showed a decrease in the blinking rate to about 9 to 17 per minute, and for each additional blink the CVS score lowered by 1.26. These data suggest that the decrease in blinking rate was directly associated with CVS. These results are important for allowing the development of a CVS real-time detection algorithm and a related recommendation system that provides interventions to promote health, well-being, and improved performance.

Suggested Citation

  • Inês Lapa & Simão Ferreira & Catarina Mateus & Nuno Rocha & Matilde A. Rodrigues, 2023. "Real-Time Blink Detection as an Indicator of Computer Vision Syndrome in Real-Life Settings: An Exploratory Study," IJERPH, MDPI, vol. 20(5), pages 1-11, March.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4569-:d:1087720
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    References listed on IDEAS

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    1. Flachaire, Emmanuel, 2005. "Bootstrapping heteroskedastic regression models: wild bootstrap vs. pairs bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 361-376, April.
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