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

Inês Lapa, Simão Ferreira, Catarina Mateus, Nuno Rocha and Matilde A. Rodrigues ()
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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
Simão Ferreira: Center for Translational Health and Medical Biotechnology Research, School of Health of Polytechnic Institute of Porto, 4200-465 Porto, Portugal
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

IJERPH, 2023, vol. 20, issue 5, 1-11

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.

Keywords: computer vision syndrome; eye blink; blinking rate; eye-blink detection (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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