Monitor Eye-Care System using Blink Detection A Convolutional Neural Net Approach

Worah, Gautam; Khan, None Aasim; Kothari, Mehul; Naik, Meghana

doi:10.17577/ijertv6is110012

Cited by 1 publication

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“…This carries a high potential to be used also in the field of occupational safety and health as, for example, to predict CVS. However, none of the studies have associated blinking with CVS, despite this relevance being emphasized by Worah et al [29].…”

Section: Systems That Determine Blinkmentioning

confidence: 99%

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

Lapa

Ferreira

Mateus

et al. 2023

IJERPH

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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.

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Section: Systems That Determine Blinkmentioning

confidence: 99%