Abstract
With the use of information terminals represented by smartphones, our eyes get tired. One index indicating eye fatigue is a change in the number of blinks. In this study, we set the ultimate goal that is to develop eye fatigue prevention system with less time and less physical burden for patient. By doing this, the information terminal performs a flickering of the eyes using a detection camera and a validity check. Previous EAR study with blink detection is confirmed to be effective, and this paper proposes a new formula EARM used EAR. As an evaluation method of blink detection, we used the total time average of the square of the residual value. The results showed that EARM was more accurate than the EAR. Further, it was suggested that the number of blinks during VDT work can be classified into several patterns.
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Kuwahara, A., Hirakawa, R., Kawano, H., Nakashi, K., Nakatoh, Y. (2021). Blink Detection Using Image Processing to Predict Eye Fatigue. In: Ahram, T., Taiar, R., Langlois, K., Choplin, A. (eds) Human Interaction, Emerging Technologies and Future Applications III. IHIET 2020. Advances in Intelligent Systems and Computing, vol 1253. Springer, Cham. https://doi.org/10.1007/978-3-030-55307-4_55
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DOI: https://doi.org/10.1007/978-3-030-55307-4_55
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