Abstract: This Paper presents a Strain Analysis Based on Eye Blinking This cloud of a world has splattered towards more than normal screen hours, leading towards executable eye strain plus exhaustion. This system focuses on resolving eye strain by “monitoring eye blinking patterns.” Staring passively is a behavior that can be recorded from live video feeds of individuals, like how many times a person blinks and how long each time the individual looks at the screen. By watching these movements, the system determines if the user struggles with visual fatigue or exhaustion. It is possible for a person who suffers a lot from eye strain. In this step, the accuracy of the system will be determined. In the future, expanding the datasets and fine-tuning the accuracy of user comfort levels for more than just different lighting conditions or variations plus activities performed on the screen can make this more efficient. Another step we can take is the cross-device implementation so every device can use it. Overall simple eye care goals were achieved- making life easy in this world of digitization and screens surrounding us everywhere.
Keywords: Electrooculography, Blink Duration, Frequency, Deep learning techniques, Convolutional neural network (CNN), Fatigue.
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DOI:
10.17148/IJARCCE.2025.14239
