Abstract: Computer Vision Syndrome (CVS) is a growing public health concern caused by the increased use of digital defenses in everyday life, particularly among working professionals and scholars. Symptoms similar as eye strain, blankness, blurred vision, and headaches are generally reported due to extended screen exposure. Addressing these challenges, this study introduces EYELUME, an innovative,non-invasive system that leverages the VIT- Pupil model a Vision Motor (VIT)- grounded armature for the accurate segmentation and analysis of pupil images.
The VIT- Pupil model is specifically designed to handle noisy and low- resolution images, making it largely suitable for real- world operations where ideal imaging conditions can’t always be guaranteed. Unlike traditional Convolutional Neural Network (CNN) approaches, which struggle to capture global dependences in visual data, VIT models exceed in landing contextual and spatial information throughout the image.
By tracking pupil variations over time, EYELUME enables real- time monitoring of digital eye strain symptoms. The model achieves an emotional segmentation delicacy of 99.6, thereby offering a dependable foundation for early discovery of CVS and enhancing digital eye health monitoring systems.
Keywords: Vision Mills, Computer Vision Syndrome, VIT- Pupil, Pupil Segmentation, Deep Learning, Pupillometry, Digital Eye Health.
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DOI:
10.17148/IJARCCE.2025.14647
