Eye Health Monitoring and Eye Care System

Abstract

This study presents a multi-modal AI-powered system designed for real-time eye health monitoring and early diagnosis. The proposed system integrates computer vision, machine learning, and image processing techniques to deliver non-invasive and accessible diagnostic solutions. The system comprises four key modules. The first one is a real-time eye exercise module that utilizes webcam-based eye tracking and adaptive machine learning to provide personalized routines for reducing digital eye fatigue. The second module is an AI-driven cataract detection module that analyzes retinal images to enable early diagnosis, particularly in resource-limited settings. The third module is a glaucoma detection system that tracks pupil dynamics, such as size and light reactivity, to identify early symptoms; and the fourth and final module is a color blindness and eye fatigue detection module that employs multi-modal AI techniques to assess color vision deficiencies and monitor signs of visual fatigue in real-time Collectively, these components form a comprehensive array of tools focused on improving eye health monitoring and treatment. Through the combination of advanced AI technologies with user-friendly interfaces, the proposed systems aim to democratize access to eye care, reduce the global burden of preventable vision loss, and enhance the quality of life for individuals everywhere.