A Survey and Design of an Intelligent Eye Tracking and Blink Classification System for Inclusive Education

This paper presents a survey and design framework for an Intelligent Eye Tracking and Blink Classification System for Inclusive Education. The proposed framework aims to provide a low-cost, real-time assistive communication solution designed to support paralyzed and motor-impaired individuals in educational and daily interaction environments. The system utilizes a standard webcam to detect eye gaze direction and classify voluntary blinks, enabling cursor control and command selection without requiring physical input. By integrating computer vision techniques with lightweight machine learning models, it maintains reliable performance under varying lighting conditions and head poses. The proposed system is inspired by advancements in blink detection, gaze tracking, and assistive technologies, aiming to provide an accessible and affordable alternative to expensive commercial eye-tracking devices. Ultimately, the solution promotes independent communication and enhances inclusive educational opportunities for users with severe motor limitations. Since this work represents the initial design and survey phase of the project, detailed implementation, performance evaluation, and experimental validation are planned as part of the next phase of development.

Eye Tracking, Eye Blink Classification, Machine Learning, Computer Vision

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