Vision-Shield AI -Based Eye Strain Detection System
Mrs.Priyanka.A.Avhad(Lecturer)
Department of Computer Technology
K.K. Wagh Polytechnic,
Nashik,India
paavhad@kkwagh.edu.in
Ms.Sakshi D. Kasar(Student)
Department of Computer Technology
K.K. Wagh Polytechnic,
Nashik,India
sakshikasar994@gmail.com
Ms.Dnyaneshwari B. Nagare(Student)
Department of Computer Technology
K.K. Wagh Polytechnic,
Nashik,India
dnyaneshwari6747@gmail.com
Ms.Sneha N. Darade(Student)
Department of Computer Technology
K.K. Wagh Polytechnic,
Nashik,India
daradesneha27@gmail.com
Ms.Sakshi R. Pagar(Student)
Department of Computer Technology
K.K. Wagh Polytechnic,
Nashik,India
sakshipagar2007@gmail.com
ABSTRACT
Vision Shield is an AI-based Eye Strain Detection System designed to reduce the negative effects of prolonged screen exposure. In today’s digital world, students, office workers, and professionals spend long hours using computers and smartphones, which often leads to eye strain, dryness, blurred vision, headaches, and reduced productivity. The main objective of this project is to develop a real-time monitoring system that can detect early signs of visual fatigue and provide preventive suggestions. The system uses computer vision techniques through MediaPipe and OpenCV to analyze blink rate, Eye Aspect Ratio (EAR), sitting distance from the screen, ambient lighting conditions, and total screen time. A PyQt6-based graphical interface displays live monitoring results, warning alerts, and detailed usage reports in a simple and user-friendly manner. AI APIs are integrated to generate personalized recommendations and adaptive feedback based on user behavior. The system stores historical data for analysis and performance evaluation. Experimental testing shows accurate detection with smooth real-time performance on standard hardware. This project promotes digital wellness, improves awareness about healthy screen habits, and supports safer and more sustainable use of technology.
Key Words — Eye Strain Detection, Artificial Intelligence, Computer Vision, MediaPipe, OpenCV, Digital Wellness.
