The project entitled "Computer Vision: Realtime object detection using AI and Machine Learning Realtime Eye Strain Detection System", focuses on enhancing digital well-being by addressing the growing problem of Computer Vision Syndrome (CVS), commonly known as Digital Eye Strain. With the increasing dependency on digital devices for work, study, and entertainment, users often experience symptoms such as eye dryness, irritation, blurred vision, headaches, and reduced concentration. The proposed system utilizes Artificial Intelligence (AI) and Computer Vision (CV) technologies to monitor and analyze real-time indicators of visual fatigue. Using tools such as MediaPipe and OpenCV, it detects parameters like blink rate, eye aspect ratio (EAR), sitting distance, and ambient lighting. A user-friendly PyQt6 graphical interface enables seamless interaction, providing users with real-time alerts, adaptive feedback, and personalized wellness recommendations. By integrating AI APIs like Gemini or Grok, the system generates intelligent insights, preventive suggestions, and health trend reports. This promotes healthy screen habits and reduces the risk of long-term eye strain. The Vision Shield system contributes to digital wellness, productivity improvement, and AI-based health monitoring, offering a scalable solution for students, professionals, and organizations alike.
Pangavhane et al. (Thu,) studied this question.