Open CV Based a Rehabilitation Gaze Quadratic Tracking Movement Using Ear Algorithm

Locked-in syndrome severely restricts voluntary muscle movement, leaving patients unable to communicate except through eye movements. Traditional assistive communication devices are often expensive, complex, and require specialized training, making them less accessible for many patients. This project aims to bridge that gap by developing an affordable and user-friendly gaze-tracking system that enables communication through eye movements. Using a camera positioned at eye level, the system continuously tracks the user’s gaze in real time with the integration of Python, OpenCV, and Dlib. The Eye Aspect Ratio (EAR) algorithm detects an interprets specific eye movements, converting them into predefined commands. These commands are then translated into voice outputs and email alerts, allowing patients to express their needs and communicate effectively with caregivers and family members. This innovative approach enhances patient independence, providing a more intuitive and accessible alternative to existing communication solutions. By leveraging cost-effective hardware and open-source software, the system ensures greater adaptability, making it a viable option for home and clinical use. Future enhancements could include improved accuracy with deep learning models, multilingual support, and integration with smart home systems to further improve the quality of life for individuals with locked-in syndrome. (S, 2023)d Keywords: Locked-in Syndrome,Gaze Tracking, Eye Movement Detection, Assistive Communication, Eye Aspect Ratio (EAR), OpenCV, Dlib, Real-time Processing, Voice Command Generation, Email Alert System