Gaze Based Attention Monitoring and Assessment System

Gaze-based attention monitoring is a fresh approach to understanding how we focus using real-time gaze-tracking technology. By combining signal processing and machine learning, this system tracks visual attention and gives feedback to help improve focus. Using tools like WebGazer.js and standard webcams, it skips the need for expensive equipment, making it affordable and accessible for various applications. During calibration, participants are asked to look at certain points to create baseline data, so the system can adapt to individual gaze patterns for better accuracy. Advanced noise reduction techniques, such as Kalman filtering, handle disruptions from blinks or head movements, ensuring smooth and reliable tracking. The system also generates heatmaps and visuals, making it easy to see where attention is strongest or weakest. Real-time feedback keeps the attention of users on track throughout tasks such as online learning or therapy, while long-term analysis helps identify attention trends. This creates opportunities for individualized interventions and engagement strategies. Whether in education or healthcare, this system will provide an accessible route toward monitoring and enhancing attention so users can stay on task. By making gaze tracking more affordable and efficient, this innovative framework opens up the doors to new applications and opportunities for attention monitoring and attention improvement.