Institutions often struggle to accurately monitor student engagement, emotional well-being, and attentiveness in real-time, leading to inefficiencies in learning environments and reduced academic performance. Existing solutions lack personalization, automation, and deep behavioral insights.
During development, we faced challenges like processing real-time video data with low latency, ensuring accurate emotion and gaze detection across diverse classroom environments, and maintaining data privacy and ethical standards. Additionally, we had to build a scalable system that integrates smoothly with existing institutional infrastructure while managing computational constraints effectively.
Technologies used