DISTRIBUTED SYSTEM FOR ESTIMATING STUDENT ENGAGEMENT, ATTENTION, AND INTERACTION IN REAL TIME USING EDGE PROCESSING DEVICES

Abstract

The invention focuses on developing a distributed system of computing and video capture devices to estimate, in real time, student engagement, attention, and interaction in classrooms. It uses artificial intelligence and high-performance edge computing to locally process video signals, reducing bandwidth consumption and network congestion. The distributed system enables efficient management of large data volumes and high availability. Real-time processing ensures immediate and effective responses, while artificial intelligence applications perform complex tasks without direct human intervention. High-performance edge computing minimizes latency and improves response speed. Local processing reduces reliance on external networks, improving efficiency.The invention uses Nvidia's Jetson Xavier NX devices to process video within classrooms, optimizing data transmission and mitigating latency issues. Each device runs deep neural networks and fusion algorithms to measure engagement and attention, facilitating the real-time personalization of educational strategies. Video capture is performed with a high-resolution IP camera, ensuring security and privacy by storing data securely and anonymously on a protected web server.