edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platforms
Roberto Daza, Aythami Morales, Ruben Tolosana, Luis F. Gomez, Julian, Fierrez, Javier Ortega-Garcia

TL;DR
edBB-Demo is an AI-powered platform that integrates multimodal sensor data to monitor student behavior and biometric signals in remote education, enabling biometric authentication, action recognition, and attention estimation.
Contribution
This work introduces edBB-Demo, a novel multimodal platform for comprehensive student monitoring in remote learning environments.
Findings
Successful biometric user authentication in an unsupervised setting
Effective human action recognition from remote video analysis
Accurate heart rate and attention level estimation from sensor data
Abstract
We present edBB-Demo, a demonstrator of an AI-powered research platform for student monitoring in remote education. The edBB platform aims to study the challenges associated to user recognition and behavior understanding in digital platforms. This platform has been developed for data collection, acquiring signals from a variety of sensors including keyboard, mouse, webcam, microphone, smartwatch, and an Electroencephalography band. The information captured from the sensors during the student sessions is modelled in a multimodal learning framework. The demonstrator includes: i) Biometric user authentication in an unsupervised environment; ii) Human action recognition based on remote video analysis; iii) Heart rate estimation from webcam video; and iv) Attention level estimation from facial expression analysis.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsContext-Aware Activity Recognition Systems
