edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platforms

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.