edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platforms
Roberto Daza, Aythami Morales, Ruben Tolosana, Luis F. Gomez, Julian Fierrez, Javier Ortega-Garcia
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.
BibTeX
@article{Daza_Morales_Tolosana_Gomez_Fierrez_Ortega-Garcia_2024, title={edBB-Demo: Biometrics and Behavior Analysis for Online Educational Platforms}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/27066}, DOI={10.1609/aaai.v37i13.27066}, abstractNote={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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Daza, Roberto and Morales, Aythami and Tolosana, Ruben and Gomez, Luis F. and Fierrez, Julian and Ortega-Garcia, Javier}, year={2024}, month={Jul.}, pages={16422-16424} }