Engagement Detection in Online Learning

Due to COVID-19, the teaching mode has shifted from in-person to blended or purely online. The level of engagement in class is one of the key factors that affect students’ overall learning outcomes. Therefore, it is essential for instructors to keep track of class engagement and adjust their teaching strategies accordingly…

Learning Personalised Models for Automatic Self-Reported Personality Recognition 

Smart phones, voice assistants, and home robots are becoming more intelligent every day to support humans in their daily routines and tasks. Achieving the acceptance of such technologies by their users and ensuring their success make it necessary for them to be socially informed, responsive, and responsible…

Multimodal depression recognition

Major Depressive Disorder is a highly prevalent and disabling mental health condition. In this project we explore multimodal fusion systems combining visual, audio, textual, and contextual features via deep learning architectures for clinical depression recognition.

EEG signals analysis for emotions recognition.

The goal of this project is to study the timing and brain signals response of emotional recognition in a standardized way thanks to an avatar. Emotional detection is a mechanism that has been widely studied in neuroscience, however the novelty of this protocol lies in the fact of using a 3D avatar.

In the context of emotional detection…

Early prediction and detection of potential pharmaceutical scandals based on the analysis of social networks data: Application to the levothyrox scandal in 2017 on doctissimo forum.

The aim of this project is to analyze users comments in order to detect possible pharmaceutical scandals. A famous pharmaceutical scandal is the Levothyrox drug scandal…