Perception d'états affectifs et apprentissage
Gaëlle Loosli, Sang-Goog Lee, Vincent Guigue, Alain Rakotomamonjy and Stéphane Canu
RIA - Revue d'intelligence artificielle
ISSN 0992 499x
This article deals with the problem of affective states recognition from physical and physiological wearable sensors.
Given the complex nature of the relationship between available signals and affective states to be detected we propose to use a statistical learning method.
We begin with a discussion about the state of the art in the field of statistical learning algorithms and their application to affective states recognition.
Then a framework is presented to compare different learning algorithms and methodologies.
Using the results of this pre-study, a global architecture is proposed for a real time embedded recognition system.
Instead of directly recognizing the affective states we propose to begin with detecting abrupt changes in the incoming signal
to segment it first and label each segment afterwards.
The interest of the proposed method is demonstrated on two real affective state recognition tasks.