PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Perception d'états affectifs et apprentissage
Gaëlle Loosli, Sang-Goog Lee, Vincent Guigue, Alain Rakotomamonjy and Stéphane Canu
RIA - Revue d'intelligence artificielle Volume 20, Number 4-5, pp. 553-582, 2006. ISSN 0992 499x

Abstract

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
Multimodal Integration
ID Code:1084
Deposited By:Gaëlle Loosli
Deposited On:19 January 2007