title: Towards Learning Affective Body Gesture
creator: Kleinsmith, Andrea
creator: Bianchi-Berthouze, Nadia
subject: Machine Learning
subject: Artificial Intelligence
subject: Robotics
description: Robots are assuming an increasingly important role in our society. They now become pets and help support children healing. In other words, they are now trying to entertain an active and affective communication with human agents. However, up to now, such systems have primarily relied on the human agents' ability to empathize with the system. Changes in the behavior of the system could therefore reult in changes of mood or behavior in the human partner. This paper describes experiments we carried out to study the importance of body language in affective communication. The results of the experiments led us to develop a system that can incrementally learn to recognize affective messages conveyed by body postures.
publisher: Lund University Cognitive Studies
contributor: Prince, Christopher G.
contributor: Berthouze, Luc
contributor: Kozima, Hideki
contributor: Bullock, Daniel
contributor: Stojanov, Georgi
contributor: Balkenius, Christian
date: 2003
type: Conference Poster
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/3349/1/Kleinsmith.pdf
identifier:   Kleinsmith, Andrea and Bianchi-Berthouze, Nadia  (2003) Towards Learning Affective Body Gesture.  [Conference Poster]     
relation: http://cogprints.org/3349/