creators_name: Kleinsmith, Andrea creators_name: Bianchi-Berthouze, Nadia editors_name: Prince, Christopher G. editors_name: Berthouze, Luc editors_name: Kozima, Hideki editors_name: Bullock, Daniel editors_name: Stojanov, Georgi editors_name: Balkenius, Christian type: confposter datestamp: 2004-02-12 lastmod: 2011-03-11 08:55:26 metadata_visibility: show title: Towards Learning Affective Body Gesture ispublished: pub subjects: comp-sci-mach-learn subjects: comp-sci-art-intel subjects: comp-sci-robot full_text_status: public keywords: gesture recognition, affect recognition, robot-human communication, Categorizing and Learning modules abstract: 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. date: 2003 date_type: published volume: 101 publisher: Lund University Cognitive Studies pagerange: 169-170 refereed: TRUE citation: Kleinsmith, Andrea and Bianchi-Berthouze, Nadia (2003) Towards Learning Affective Body Gesture. [Conference Poster] document_url: http://cogprints.org/3349/1/Kleinsmith.pdf