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The AffectMove 2021 Challenge - Affect Recognition from Naturalistic Movement Data

Olugbade, T; Sagoleo, R; Ghisio, S; Gold, N; Williams, A; de Gelder, B; Camurri, A; ... Berthouze, N; + view all (2021) The AffectMove 2021 Challenge - Affect Recognition from Naturalistic Movement Data. In: Proceedings of the 9th International Conference on Affective Computing & Intelligent Interaction (ACII 2021). IEEE: Virtual conference. (In press). Green open access

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Abstract

We ran the first Affective Movement Recognition (AffectMove) challenge that brings together datasets of affective bodily behaviour across different real-life applications to foster work in this area. Research on automatic detection of naturalistic affective body expressions is still lagging behind detection based on other modalities whereas movement behaviour modelling is a very interesting and very relevant research problem for the affective computing community. The AffectMove challenge aimed to take advantage of existing body movement datasets to address key research problems of automatic recognition of naturalistic and complex affective behaviour from this type of data. Participating teams competed to solve at least one of three tasks based on datasets of different sensors types and reallife problems: multimodal EmoPain dataset for chronic pain physical rehabilitation context, weDraw-1 Movement dataset for maths problem solving settings, and multimodal UnigeMaastricht Dance dataset. To foster work across datasets, we also challenged participants to take advantage of the data across datasets to improve performances and also test the generalization of their approach across different applications.

Type: Proceedings paper
Title: The AffectMove 2021 Challenge - Affect Recognition from Naturalistic Movement Data
Event: 9th International Conference on Affective Computing & Intelligent Interaction (ACII 2021)
Open access status: An open access version is available from UCL Discovery
Publisher version: https://www.acii-conf.net/2021/
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: affective computing, bodily, challenge, datasets, movement
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Clinical, Edu and Hlth Psychology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > UCL Interaction Centre
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10131476
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