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Emotion Recognition for Exergames using Laban Movement Analysis

Emotion Recognition for Exergames using Laban Movement Analysis Emotion Recognition for Exergames using Laban Movement Analysis Haris Zacharatos University of Cyprus Christos Gatzoulis Bahrain Polytechnic Yiorgos Chrysanthou University of Cyprus Andreas Aristidou§ University of Cyprus Abstract Exergames do not have the capacity to detect whether the players are really enjoying the game-play. The games are not intelligent enough to detect significant emotional states and adapt accordingly in order to offer a better user experience for the players. We propose a set of body motion features, based on the Effort component of Laban Movement Analysis (LMA), that are used to provide sets of classifiers for emotion recognition in a game scenario for four emotional states:concentration, meditation, excitement and frustration. Experimental results show that, the system is capable of successfully recognizing the four different emotional states at a very high rate. CR Categories: I.2.10 [Computing Methodologies]: Artificial Intelligence--Vision and Scene Understanding I.4.7 [Computing Methodologies]: Image Processing and Computer Vision--Feature Measurement J.0 [Computer Applications]: General--; Keywords: Emotion recognition, Laban Movement Analysis, Exergames KOTROPOULOS 2006], exergame players express their emotions using their bodies as these modalities are more active and energetic during exergaming. Existing research that attempts to recognize emotions using human motion data does not achieve sufficient recognition rates, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Emotion Recognition for Exergames using Laban Movement Analysis

Zacharatos, Haris; Gatzoulis, Christos; Chrysanthou, Yiorgos; Aristidou, Andreas
Association for Computing Machinery — Nov 6, 2013
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Datasource
Association for Computing Machinery
Copyright
Copyright © 2013 by ACM Inc.
ISBN
978-1-4503-2546-2
doi
10.1145/2522628.2522651
Publisher site
See Article on Publisher Site

Abstract

Emotion Recognition for Exergames using Laban Movement Analysis Haris Zacharatos University of Cyprus Christos Gatzoulis Bahrain Polytechnic Yiorgos Chrysanthou University of Cyprus Andreas Aristidou§ University of Cyprus Abstract Exergames do not have the capacity to detect whether the players are really enjoying the game-play. The games are not intelligent enough to detect significant emotional states and adapt accordingly in order to offer a better user experience for the players. We propose a set of body motion features, based on the Effort component of Laban Movement Analysis (LMA), that are used to provide sets of classifiers for emotion recognition in a game scenario for four emotional states:concentration, meditation, excitement and frustration. Experimental results show that, the system is capable of successfully recognizing the four different emotional states at a very high rate. CR Categories: I.2.10 [Computing Methodologies]: Artificial Intelligence--Vision and Scene Understanding I.4.7 [Computing Methodologies]: Image Processing and Computer Vision--Feature Measurement J.0 [Computer Applications]: General--; Keywords: Emotion recognition, Laban Movement Analysis, Exergames KOTROPOULOS 2006], exergame players express their emotions using their bodies as these modalities are more active and energetic during exergaming. Existing research that attempts to recognize emotions using human motion data does not achieve sufficient recognition rates,

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