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Automatic Recognition of Affective Body Movement in a Video Game Scenario

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Intelligent Technologies for Interactive Entertainment (INTETAIN 2011)

Abstract

This study aims at recognizing the affective states of players from non-acted, non-repeated body movements in the context of a video game scenario. A motion capture system was used to collect the movements of the participants while playing a Nintendo Wii tennis game. Then, a combination of body movement features along with a machine learning technique was used in order to automatically recognize emotional states from body movements. Our system was then tested for its ability to generalize to new participants and to new body motion data using a sub-sampling validation technique. To train and evaluate our system, online evaluation surveys were created using the body movements collected from the motion capture system and human observers were recruited to classify them into affective categories. The results showed that observer agreement levels are above chance level and the automatic recognition system achieved recognition rates comparable to the observers’ benchmark.

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Authors and Affiliations

  1. UCLIC, University College London, MPEB Gower Street, London, WC1E6BT, UK

    Nikolaos Savva & Nadia Bianchi-Berthouze

Authors
  1. Nikolaos Savva
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  2. Nadia Bianchi-Berthouze
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Editors and Affiliations

  1. InfoMus Lab, DIST, University of Genova, Piazza Santa Maria in Passione 34, 16145, Genova, Italy

    Antonio Camurri

  2. UBINET, CREATE-NET, Via alla Cascata 56/D Povo, 38123, Trento, Italy

    Cristina Costa

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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

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Savva, N., Bianchi-Berthouze, N. (2012). Automatic Recognition of Affective Body Movement in a Video Game Scenario. In: Camurri, A., Costa, C. (eds) Intelligent Technologies for Interactive Entertainment. INTETAIN 2011. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 78. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30214-5_17

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  • DOI: https://doi.org/10.1007/978-3-642-30214-5_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30213-8

  • Online ISBN: 978-3-642-30214-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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