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Comparing Two Emotion Models for Deriving Affective States from Physiological Data

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Affect and Emotion in Human-Computer Interaction

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4868))

Abstract

This paper describes an experiment on emotion measurement and classification based on different physiological parameters, which was conducted in the context of a European project on ambient intelligent mobile devices. Emotion induction material consisted of five four-minute video films that induced two positive and three negative emotions. The experimental design gave consideration to both, the basic and the dimensional model of the structure of emotion. Statistical analyses were conducted for films and for self-assessed emotional state and in addition, supervised machine learning technique was utilized. Recognition rates reached up to 72% for a specific emotion (one out of five) and up to 82% for an underlying dimension (one out of two).

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Authors and Affiliations

  1. Institut für Psychologie und Arbeitswissenschaft, Technische Universität Berlin, Fachgebiet Mensch-Maschine-Systeme, Franklinstr. 28-29, 10587, Berlin, Germany

    Antje Lichtenstein

  2. HFC Human-Factors-Consult GmbH, Köpenickerstraße 325, 12555, Berlin, Germany

    Astrid Oehme, Stefan Kupschick & Thomas Jürgensohn

Authors
  1. Antje Lichtenstein
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  2. Astrid Oehme
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  3. Stefan Kupschick
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  4. Thomas Jürgensohn
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Christian Peter Russell Beale

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Lichtenstein, A., Oehme, A., Kupschick, S., Jürgensohn, T. (2008). Comparing Two Emotion Models for Deriving Affective States from Physiological Data. In: Peter, C., Beale, R. (eds) Affect and Emotion in Human-Computer Interaction. Lecture Notes in Computer Science, vol 4868. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85099-1_4

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  • DOI: https://doi.org/10.1007/978-3-540-85099-1_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85098-4

  • Online ISBN: 978-3-540-85099-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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