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Simple Games – Complex Emotions: Automated Affect Detection Using Physiological Signals

  • Thomas Friedrichs
  • Carolin Zschippig
  • Marc Herrlich
  • Benjamin Walther-Franks
  • Rainer Malaka
  • Kerstin Schill
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9353)

Abstract

Understanding the impact of interaction mechanics on the user’s emotional state can aid in shaping the user experience. For eliciting the emotional state of a user, designers and researchers typically employ subjective or expert assessment. Yet these methods are typically applied after the user has finished the interaction, causing a delay between stimulus and assessment. Physiological measures potentially offer more reliable indication of a user’s affective state in real-time. We present an experiment to increase our understanding of the relation of certain stimuli and valence of induced emotions in games. For this we designed a simple game to induce negative and positive emotions in the player. The results show a high correspondence between our classification of participants’ physiological signals and subjective assessment. However, creating a clear causality between game elements and emotions is a daunting task, and our designs offer room for improvement.

Keywords

Objective game evaluation Psycho-physiology Affective gaming Valence detection 

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References

  1. 1.
    Brockmyer, J.H., Fox, C.M., Curtiss, K.A., McBroom, E., Burkhart, K.M., Pidruzny, J.N.: The development of the Game Engagement Questionnaire: A measure of engagement in video game-playing. J. of Experimental Social Psychology 45(4), 624–634 (2009)CrossRefGoogle Scholar
  2. 2.
    Calvo, R.A., D’Mello, S.: Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications. IEEE Trans. on Affective Computing 1(1), 18–37 (2010)CrossRefGoogle Scholar
  3. 3.
    Calvo, R.A., D’Mello, S., Gratch, J., Kappas, A.: The Oxford Handbook of Affective Computing. Oxford University Press (2014)Google Scholar
  4. 4.
    Chanel, G., Rebetez, C., Pun, T.: Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty. IEEE Trans. On Systems, Man, and Cybernetics 41(6), 1052–1063 (2011)CrossRefGoogle Scholar
  5. 5.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Trans. on Intelligent Systems and Technology 2, 27:1–27:27 (2011)Google Scholar
  6. 6.
    Ganglbauer, E., Schrammel, J., Deutsch, S.: Applying Psychophysiological Methods for Measuring User Experience: Possibilities, Challenges and FeasibilityGoogle Scholar
  7. 7.
    Hébert, S., Béland, R., Dionne-Fournelle, O., Crête, M., Lupien, S.J.: Physiological stress response to video-game playing: the contribution of built-in music. Life Sciences 76(20), 2371–2380 (2005)CrossRefGoogle Scholar
  8. 8.
    Kivikangas, J.M., Chanel, G., Cowley, B., Ekman, I., Salminen, M., Järvelä, S., Ravaja, N.: A review of the use of psychophysiological methods in game research. J. of Gaming & Virtual Worlds 3(3), 181–199 (2011)CrossRefGoogle Scholar
  9. 9.
    McCraty, R., Atkinson, M., Tiller, W.A., Rein, G., Watkins, A.D.: The effects of emotions on short-term power spectrum analysis of heart rate variability. The American Journal of Cardiology 76(14), 1089–1093 (1995)CrossRefGoogle Scholar
  10. 10.
    Morris, J.: Observations: SAM: the Self-Assessment Manikin; an efficient cross-cultural measurement of emotional response. J. of Advertising Research (December 1995)Google Scholar
  11. 11.
    Nogueira, P.A., Rodrigues, R., Nacke, L.E.: Guided Emotional State Regulation: Understanding and Shaping Players’ Affective Experiences in Digital Games. In: AIIDE (2013)Google Scholar
  12. 12.
    Rachuy, C., Budde, S., Schill, K.: Unobtrusive data retrieval for providing individual assistance in aal environments. In: Int. Conf. on Health Informatics (2011)Google Scholar
  13. 13.
    Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C.M., Suri, J.S.: Heart rate variability: a review. Medical & Biological Engineering & Computing 44(12), 1031–1051 (2006)CrossRefGoogle Scholar
  14. 14.
    Vidaurre, C., Sander, T.H., Schlögl, A.: Biosig: The free and open source software library for biomedical signal processing. Computational Intelligence and Neuroscience (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Thomas Friedrichs
    • 1
  • Carolin Zschippig
    • 2
  • Marc Herrlich
    • 3
  • Benjamin Walther-Franks
    • 3
  • Rainer Malaka
    • 3
  • Kerstin Schill
    • 2
  1. 1.OFFIS – Institute for Information TechnologyOldenburgGermany
  2. 2.Cognitive NeuroinformaticsUniversity of BremenBremenGermany
  3. 3.Digital Media Lab, TZIUniversity of BremenBremenGermany

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