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Design of a Fuzzy Affective Agent Based on Typicality Degrees of Physiological Signals

  • Joseph Onderi Orero
  • Maria Rifqi
Part of the Communications in Computer and Information Science book series (CCIS, volume 443)

Abstract

Physiology-based emotionally intelligent paradigms provide an opportunity to enhance human computer interactions by continuously evoking and adapting to the user experiences in real-time. However, there are unresolved questions on how to model real-time emotionally intelligent applications through mapping of physiological patterns to users’ affective states.

In this study, we consider an approach for design of fuzzy affective agent based on the concept of typicality. We propose the use of typicality degrees of physiological patterns to construct the fuzzy rules representing the continuous transitions of user’s affective states. The approach was tested on experimental data in which physiological measures were recorded on players involved in an action game to characterize various gaming experiences. We show that, in addition to exploitation of the results to characterize users’ affective states through typicality degrees, this approach is a systematic way to automatically define fuzzy rules from experimental data for an affective agent to be used in real-time continuous assessment of user’s affective states.

Keywords

Machine learning fuzzy logic prototypes typicality degrees affective computing physiological signals 

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References

  1. 1.
    Bouchon-Meunier, B.: Aggregation and Fusion of Imperfect Information. Physica-Verlag, Spring-Verlag Company (1998)Google Scholar
  2. 2.
    Calvo, R.A., D’Mello, S.: Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing 1, 18–37 (2010)CrossRefGoogle Scholar
  3. 3.
    Chanel, G., Kierkels, J.J.M., Soleymani, M., Pun, T.: Short-term emotion assessment in a recall paradigm. International Journal of Human-Computer Studies 67, 607–627 (2009)CrossRefGoogle Scholar
  4. 4.
    Chanel, G., Rebetez, C., Btrancourt, M., Pun, T.: Emotion assessment from physiological signals for adaptation of game difficulty. IEEE Transactions on Systems, Man, and Cybernetics 41(6), 1052–1063 (2011)CrossRefGoogle Scholar
  5. 5.
    Csikszentmihalyi, M.: Harper and row flow: the psychology of optimal experience. Harper & Row, New York (1990)Google Scholar
  6. 6.
    Detyniecki, M.: Mathematical aggregation operators and their application to video querying. PhD thesis, Université Pierre et Marie Curie, France (2001)Google Scholar
  7. 7.
    Ekman, P., Levenson, R.W., Friesen, W.V.: Autonomic nervous system activity distinguishes among emotions. Science 221, 1208–1210 (1983)CrossRefGoogle Scholar
  8. 8.
    Fairclough, S.H.: Fundamentals of physiological computing. Interacting With Computers 21, 133–145 (2009)CrossRefGoogle Scholar
  9. 9.
    Kim, J., André, E.: Emotion recognition based on physiological changes in music listening. IEEE Transactions on Pattern Analysis And Machine Intelligence 30(12), 2067–2083 (2008)CrossRefGoogle Scholar
  10. 10.
    Lesot, M.-J., Rifqi, M., Bouchon-Meunier, B.: Fuzzy prototypes: from a cognitive view to a machine learning principle. In: Bustince, H., Herrera, F., Montero, J. (eds.) Fuzzy sets and Their Extensions: Representation, Aggregation and Models. STUDFUZZ, vol. 220, pp. 431–452. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Levillain, F., Orero, J.O., Rifqi, M., Bouchon-Meunier, B.: Characterizing player’s experience from physiological signals using fuzzy decision trees. In: IEEE Conference on Computational Intelligence and Games (2010)Google Scholar
  12. 12.
    Liu, C., Agrawal, P., Sarkar, N., Chen, S.: Dynamic difficulty adjustment in computer games through real-time anxiety-based affective feedback. International Journal of Human-Computer Interaction 25(6), 506–529 (2009)CrossRefGoogle Scholar
  13. 13.
    Mandryk, R.L., Atkins, M.S.: A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. International Journal of Human-Computer Studies 65(4), 329–347 (2007)CrossRefGoogle Scholar
  14. 14.
    Novak, D., Mihelj, M., Munih, M.: A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing. Interacting with Computers 24, 154–172 (2012)CrossRefGoogle Scholar
  15. 15.
    Orero, J.O., Levillain, F., Damez-Fontaine, M., Rifqi, M., Bouchon-Meunier, B.: Assessing gameplay emotions from physiological signals: a fuzzy decision trees based model. In: International Conference on Kansei Engineering and Emotion Research (2010)Google Scholar
  16. 16.
    Picard, R.W.: Affective computing. The MIT Press, Cambridge (1997)Google Scholar
  17. 17.
    Picard, R.W., Vyzas, E., Healey, J.: Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Transactions Pattern Analysis and Machine Intelligence 23, 1175–1191 (2001)CrossRefGoogle Scholar
  18. 18.
    Rainville, P., Bechara, A., Naqvi, N., Damasio, A.R.: Basic emotions are associated with distinct patterns of cardiorespiratory activity. International Journal of Psychophysiology 61, 5–18 (2006)CrossRefGoogle Scholar
  19. 19.
    Rani, P., Sarkar, N., Adams, J.: Anxiety-based affective communication for implicit human machine interaction. Advanced Engineering Informatics 21, 323–334 (2007)CrossRefGoogle Scholar
  20. 20.
    Rifqi, M.: Constructing prototypes from large databases. In: International Confrence on Information Processing and Management of Uncertainity in Knowledge-Based System, IPMU (1996)Google Scholar
  21. 21.
    Rosch, E., Mervis, C.: Family resemblance: studies of the internal structure of categories. Cognitive Psychology 7, 573–605 (1975)CrossRefGoogle Scholar
  22. 22.
    Wagner, J., Kim, J., André, E.: From physiological signals to emotions: implementing and comparing selected methods for feature extraction and classification. In: IEEE International Conference in Multimedia and Expo, pp. 940–943 (2005)Google Scholar
  23. 23.
    Yannakakis, G.N., Hallam, J.: Entertainment modeling through physiology in physical play. International Journal of Human-Computer Studies 66, 741–755 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Joseph Onderi Orero
    • 1
  • Maria Rifqi
    • 2
  1. 1.Faculty of Information TechnologyStrathmore UniversityKenya
  2. 2.LEMMAUniversity Panthéon-AssasFrance

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