Grounding Affective Dimensions into Posture Features

  • Andrea Kleinsmith
  • P. Ravindra De Silva
  • Nadia Bianchi-Berthouze
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3784)


Many areas of today’s society are seeing an increased importance in the creation of systems capable of interacting with users on an affective level through a variety of modalities. Our focus has been on affective posture recognition. However, a deeper understanding of the relationship between emotions in terms of postural expressions is required. The goal of this study was to identify affective dimensions that human observers use when discriminating between postures, and to investigate the possibility of grounding this affective space into a set of posture features. Using multidimensional scaling, arousal, valence, and action tendency were identified as the main factors in the evaluation process. Our results showed that, indeed, low-level posture features could effectively discriminate between the affective dimensions.


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  1. 1.
    Bianchi-Berthouze, N., Kleinsmith, A.: A categorical approach to affective gesture recognition. Connection Science 15, 259–269 (2003)CrossRefGoogle Scholar
  2. 2.
    Breazeal, C.: Emotion and sociable humanoid robots. International Journal of Human-Computer Studies 59, 119–155 (2003)CrossRefGoogle Scholar
  3. 3.
    Coulson, M.: Attributing emotion to static body postures: recognition accuracy, confusions, and viewpoint dependence. Jour. of Nonv. Behav. 28, 117–139 (2004)CrossRefGoogle Scholar
  4. 4.
    Cowie, R., Douglas-Cowie, E., Savvidou, S., McMahon, E., Sawey, M., Schroder, M.: FEELTRACE: An instrument for recording perceived emotion in real time. In: Proc. ISCA Workshop on Speech and Emotion, pp. 19–24 (2000)Google Scholar
  5. 5.
    Dailey, M.N., Cottrell, G.W., Padgett, C., Adolphs, R.: EMPATH: A neural network that categorizes facial expressions. Journal of Cognitive Neuroscience 14, 1158–1173 (2002)CrossRefGoogle Scholar
  6. 6.
    Ekman, P., Friesen, W.: Unmasking the Face: A Guide to Recognizing Emotions from Facial Expressions. Prentice Hall, Englewood Cliffs (1975)Google Scholar
  7. 7.
    Hastie, T., Tibshirabi, R.: Discriminant analysis by Gaussian mixture. Journal of the Royal Statistical Society B(58), 155–176 (1996)zbMATHGoogle Scholar
  8. 8.
    Kleinsmith, A., De Silva, P.R., Bianchi-Berthouze, N.: Building User Models Based on Cross-Cultural Differences in Recognizing Emotion from Affective Postures. In: Int’l Conf. on User Modeling, Edinburgh, July 2005, pp. 50–59 (2005) (to appear)Google Scholar
  9. 9.
    Kleinsmith, A., Fushimi, T., Bianchi-Berthouze, N.: An incremental and interactive affective posture recognition system. In: Proc. Workshop on Adapting the Interaction Style to Affective Factors, Edinburgh (July 2005) (to appear)Google Scholar
  10. 10.
    Kruskal, J.B., Wish, M.: Multidimensional Scaling. Series: Quantitative Applications in the Social Sciences, Sage University paper (1978)Google Scholar
  11. 11.
    Lachenbruch, P.A.: Discriminant Analysis. Hafner, NY (1975)zbMATHGoogle Scholar
  12. 12.
    Larsen, J.T., McGraw, A.P., Cacioppo, J.T.: Can People Feel Happy and Sad at the Same Time? Journ. of Pers. and Social Psych. 81, 684–696 (2001)CrossRefGoogle Scholar
  13. 13.
    Picard, R.: Toward Agents that Recognize Emotion. In: Actes Proc. IMAGINA, pp. 153–165 (1998)Google Scholar
  14. 14.
    Plutchik, R.: Emotions: A general psychoevolutionary theory. In: Scherer, K., Ekman, P. (eds.) Approaches to Emotion, Lawrence Erlbaum Associates, NJ (1984)Google Scholar
  15. 15.
    Plutchik, R.: The Nature of Emotions. American Scientist 89, 344–350 (2001)Google Scholar
  16. 16.
    Russell, J.: Reading emotions from and into faces: resurrecting a dimensional-contextual perspective. In: Russell, J., Fernandez-Dols, J. (eds.) The Psychology of Facial Expression. Cambridge University Press, Cambridge (1997)CrossRefGoogle Scholar
  17. 17.
    Schroder, M.: Dimensional emotion representation as a basis for speech synthesis with non-extreme emotions. In: André, E., Dybkjær, L., Minker, W., Heisterkamp, P. (eds.) ADS 2004. LNCS (LNAI), vol. 3068, pp. 209–220. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    de Silva, P.R., Bianchi-Berthouze, N.: Modeling human affective postures: An information theoretic characterization of posture features. Journal of Computer Animation and Virtual Worlds 15, 269–276 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Andrea Kleinsmith
    • 1
  • P. Ravindra De Silva
    • 1
  • Nadia Bianchi-Berthouze
    • 1
  1. 1.Database Systems LaboratoryUniversity of AizuAizu WakamatsuJapan

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