Affective Modeling from Multichannel Physiology: Analysis of Day Differences

  • Omar Alzoubi
  • Md. Sazzad Hussain
  • Sidney D’Mello
  • Rafael A. Calvo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6974)

Abstract

Physiological signals are widely considered to contain affective information. Consequently, pattern recognition techniques such as classification are commonly used to detect affective states from physiological data. Previous studies have achieved some success in detecting affect from physiological measures, especially in controlled environments where emotions are experimentally induced. One challenge that arises is that physiological measures are expected to exhibit considerable day variations due to a number of extraneous factors such as environmental changes and sensor placements. These variations pose challenges to effectively classify affective sates from future physiological data; this is a common problem for real world requirements. The present study provides a quantitative analysis of day variations of physiological signals from different subjects. We propose a classifier ensemble approach using a Winnow algorithm to address the problem of day-variation in physiological signals. Our results show that the Winnow ensemble approach outperformed a static classification approach for detecting affective states from physiological signals that exhibited day variations.

Keywords

Affect detection classifier ensembles non-stationarity physiology 

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References

  1. 1.
    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
  2. 2.
    Wagner, J., Kim, J., Andre, E.: From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification. In: IEEE International Conference on Multimedia and Expo, ICME 2005, pp. 940–943 (2005) Google Scholar
  3. 3.
    Whang, M., Lim, J.: A Physiological Approach to Affective Computing. In: Affective Computing: Focus on Emotion Expression, Synthesis, and Recognition, pp. 310–318. I-Tech Education and Publishing, Vienna (2008)Google Scholar
  4. 4.
    Haag, A., Goronzy, S., Schaich, P., Williams, J.: Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System. In: André, E., Dybkjær, L., Minker, W., Heisterkamp, P. (eds.) ADS 2004. LNCS (LNAI), vol. 3068, pp. 36–48. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Kim, J., Andre, E.: Emotion Recognition Based on Physiological Changes in Music Listening. IEEE Trans. Pattern Anal. Mach. Intell. 30, 2067–2083 (2008)CrossRefGoogle Scholar
  6. 6.
    Picard, R.W., Vyzas, E., Healey, J.: Toward Machine Emotional Intelligence: Analysis of Affective Physiological State. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1175–1191 (2001)CrossRefGoogle Scholar
  7. 7.
    Kim, K., Bang, S., Kim, S.: Emotion recognition system using short-term monitoring of physiological signals. Medical and Biological Engineering and Computing 42, 419–427 (2004)CrossRefGoogle Scholar
  8. 8.
    Lichtenstein, A., Oehme, A., Kupschick, S., Jürgensohn, T.: Comparing Two Emotion Models for Deriving Affective States from Physiological Data. In: Peter, C., Beale, R. (eds.) Affect and Emotion in Human-Computer Interaction. LNCS, vol. 4868, pp. 35–50. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Plarre, K., Raij, A., Hossain, M., Ali, A., Nakajima, M., Al’Absi, M., Ertin, E., Kamarck, T., Kumar, S., Scott, M., Siewiorek, D., Smailagic, A., Wittmers, L.: Continuous Inference of Psychological Stress from Sensory Measurements Collected in the Natural Environment. In: Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), Chicago, IL (April 12-14, 2011)Google Scholar
  10. 10.
    Popivanov, D., Mineva, A.: Testing procedures for non-stationarity and non-linearity in physiological signals. Mathematical Biosciences 157, 303–320 (1999)CrossRefGoogle Scholar
  11. 11.
    Last, M.: Online classification of nonstationary data streams. Intell. Data Anal. 6, 129–147 (2002)MATHGoogle Scholar
  12. 12.
    Kuncheva, L.I.: Classifier Ensembles for Changing Environments. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 1–15. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Sinha, A., Chen, H., Danu, D.G., Kirubarajan, T., Farooq, M.: Estimation and decision fusion: A survey. Neurocomputing 71, 2650–2656 (2008)CrossRefGoogle Scholar
  14. 14.
    Oza, N.C., Tumer, K.: Classifier ensembles: Select real-world applications. Information Fusion 9, 4–20 (2008)CrossRefGoogle Scholar
  15. 15.
    Muhlbaier, M., Polikar, R.: An Ensemble Approach for Incremental Learning in Nonstationary Environments. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 490–500. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): Technical manual and affective ratings. The Center for Research in Psychophysiology, University of Florida, Gainesville, FL (1995) Google Scholar
  17. 17.
    Andreassi, J.L.: Psychophysiology: Human behavior and physiological response. Lawrence Erlbaum Associates Publishers, New Jersey (2007)Google Scholar
  18. 18.
    Kreibig, S.D.: Autonomic nervous system activity in emotion: A review. Biological Psychology 84, 394–421 (2010)CrossRefGoogle Scholar
  19. 19.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann, San Francisco (2005)MATHGoogle Scholar
  20. 20.
    Heijden, F.v.d., Duin, R.P., Ridder, D.d., Tax, D.M.: Classification, parameter estimation and state estimation - an engineering approach using Matlab. John Wiley & Sons, Chichester (2004)CrossRefMATHGoogle Scholar
  21. 21.
    Jain, A.K., Duin, R.P.W., Jianchang, M.: Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 4–37 (2000)CrossRefGoogle Scholar
  22. 22.
    Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31, 264–323 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Omar Alzoubi
    • 1
  • Md. Sazzad Hussain
    • 1
  • Sidney D’Mello
    • 2
  • Rafael A. Calvo
    • 1
  1. 1.School of Electrical and Information EngineeringUniversity of SydneyAustralia
  2. 2.Institute for Intelligent SystemsUniversity of MemphisMemphisUSA

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