Skip to main content

A User Independent, Biosignal Based, Emotion Recognition Method

  • Conference paper
User Modeling 2007 (UM 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4511))

Included in the following conference series:

Abstract

A physiological signal based emotion recognition method, for the assessment of three emotional classes: happiness, disgust and fear, is presented. Our approach consists of four steps: (i) biosignal acquisition, (ii) biosignal preprocessing and feature extraction, (iii) feature selection and (iv) classification. The input signals are facial electromyograms, the electrocardiogram, the respiration and the electrodermal skin response. We have constructed a dataset which consists of 9 healthy subjects. Moreover we present preliminary results which indicate on average, accuracy rates of 0.48,0.68 and 0.69 for recognition of happiness, disgust and fear emotions, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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)

    Article  Google Scholar 

  2. Fujita, M., Takagi, T., Hasegawa, R., Arkin, R.C.: Ethnological modeling and architecture for an entertainment robot. pp. 453–458 (2001)

    Google Scholar 

  3. Cohen, M.H., Moser, M.C., Hasha, R., Flanagan, J.L., Hirsh, H.: Room service, al-style. IEEE intelligent systems 14, 8–19 (1999)

    Google Scholar 

  4. Picard, R.W.: Affective computing. MIT Press, Cambridge (1995)

    Google Scholar 

  5. Goronzy, S., Schaich, P., Williams, J., Haag, A.: Recognition using bio-sensors: First steps towards an automatic system, pp. 36–48. Springer, Heidelberg (2004)

    Google Scholar 

  6. Bang, S.W., Kim, S.R., Kim, K.H.: Emotion recognition system using short-term monitoring of physiological signals. Medical Biological Engineering and Computers 42, 419–427 (2004)

    Article  Google Scholar 

  7. Dryer, D.C., Lu, D.J., Ark, W.: The emotion mouse. In: 8th International Conference Human Computer interaction, pp. 818–823 ( 1999)

    Google Scholar 

  8. Van Dijk, V., Jonas, I.E., Zwarts, M.J., Stegeman, D.F., Lapatki, B.G.: A thin, flexible multielectrode grid for high-density surface emg. J. Appl Physiol 96, 327–336 (2004)

    Article  Google Scholar 

  9. http://cortechsolutions.com/g.sensors.htm (Last visited 10-11- (2006)

  10. Ghman, A., Vaitl, D., Lang, P.J.: The international affective picture system [photographic slides]. Technical report, Gainesville, The Center for Research in Psychophysiology, University of Florida (1988)

    Google Scholar 

  11. Greenwald, M.K., Bradley, M.M., Hamm, A.O., Lang, P.J.: Looking at pictures: evaluative, facial, visceral and behavioral responses. Psychophysiology (1993)

    Google Scholar 

  12. Bradley, M., Bowers, D., Lang, P., Heilman, K., Morris, M.: Valence-specific hypoarousal following right temporal lobectomy (1991)

    Google Scholar 

  13. Katsis, C.D., Ganiatsas, G., Fotiadis, D.I.: An integrated telemedicine platform for the assessment of affective physiological states. Diagnostic Pathology 1, 1–16 (2006)

    Article  Google Scholar 

  14. Navot, A., Tishby, N., Gilad-Bachrach, R.: Margin based feature selection - theory and algorithms. In: Proc. 21International Conference on Machine Learning (ICML) (2004)

    Google Scholar 

  15. Darrell, T., Indyk, P., Shakhnarovish, G. (eds.): Nearest-Neighbor Methods in Learning and Vision. MIT Press, Cambridge (2005)

    Google Scholar 

  16. Breiman, L.: Random forests. Machine Learning, 45 (2001)

    Google Scholar 

  17. Frank, E., Witten, I.H.: Data Mining: Practical machine learning tools and techniques, 2nd edn. San Francisco (2005)

    Google Scholar 

  18. Pearson, K.: On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2, 559–572 (1901)

    Google Scholar 

  19. Schell, A.M., Filion, D.L., Dawson, M.E.: Handbook of psychophysiology. Cambridge University Press, Cambridge (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Cristina Conati Kathleen McCoy Georgios Paliouras

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rigas, G., Katsis, C.D., Ganiatsas, G., Fotiadis, D.I. (2007). A User Independent, Biosignal Based, Emotion Recognition Method. In: Conati, C., McCoy, K., Paliouras, G. (eds) User Modeling 2007. UM 2007. Lecture Notes in Computer Science(), vol 4511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73078-1_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73078-1_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73077-4

  • Online ISBN: 978-3-540-73078-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics