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the prerequisites can contribute to or even guide the
promising future ASP provides us with.
ACKNOWLEDGEMENTS
The authors would like to thank Joyce H.D.M. West-
erink (Philips Research, Eindhoven, The Netherlands)
for her comments on an earlier versions of this pa-
per. Furthermore, we would like to thank the anony-
mous reviewers, who provided us the opportunity to
improve this paper.
REFERENCES
Berzal, F., Cubero, J.-C., S´anchez, D., and Serrano, J. M.
(2004). ART: A hybrid classification model. Machine
Learning, 54(1):67–.
Cacioppo, J. and Tassinary, L. (1990). Inferring psycholog-
ical significance from physiological signals. American
Psychologist, 45(1):16–28.
Chanel, G., Kierkels, J. J. M., Soleymani, M., and Pun,
T. (2009). Short-term emotion assessment in a recall
paradigm. International Journal of Human-Computer
Studies, 67(8):607–627.
Cheng, B. and Liu, G. (2008). Emotion recognition from
surface EMG signal using wavelet transform and neu-
ral network. In Proceedings of The 2nd International
Conference on Bioinformatics and Biomedical Engineer-
ing (ICBBE), pages 1363–1366, Shanghai, China. Piscat-
away, NJ, USA: IEEE Press.
Choi, A. and Woo, W. (2005). Physiological sensing and
feature extraction for emotion recognition by exploit-
ing acupuncture spots. Lecture Notes in Computer Sci-
ence (Affective Computing and Intelligent Interaction),
3784:590–597.
Gamboa, H., Silva, F., Silva, H., and Falc˜ao, R. (2009).
PLUX – Biosignals Acquisition and Processing. URL:
http://www.plux.info
[Last accessed on November
02, 2009].
Gunes, H. and Piccardi, M. (2009). Automatic temporal
segment detection and affect recognition from face and
body display. IEEE Transactions on Systems, Man, and
Cybernetics – Part B: Cybernetics, 39(1):64–84.
H˚akansson, J. and Montgomery, H. (2003). Empathy as an
interpersonal phenomenon. Journal of Social and Per-
sonal Relationships, 20(3):267–284.
Healey, J. A. (2009). Affect detection in the real world:
Recording and processing physiological signals. In Pro-
ceedings of the IEEE 3rd International Conference on
Affective Computing and Intelligent Interaction, ACII,
volume 1, pages 729–734, Amsterdam, The Netherlands.
IEEE Press.
Healey, J. A. and Picard, R. W. (2005). Detecting stress
during real-world driving tasks using physiological sen-
sors. IEEE Transactions on Intelligent Transportation
Systems, 6(2):156–166.
Janssen, J. H., van den Broek, E. L., and Westerink, J. H.
D. M. (2009). Personalized affective music player. In
Proceedings of the IEEE 3rd International Conference
on Affective Computing and Intelligent Interaction, ACII,
volume 1, pages 472–477, Amsterdam, The Netherlands.
IEEE Press.
Jones, C. M. and Troen, T. (2007). Biometric valence and
arousal recognition. In Thomas, B. H., editor, Proceed-
ings of the Australasian Computer-Human Interaction
Conference (OzCHI), pages 191–194, Adelaide, Aus-
tralia.
Kim, J. (2007). Bimodal emotion recognition using speech
and physiological changes, chapter 15, pages 265–280.
Vienna, Austria: I-Tech Education and Publishing.
Kim, J., Andr´e, E., Rehm, M., Vogt, T., and Wagner, J.
(2005). Integrating information from speech and phys-
iological signals to achieve emotional sensitivity. In
Proceedings of the 9th European Conference on Speech
Communication and Technology, pages 809–812, Lis-
boa, Portugal. Lisboa, Portugal: L2F – Spoken Language
Systems Laboratory.
Kim, K. H., Bang, S. W., and Kim, S. R. (2002). Develop-
ment of person-independent emotion recognition system
based on multiple physiological signals. In Proceedings
of the Second Joint EMBS–BMES Conference, volume 1:
Bioengineering – Integrative Methodologies, New Tech-
nologies, pages 50–51, Houston, Texas, USA.
Kim, K. H., Bang, S. W., and Kim, S. R. (2004). Emotion
recognition system using short-term monitoring of phys-
iological signals. Medical & Biological Engineering &
Computing, 42(3):419–427.
Krohne, H. W., Pieper, M., Knoll, N., and Breimer, N.
(2002). The cognitive regulation of emotions: The role
of success versus failure experience and coping disposi-
tions. Cognition & Emotion, 16(2):217–243.
Kuncheva, L. I. (2002). A theoretical study on six classifier
fusion techniques. IEEE Transactions on Pattern Analy-
sis and Machine Intelligence, 24(2):281–286.
Lam, L. and Suen, C. Y. (1995). Optimal combina-
tions of pattern classifiers. Pattern Recognition Letters,
16(9):945–954.
Lichtenstein, A., Oehme, A., Kupschick, S., and
J¨urgensohn, T. (2008). Comparing two emotion mod-
els for deriving affective states from physiological data.
Lecture Notes in Computer Science (Affect and Emotion
in Human-Computer Interaction), 4868:35–50.
Lisetti, C. L. and Nasoz, F. (2002). MAUI: a multimodal
affective user interface. In Proceedings of the tenth ACM
international conference on Multimedia, pages 161–170,
Juan les Pins, France. New York, NY, USA: ACM Press.
Lisetti, C. L. and Nasoz, F. (2005). Affective intelligent car
interfaces with emotion recognition. In Proceedings of
11th International Conference on Human-Computer In-
teraction, pages [on cd–rom], Las Vegas, Nevada, USA.
Mahwah, NJ, USA: Lawrence Erlbaum Associates, Inc.
Liu, C., Rani, P., and Sarkar, N. (2005). An empirical
study of machine learning techniques for affect recog-
nition in human-robot interaction. In Proceedings of
the IEEE/RSJ International Conference on Intelligent
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