Abstract
Monitoring the electrodermal activity is increasingly accomplished in agent-based experimental settings as the skin is believed to be the only organ to react only to the sympathetic nervous system. This physiological signal has the potential to reveal paths that lead to excitement, attention, arousal and anxiety. However, electrodermal analysis has been driven by simple feature-extraction, instead of using expressive models that consider a more flexible behavior of the signal for improved emotion recognition. This paper proposes a novel approach centered on sequential patterns to classify the signal into a set of key emotional states. The approach combines SAX for pre-processing the signal and hidden Markov models. This approach was tested over a collected sample of signals using Affectiva-QSensor. An extensive human-to-human and human-to-robot experimental setting is under development for further validation and characterization of emotion-centered patterns.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Andreassi, J.: Psychophysiology: Human Behavior and Physiological Response. In: Psychophysiology: Human Behavior & Phy. Response. Lawrence Erlbaum (2007)
Ben-Shakhar, G.: A Further Study of the Dichotomization Theory in Detection of Information. Psychophysiology 14, 408–413 (1977)
Ben-Shakhar, G.: Standardization within individuals: A simple method to neutralize individual differences in skin conductance. Psychophy 22(3), 292–299 (1985)
Bilmes, J.A.: What hmms can do. IEICE Journal E89-D(3), 869–891 (2006)
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus (2006)
Bos, D.O.: Eeg-based emotion recognition the influence of visual and auditory stimuli. Emotion 57(7), 1798–1806 (2006)
Brown, G., Birley, J., Wing, J.: Influence of family life on the course of schizophrenic disorders: a replication. B.J. of Psychiatry 121(562), 241–258 (1972)
Cacioppo, J., Tassinary, L., Berntson, G.: Handbook of psychophysiology. Cambridge University Press (2007)
Cao, L.: Data mining and multi-agent integration. Springer, Dordrecht (u.a) (2009)
Chang, C., Zheng, J., Wang, C.: Based on support vector regression for emotion recognition using physiological signals. In: IJCNN, pp. 1–7 (2010)
Crider, A.: Electrodermal response lability-stability: Individual difference correlates. In: Prog. in Electrod. Research, vol. 249, pp. 173–186. Springer, US (1993)
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.J.: Querying and mining of time series data: experimental comparison of representations and distance measures. PVLDB 1(2), 1542–1552 (2008)
Ekman, P., Friesen, W.: Universals and cultural differences in the judgments of facial expressions of emotion. J. of Personality and Social Psychology 53, 712–717 (1988)
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)
Jerritta, S., Murugappan, M., Nagarajan, R., Wan, K.: Physiological signals based human emotion recognition: a review. In: 2011 IEEE 7th International Colloquium on Signal Processing and its Applications (CSPA), pp. 410–415 (2011)
Katsis, C., Katertsidis, N., Ganiatsas, G., Fotiadis, D.: Toward emotion recognition in car-racing drivers: A biosignal processing approach. IEEE Transactions on Systems, Man and Cybernetics, Systems and Humans 38(3), 502–512 (2008)
Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): Technical Manual and Affective Ratings. NIMH (1997)
Lang, P.: The emotion probe: Studies of motivation and attention. American Psychologist 50, 372–372 (1995)
Lessard, C.S.: Signal Processing of Random Physiological Signals. Synthesis Lectures on Biomedical Engineering, Morgan and Claypool Publishers (2006)
Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: ACM SIGMOD Workshop on DMKD, pp. 2–11. ACM, New York (2003)
Lorber, M.F.: Psychophysiology of aggression, psychopathy, and conduct problems: a meta-analysis. Psychological Bulletin 130(4), 531–552 (2004)
Lykken, D.T.: The gsr in the detection of guilt. J. A. Psych. 43(6), 385–388 (1959)
Lykken, D.: A study of anxiety in the sociopathic personality. U. Minnesota (1955)
Maaoui, C., Pruski, A., Abdat, F.: Emotion recognition for human-machine communication. In: IROS, pp. 1210–1215. IEEE/RSJ (September 2008)
Mitsa, T.: Temporal Data Mining. In: DMKD. Chapman & Hall/CRC (2009)
Murphy, K.: Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. thesis, UC Berkeley, Computer Science Division (July 2002)
Oatley, K., Keltner, Jenkins: Understanding Emotions. Blackwell P. (2006)
Petrantonakis, P.C., Hadjileontiadis, L.J.: Emotion recognition from eeg using higher order crossings. Trans. Info. Tech. Biomed. 14(2), 186–197 (2010)
Picard, R.W.: Affective computing: challenges. International Journal of Human-Computer Studies 59(1-2), 55–64 (2003)
Rabiner, L., Juang, B.: An introduction to hidden Markov models. ASSP Magazine 3(1), 4–16 (2003)
Rigas, G., Katsis, C.D., Ganiatsas, G., Fotiadis, D.I.: A User Independent, Biosignal Based, Emotion Recognition Method. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 314–318. Springer, Heidelberg (2007)
Schell, A.M., Dawson, M.E., Filion, D.L.: Psychophysiological correlates of electrodermal lability. Psychophysiology 25(6), 619–632 (1988)
Shieh, J., Keogh, E.: isax: indexing and mining terabyte sized time series. In: ACM SIGKDD, KDD 2008, pp. 623–631. ACM, New York (2008)
Tranel, D., Damasio, H.: Neuroanatomical correlates of electrodermal skin conductance responses. Psychophysiology 31(5), 427–438 (1994)
Villon, O., Lisetti, C.: Toward recognizing individual’s subjective emotion from physiological signals in practical application. In: Computer-Based Medical Systems, pp. 357–362 (2007)
Viterbi, A.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. on Inf. Theory 13(2), 260–269 (1967)
Vyzas, E.: Recognition of Emotional and Cognitive States Using Physiological Data. Master’s thesis. MIT (1999)
Wagner, J., Kim, J., Andre, E.: From physiological signals to emotions: Implementing and comparing selected methods for feature extraction and classification. In: ICME, pp. 940–943. IEEE (2005)
Wu, C.K., Chung, P.C., Wang, C.J.: Extracting coherent emotion elicited segments from physiological signals. In: WACI, pp. 1–6. IEEE (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Henriques, R., Paiva, A., Antunes, C. (2013). On the Need of New Methods to Mine Electrodermal Activity in Emotion-Centered Studies. In: Cao, L., Zeng, Y., Symeonidis, A.L., Gorodetsky, V.I., Yu, P.S., Singh, M.P. (eds) Agents and Data Mining Interaction. ADMI 2012. Lecture Notes in Computer Science(), vol 7607. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36288-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-642-36288-0_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36287-3
Online ISBN: 978-3-642-36288-0
eBook Packages: Computer ScienceComputer Science (R0)