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On the Need of New Methods to Mine Electrodermal Activity in Emotion-Centered Studies

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7607)

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.

Keywords

  • Hide Markov Model
  • Emotion Recognition
  • Physiological Signal
  • Skin Conductance Response
  • Dynamic Bayesian Network

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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

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  • DOI: https://doi.org/10.1007/978-3-642-36288-0_18

  • Publisher Name: Springer, Berlin, Heidelberg

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