By Arvid Kappas (auth.), Sidney D’Mello, Arthur Graesser, Björn Schuller, Jean-Claude Martin (eds.)
The two-volume set LNCS 6974 and LNCS 6975 constitutes the refereed complaints of the Fourth foreign convention on Affective Computing and clever interplay, ACII 2011, held in Memphis,TN, united states, in October 2011.
The one hundred thirty five papers during this quantity set offered including three invited talks have been conscientiously reviewed and chosen from 196 submissions. The papers are equipped in topical sections on reputation and synthesis of human impact, affect-sensitive functions, methodological concerns in affective computing, affective and social robotics, affective and behavioral interfaces, appropriate insights from psychology, affective databases, assessment and annotation tools.
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Additional info for Affective Computing and Intelligent Interaction: 4th International Conference, ACII 2011, Memphis, TN, USA, October 9–12, 2011, Proceedings, Part I
Comparing Two Emotion Models for Deriving Affective States from Physiological Data. , Beale, R. ) Affect and Emotion in Human-Computer Interaction. LNCS, vol. 4868, pp. 35–50. Springer, Heidelberg (2008) 9. : 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) 10. : Testing procedures for non-stationarity and non-linearity in physiological signals.
4472, pp. 490–500. Springer, Heidelberg (2007) 16. : International affective picture system (IAPS): Technical manual and affective ratings. The Center for Research in Psychophysiology, University of Florida, Gainesville, FL (1995) 17. : Psychophysiology: Human behavior and physiological response. Lawrence Erlbaum Associates Publishers, New Jersey (2007) 18. : Autonomic nervous system activity in emotion: A review. Biological Psychology 84, 394–421 (2010) 19. : Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn.
The literature is rife with a number of physiological-based affect detection systems that classify discrete emotions as well as primitive affective dimensions such as valence and arousal [4-6]. Despite impressive classification performance under controlled laboratory conditions [7, 8], the stochastic nature of physiological signals poses significant challenges when one moves from the lab and into the real world . In particular, physiological data is expected to exhibit day variations , which introduce problems when previously trained models are used to generate predictions in the future; this is an important requirement for real world applications.
Affective Computing and Intelligent Interaction: 4th International Conference, ACII 2011, Memphis, TN, USA, October 9–12, 2011, Proceedings, Part I by Arvid Kappas (auth.), Sidney D’Mello, Arthur Graesser, Björn Schuller, Jean-Claude Martin (eds.)