Abstract
It is a challenge to make cognitive technical systems more empathetic for user emotions and dispositions. Among channels like facial behavior and nonverbal cues, psychobiological patterns of emotional or dispositional behavior contain rich information, which is continuously available and hardly willingly controlled. However, within this area of research, gender differences or even hormonal cycle effects as potential factors in influencing the classification of psychophysiological patterns of emotions have rarely been analyzed so far.
In our study, emotions were induced with a blocked presentation of pictures from the International Affective Picture System (IAPS) and Ulm pictures. For the automated emotion classification in a first step 5 features from the heart rate signal were calculated and in a second step combined with two features of the facial EMG. The study focused mainly on gender differences in automated emotion classification and to a lesser degree on classification accuracy with Support Vector Machine (SVM) per se. We got diminished classification results for a gender mixed population and also we got diminished results for mixing young females with their hormonal cycle phases. Thus, we could show an improvement of the accuracy rates when subdividing the population according to their gender, which is discussed as a possibility of incrementing automated classification results.
Keywords
Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Reeves, B., Nass, C.: The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. Center for the Study of Language and Information. Cambridge University Press, New York (1996)
Lang, P.J., et al.: Looking at pictures: affective, facial, visceral, and behavioral reactions. Psychophysiology 30(3), 261–273 (1993)
Burriss, L., Powell, D.A., White, J.: Psychophysiological and subjective indices of emotion as a function of age and gender. Cognition & Emotion 21(1), 182–210 (2007)
Cowie, J.M., et al.: A review of Clinical Terms Version 3 (Read Codes) for speech and language record keeping. Int. J. Lang. Commun. Disord. 36(1), 117–126 (2001)
Limbrecht, K., et al.: Advantages of mimic based pre-analysis for neurophysiologic emotion recognition in human-computer interaction. In: F.-M.A. (ed.) Emotional Expression: The Brain and The Face. UFP Press (2012)
Frantzidis, C.A., et al.: Toward emotion aware computing: an integrated approach using multichannel neurophysiological recordings and affective visual stimuli. IEEE Transactions on Information Technology in Biomedicine 14(3), 589–597 (2010)
Bailenson, J.N., et al.: Real-time classification of evoked emotions using facial feature tracking and physiological responses. International Journal of Human-Computer Studies 66, 303–317 (2007)
Ossewaarde, L., et al.: Neural mechanisms underlying changes in stress-sensitivity across the menstrual cycle. Psychoneuroendocrinology 35(1), 47–55 (2010)
Goldstein, J.M., et al.: Sex differences in stress response circuitry activation dependent on female hormonal cycle. Journal of Neuroscience 30(2), 431–438 (2010)
Andreano, J.M., Cahill, L.: Menstrual cycle modulation of medial temporal activity evoked by negative emotion. Neuroimage 53(4), 1286–1293 (2010)
Farage, M.A., Osborn, T.W., MacLean, A.B.: Cognitive, sensory, and emotional changes associated with the menstrual cycle: a review. Archives of Gynecology and Obstetrics 278(4), 299–307 (2008)
Tan, J., et al.: Repeatability of facial electromyography (EMG) activity over corrugator supercilii and zygomaticus major on differentiating various emotions. J. Ambient Intell. Human Comput. 3(3), 3–10 (2012)
Bradley, M.M., et al.: Emotion and motivation II: sex differences in picture processing. Emotion 1(3), 300–319 (2001)
Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): Technical manual and affective ratings. University of Florida, Center for Research in Psychophysiology, Gainesville (1999)
Walter, S., et al.: The influence of neuroticism and psychological symptoms on the assessment of images in three-dimensional emotion space. Psychosoc. Med. 8, Doc04 (2011)
Smith, J.C., Bradley, M.M., Lang, P.J.: State anxiety and affective physiology: effects of sustained exposure to affective pictures. Biological Psychology 69(3), 247–260 (2005)
Valenza, G., Lanata, A., Scilingo, E.P.: The Role of Nonlinear Dynamics in Affective Valence and Arousal Recognition. IEEE Transactions on Affective Computing 99 (2011) (PrePrints)
Rukavina, S., et al.: The influence of the menstrual cycle on gender differences in personality items. In: Annual International Conference of Cognitive and Behavioral Psychology (CBP). Global Science & Technology Forum (GSTF), Singapore (2012)
Fridlund, A.J., Cacioppo, J.T.: Guidelines for human electromyographic research. Psychophysiology 23(5), 567–589 (1986)
Nussinovitch, U., et al.: Reliability of Ultra-Short ECG Indices for Heart Rate Variability. Ann. Noninvasive Electrocardiol. 16(2), 117–122 (2011)
Andrade, A.O., et al.: EMG Decomposition and Artefact Removal, in Computational Intelligence in Electromyography Analysis - A Perspective on Current Applications and Future Challenges, I.-.-.-.-. Ganesh R. Naik, Editor. 2012, InTech.
Neta, M., Norris, C.J., Whalen, P.J.: Corrugator muscle responses are associated with individual differences in positivity-negativity bias. Emotion 9(5), 640–648 (2009)
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
Rukavina, S. et al. (2013). The Impact of Gender and Sexual Hormones on Automated Psychobiological Emotion Classification. In: Kurosu, M. (eds) Human-Computer Interaction. Towards Intelligent and Implicit Interaction. HCI 2013. Lecture Notes in Computer Science, vol 8008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39342-6_52
Download citation
DOI: https://doi.org/10.1007/978-3-642-39342-6_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39341-9
Online ISBN: 978-3-642-39342-6
eBook Packages: Computer ScienceComputer Science (R0)