Abstract
Awareness of the emotion in human–computer interaction is a challenging task when building human-centered computing systems. Emotion is a complex state of mind that is affected by external events, physiological changes and, generally, human relationships. Researchers suggest various methods of measuring human emotions through the analysis of physiological signals, facial expressions, voice, etc. This chapter presents a system for recognizing the emotions of a smartphone user through the collection and analysis of data generated from different types of sensors on the device. Data collection is carried out by an application installed on the participants’ smartphone provided that the smartphone remains in their pocket throughout the experiments. The collected data are processed and utilized to train different classifiers (decision trees, naïve Bayes and k-nearest neighbors). Emotions are classified in the following six categories: happiness, neutral, sadness, disgust, fear, surprise. Initial results show that the system classifies user’s emotions with 82.83% accuracy. The proposed system applied to a smartphone demonstrates the feasibility of an emotion recognition approach through a user-friendly scenario for users’ activity recognition.
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
References
Picard, R.W.: Affective Computing. The MIT press, Cambridge (1997)
Russell, J.A., Barrett, F.L.: Core affect. In: Sander, D., Scherer, K.R. (eds.) The Oxford Companion to Emotion and the Affective Sciences, p. 104. Oxford University Press, New York (2009)
Russell, J. A, Barrett, L. F. (1999). Core affect, prototypical emotional episodes, and other things called Emotion: Dissecting the elephant. J. Pers. Soc. Psychol. 76, 805
Frijda, N.H.: Moods, emotion episodes and emotions. In: Lewis, M., Haviland, J.M. (eds.) Handbook of Emotions. Guilford Press, Heidelberg (1993)
Weiss, H. M., Cropanzano, R.: Affective events theory: A theoretical discussion of the structure, causes and consequences of affective experiences at work, vol. 18. In: Staw, B. M., Cummings, L. L. (eds.) Research in Organizational Behavior, CT, JAI Press, Greenwich (1996)
Frijda, N.H.: Mood. In: Sander, D., Scherer, K.R. (eds.) The Oxford Companion to Emotion and the Affective Sciences, pp. 258–259. Oxford University Press, New York (2009)
Ekman, P., Davidson, R.J.: The Nature of Emotion: Fundamental Questions. Oxford University Press, USA (1994)
Tomkins, S. S.: Affect theory. In: Scherer, K. R., Ekman, P. (eds.) Approaches to Emotion (1984)
Lövheim, H.: A new three-dimensional model for emotions and monoamine neurotransmitters. Med. Hypotheses 78(2), 341–348 (2011)
Posner, J., Russell, A.J., Peterson, B.: The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17(3), 715–734 (2005)
Kim, K.H., Bang, S.W., Kim, S.R.: Emotion recognition system using short-term monitoring of physiological signals. Med. Biol. Eng. Compu. 42(3), 419–427 (2004)
Epp, C., Lippold, M., Mandryk, R.: Identifying emotional states using keystroke dynamics. Proceedings Conference on Human Factors in Computing Systems, Canada, Vancouver, pp. 715–724 (2011)
Bailenson, J.N., Pontikakis, E., Mauss, I., Gross, J.J., Jabon, M., Hutcherson, C., Nass, C., Oliver, J.: Real-time classification of evoked emotions using facial feature tracking and physiological responses. Int. J. Hum Comput Stud. 66(5), 303–317 (2008)
Likamwa, R., Liu, Y., Lane, N. D., Zhong, L.: MoodScope: Building a mood sensor from smartphone usage patterns. MobiSys 2013—Proceedings of the 11th Annual International Conference on Mobile Systems, Applications and Services (2013)
Kallipolitis, A., Galliakis, M., Menychtas, A., Maglogiannis, I.: Emotion analysis in hospital bedside infotainment platforms using speeded up robust features, vol. 559. In: MacIntyre, J., Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) Artificial Intelligence Applications and Innovations. AIAI 2019. IFIP Advances in Information and Communication Technology, Springer, Cham (2019)
Menychtas, A., Galliakis, M., Tsanakas, P., Maglogiannis, I.: Real time Integration of Emotion Analysis into Healthcare Platforms, EMBC (2019)
https://docs.microsoft.com/en-us/azure/cognitive-services/emotion/home
Hofmann, M., Kilnkenberg, R.: RapidMiner: Data Mining and Business Application Use Cases. Taylor & Francis, CRS Press (2013)
Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 20(1), 37–46 (1960)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer-Verlag GmbH Germany, part of Springer Nature
About this chapter
Cite this chapter
Kalogirou, I.P., Kallipolitis, A., Maglogiannis, I. (2020). Passive Emotion Recognition Using Smartphone Sensing Data. In: Maglogiannis, I., Brahnam, S., Jain, L. (eds) Advanced Computational Intelligence in Healthcare-7. Studies in Computational Intelligence, vol 891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-61114-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-662-61114-2_2
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-61112-8
Online ISBN: 978-3-662-61114-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)