Emotions Detection Through the Analysis of Physiological Information During Video Games Fruition

  • Marco GranatoEmail author
  • Davide Gadia
  • Dario Maggiorini
  • Laura Anna Ripamonti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10653)


Games are interactive tools able to arouse emotions in the user. This is particularly relevant in Serious Games, where the main goal could be educational, pedagogical, etc. Therefore, understanding the players’ emotions during the game fruition could provide a valid support to the developers and researchers in video games field in order to design a more effective product. The presented research is a starting point to propose a framework for the determination of the player emotions through physiological information. We acquire several signals: facial electromyography, electrocardiogram, galvanic skin response, and respiration rate. We then compare the data to an emotional player assessment, defined using a valence and an arousal vector, through the application of Machine Learning techniques. The obtained results seem to suggest that the proposed approach can represent a valid tool to analyze the players’ emotions.


Video game Serious game Emotion detection Machine Learning Feature selection Physiological data Affective Computing 


  1. 1.
    Fullerton, T.: Game Design Workshop: A Playcentric Approach to Creating Innovative Games. CRC Press, Boca Raton (2014)Google Scholar
  2. 2.
    Damasio, A.R., Tranel, D., Damasio, H.C.: Behavior: theory and preliminary testing. In: Frontal Lobe Function and Dysfunction, p. 217 (1991)Google Scholar
  3. 3.
    Damásio, A.R.: Descartes’ Error: Emotion, Reason, and the Human Brain. Quill (1994)Google Scholar
  4. 4.
    Grodal, T.: Video games and the pleasures of control. In: Media Entertainment: The Psychology of its Appeal, pp. 197–213 (2000)Google Scholar
  5. 5.
    Granic, I., Lobel, A., Engels, R.C.M.E.: The benefits of playing video games. Am. Psychol. 69(1), 66 (2014)CrossRefGoogle Scholar
  6. 6.
    Isbister, K.: How Games Move Us: Emotion by Design. MIT Press, Cambridge (2016)Google Scholar
  7. 7.
    Bateman, C.: Beyond gAme Design: Nine Steps Toward Creating Better Videogames. Cengage Learning (2009)Google Scholar
  8. 8.
    Höök, K.: Affective loop experiences – what are they? In: Oinas-Kukkonen, H., Hasle, P., Harjumaa, M., Segerståhl, K., Øhrstrøm, P. (eds.) PERSUASIVE 2008. LNCS, vol. 5033, pp. 1–12. Springer, Heidelberg (2008). CrossRefGoogle Scholar
  9. 9.
    Michael, D.R., Chen, S.L.: Serious Games: Games that eDucate, Train, and Inform. Muska & Lipman/Premier-Trade, Cengage (2005)Google Scholar
  10. 10.
    Ho, T.K.: Random decision forests. In: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282. IEEE (1995)Google Scholar
  11. 11.
    Koster, R.: Theory of Fun for Game Design. O’Reilly Media Inc., Sebastopol (2013)Google Scholar
  12. 12.
    Ravaja, N., Turpeinen, M., Saari, T., Puttonen, S., Keltikangas-Järvinen, L.: The psychophysiology of James Bond: phasic emotional responses to violent video game events. Emotion 8(1), 114 (2008)CrossRefGoogle Scholar
  13. 13.
    Salen, K., Zimmerman, E.: Rules of Play: Game Design Fundamentals. MIT Press, Cambridge (2004)Google Scholar
  14. 14.
    Lazzaro, N.: Why we play games: four keys to more emotion without story (2004).
  15. 15.
    Yannakakis, G.N., Paiva, A.: Emotion in games. In: Handbook on Affective Computing, pp. 459–471 (2014)Google Scholar
  16. 16.
    Draper, V.J., Kaber, D.B., Usher, J.M.: Telepresence. Hum. Factors 40(3), 354–375 (1998)CrossRefGoogle Scholar
  17. 17.
    Nakamura, J., Csikszentmihalyi, M.: The concept of flow. Flow and the Foundations of Positive Psychology, pp. 239–263. Springer, Dordrecht (2014). Google Scholar
  18. 18.
    Ravaja, N., Salminen, M., Holopainen, J., Saari, T., Laarni, J., Järvinen, A.: Emotional response patterns and sense of presence during video games: potential criterion variables for game design. In: Proceedings of the Third Nordic Conference on Human-Computer Interaction, pp. 339–347. ACM (2004)Google Scholar
  19. 19.
    James, W.: What is an emotion? Mind 9(34), 188–205 (1884)CrossRefGoogle Scholar
  20. 20.
    Mauss, B.I., Robinson, M.D.: Measures of emotion: a review. Cogn. Emot. 23(2), 209–237 (2009)CrossRefGoogle Scholar
  21. 21.
    Picard, R.W.: Affective computing (1995)Google Scholar
  22. 22.
    Hudlicka, E.: Affective computing for game design. In: Proceedings of the 4th International North American Conference on Intelligent Games and Simulation (GAMEON-NA) (2008)Google Scholar
  23. 23.
    Nacke, L.E.: An introduction to physiological player metrics for evaluating games. In: Seif El-Nasr, M., Drachen, A., Canossa, A. (eds.) Game Analytics, pp. 585–619. Springer, London (2013). CrossRefGoogle Scholar
  24. 24.
    Ninaus, M., Kober, S.E., Friedrich, E.V.C., Dunwell, I., De Freitas, S., Arnab, S., Ott, M., Kravcik, M., Lim, T., Louchart, S., et al.: Neurophysiological methods for monitoring brain activity in serious games and virtual environments: a review. Int. J. Technol. Enhanced Learn. 6(1), 78–103 (2014)CrossRefGoogle Scholar
  25. 25.
    Ekman, P.: An argument for basic emotions. Cogn. Emot. 6(3–4), 169–200 (1992)CrossRefGoogle Scholar
  26. 26.
    Bradley, M.M., Greenwald, M.K., Petry, M.C., Lang, P.J.: Remembering pictures: pleasure and arousal in memory. J. Exp. Psychol. Learn. Mem. Cogn. 18(2), 379–390 (1992)CrossRefGoogle Scholar
  27. 27.
    Lang, P.J.: The emotion probe: studies of motivation and attention. Am. Psychol. 50(5), 372 (1995)CrossRefGoogle Scholar
  28. 28.
    Bradley, M.M., Lang, P.J.: Measuring emotion: the self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 25(1), 49–59 (1994)CrossRefGoogle Scholar
  29. 29.
    Betella, A., Verschure, P.F.M.J.: The affective slider: a digital self-assessment scale for the measurement of human emotions. PLoS ONE 11(2), e0148037 (2016)CrossRefGoogle Scholar
  30. 30.
    Ripamonti, L.A., Mannalà, M., Gadia, D., Maggiorini, D.: Procedural content generation for platformers: designing and testing FUN PLEdGE. In: Multimedia Tools and Applications, pp. 1–50 (2016)Google Scholar
  31. 31.
    Mazza, C., Ripamonti, L.A., Maggiorini, D., Gadia, D.: Fun pledge 2.0: a funny platformers levels generator (rhythm based). In: To be presented at CHItaly 2017: the Biannual Conference of the Italian SIGCHI Chapter, Cagliari, Italy, September 2017 (2017)Google Scholar
  32. 32.
    Stojanović, R., Čaplánová, A., Kovačević, Ž., Nemanja, N., Bundalo, Z.: Alternative approach to addressing infrastructure needs in biomedical engineering programs (case of emerging economies). Folia Medica Facultatis Medicinae Universitatis Saraeviensis, 50(1) (2015)Google Scholar
  33. 33.
    Conover, M.B.: Understanding Electrocardiography. Elsevier Health Sciences (2003)Google Scholar
  34. 34.
    Van Boxtel, A.: Facial EMG as a tool for inferring affective states. In: Proceedings of measuring behavior, pp. 104–108. Noldus Information Technology Wageningen (2010)Google Scholar
  35. 35.
    Critchley, H.D.: Electrodermal responses: what happens in the brain. Neuroscientist 8(2), 132–142 (2002)CrossRefGoogle Scholar
  36. 36.
    Darwin, C., Ekman, P., Prodger, P.: The Expression of the Emotions in Man and Animals. Oxford University Press, USA (1998)Google Scholar
  37. 37.
    Venables, P.H., Christie, M.J.: Electrodermal activity. Tech. Psychophysiol. 54(3), 3–67 (1980)Google Scholar
  38. 38.
    Ds18b20 datasheet. Accessed 21 July 2017
  39. 39.
    Savitzky, A., Golay, M.J.E.: Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36(8), 1627–1639 (1964)CrossRefGoogle Scholar
  40. 40.
    Lavielle, M.: Detection of multiple changes in a sequence of dependent variables. Stochast. Processes Appl. 83(1), 79–102 (1999)MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Hamilton, P.: Open source ECG analysis. In: Computers in Cardiology, pp. 101–104. IEEE (2002)Google Scholar
  42. 42.
    Loh, W.-Y.: Regression tress with unbiased variable selection and interaction detection. Statistica Sinica 12, 361–386 (2002)MathSciNetzbMATHGoogle Scholar
  43. 43.
    Bonab, H.R., Can, F.: A theoretical framework on the ideal number of classifiers for online ensembles in data streams. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 2053–2056. ACM (2016)Google Scholar
  44. 44.
    Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, Stanford, CA, vol. 14, pp. 1137–1145 (1995)Google Scholar
  45. 45.
    Csikszentmihalyi, M.: Flow and the Psychology of Discovery and Invention. Harper Collins, New York (1996)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Marco Granato
    • 1
    Email author
  • Davide Gadia
    • 1
  • Dario Maggiorini
    • 1
  • Laura Anna Ripamonti
    • 1
  1. 1.Department of Computer ScienceUniversity of MilanMilanItaly

Personalised recommendations