EmotionBike: A Study of Provoking Emotions in Cycling Exergames

  • Larissa Müller
  • Sebastian Zagaria
  • Arne Bernin
  • Abbes Amira
  • Naeem Ramzan
  • Christos Grecos
  • Florian Vogt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9353)


In this work, we investigate the effect of how exercise game design elements generate deliberate real-time sensed emotional responses in gamers. Our experimental setup consists of a cycling game controller, a designed 3D first-person cycling game to provoke emotions, a data recording system, and an emotion analysis system. The physical cycling game controller is an enhanced computer controlled bike-exercise-trainer that enables handle bar steering and sets pedal resistance. Our developed 3D first person cycling game provokes emotions with game elements in different game settings: timed race, parcours traversal, and virtual world exploration. Our recording system synchronously captures video, game controller activity, and game events for emotion analysis. In this case study, we show evidence that crafted computer exergame elements are able to provoke subject emotions displayed in their facial expressions, which can be quantified with our developed analysis method. The game elements selected in the specific gameplay situations follow patterns that give inside and judge of individual players involvement and emotional tension. Our emotion analysis of game events provides insights into player reactions during specific game situations. Our results show that strong differing responses by individuals may be taken into account in the design of game mechanics. For example, the falling event of level 3 showed that two opposing strong reactions could be triggered in players. The emotion analysis methods may be used in other types of games. Hereby we believe that a combination of questionnaires and our in situ emotion analysis provide valuable feedback to aid decision in for game design and game mechanics.


Exergame Affective Gaming Physical Activity Cycling Game Big Five Personality Test Facial Expression Emotion Provocation Emotion Recognition 


  1. 1.
    Kotsia, I., Zafeiriou, S., Fotopoulos, S.: Affective gaming: A comprehensive survey. In: Comp. Vis. and Pat. Recog. Works (CVPRW), pp. 663–670. IEEE (2013)Google Scholar
  2. 2.
    Bailenson, J.N., Pontikakis, E.D., Mauss, I.B., Gross, J.J., Jabon, M.E., Hutcherson, C.A., Nass, C., John, O.: Real-time classification of evoked emotions using facial feature tracking and physiological responses. International Journal of Human-Computer Studies 66(5), 303–317 (2008)CrossRefGoogle Scholar
  3. 3.
    Breazeal, C.L.: Sociable machines: expressive social exchange between humans and robots. PhD thesis, Massachusetts Institute of Technology (2000)Google Scholar
  4. 4.
    Kipp, A., Kummert, F.: Dynamic dialog system for human robot collaboration: playing a game of pairs. In: Int. Conf. on Human-Agent Interact., pp. 225–228. ACM (2014)Google Scholar
  5. 5.
    Becker-Asano, C.: WASABI: Affect simulation for agents with believable interactivity, vol. 319. IOS Press (2008)Google Scholar
  6. 6.
    Müller, L., Keune, S., Bernin, A., Vogt, F.: Emotional interaction with surfaces - works of design and computing. In: Herrlich, M., Malaka, R., Masuch, M. (eds.) ICEC 2012. LNCS, vol. 7522, pp. 457–460. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Wahlster, W.: Dialogue systems go multimodal: The smartkom experience. In: SmartKom: Foundations of Multimodal Dialogue Systems, pp. 3–27. Springer (2006)Google Scholar
  8. 8.
    Dumas, B., Lalanne, D., Oviatt, S.: Multimodal interfaces: A survey of principles, models and frameworks. In: Lalanne, D., Kohlas, J. (eds.) Human Machine Interaction. LNCS, vol. 5440, pp. 3–26. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Tan, C.T., Leong, T.W., Shen, S.: Combining think-aloud and physiological data to understand video game experiences. In: ACM Conference on Human Factors in Computing Systems, pp. 381–390. ACM (2014)Google Scholar
  10. 10.
    Munia, T.T.K., Islam, A., Islam, M.M., Mostafa, S.S., Ahmad, M.: Mental states estimation with the variation of physiological signals. In: Informatics, Electronics & Vision (ICIEV), pp. 800–805. IEEE (2012)Google Scholar
  11. 11.
    Sharma, N., Gedeon, T.: Modeling stress recognition in typical virtual environments. In: International Conference on Pervasive Computing Technologies for Healthcare, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), pp. 17–24 (2013)Google Scholar
  12. 12.
    Gunes, H., Pantic, M.: Automatic, dimensional and continuous emotion recognition. International Journal of Synthetic Emotions (IJSE) 1(1), 68–99 (2010)CrossRefGoogle Scholar
  13. 13.
    Calvo, R.A., D’Mello, S.: Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing 1(1), 18–37 (2010)CrossRefGoogle Scholar
  14. 14.
    Ekman, P., Friesen, W.V.: Measuring facial movement. Environmental Psychology and Nonverbal Behavior 1(1), 56–75 (1976)CrossRefGoogle Scholar
  15. 15.
    Littlewort, G., Whitehill, J., Wu, T., Fasel, I., Frank, M., Movellan, J., Bartlett, M.: The computer expression recognition toolbox (cert). In: Automatic Face & Gesture Recognition and Workshops, pp. 298–305. IEEE (2011)Google Scholar
  16. 16.
    Grafsgaard, J.F., Wiggins, J.B., Boyer, K.E., Wiebe, E.N., Lester, J.C.: Automatically recognizing facial indicators of frustration: a learning-centricanalysis. In: Affective Computing and Intelligent Interaction (ACII), pp. 159–165. IEEE (2013)Google Scholar
  17. 17.
    Gilleade, K., Dix, A., Allanson, J.: Affective videogames and modes of affective gaming: assist me, challenge me, emote me. In: Proc. of DIGRA (2005)Google Scholar
  18. 18.
    Vachiratamporn, V., Moriyama, K., Fukui, K.I., Numao, M.: An implementation of affective adaptation in survival horror games. In: Computational Intelligence and Games (CIG), pp. 1–8. IEEE (2014)Google Scholar
  19. 19.
    Nogueira, P.A., Aguiar, R., Rodrigues, R., Oliveira, E.: Computational models of players’ physiological-based emotional reactions: A digital games case study. In: 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 3, pp. 278–285. IEEE (2014)Google Scholar
  20. 20.
    Parnandi, A., Son, Y., Gutierrez-Osuna, R.: A control-theoretic approach to adaptive physiological games. In: Affective Computing and Intelligent Interaction (ACII), pp. 7–12. IEEE (2013)Google Scholar
  21. 21.
    Nacke, L.E., Kalyn, M., Lough, C., Mandryk, R.L.: Biofeedback game design: using direct and indirect physiological control to enhance game interaction. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 103–112. ACM (2011)Google Scholar
  22. 22.
    Negini, F., Mandryk, R., Stanley, K.: Using affective state to adapt characters, npcs, and the environment in a first-person shooter game. In: IEEE Games, Entertainment, and Media, Toronto, Canada, pp. 109–116 (2014)Google Scholar
  23. 23.
    Raaijmakers, S., Steel, F., de Goede, M., van Wouwe, N.C., Van Erp, J.B., Brouwer, A.M.: Heart rate variability and skin conductance biofeedback: A triple-blind randomized controlled study. In: Affective Computing and Intelligent Interaction (ACII), pp. 289–293. IEEE (2013)Google Scholar
  24. 24.
    Biddle, S.J.: Chapter 4: Emotion, mood and physical activity. In: Physical Activity and Psychological Well-Being, p. 63, Psychology Press (2000)Google Scholar
  25. 25.
    Warburton, D.E., Bredin, S.S., Horita, L.T., Zbogar, D., Scott, J.M., Esch, B.T., Rhodes, R.E.: The health benefits of interactive video game exercise. Applied Physiology, Nutrition, and Metabolism 32(4), 655–663 (2007)CrossRefGoogle Scholar
  26. 26.
    Hoda, M., Alattas, R., Saddik, A.E.: Evaluating player experience in cycling exergames. In: Multimedia (ISM), pp. 415–420. IEEE (2013)Google Scholar
  27. 27.
    Satow, L.: Big-five-persoenlichkeitstest(b5t): Test-und skalendokumentation (2012).
  28. 28.
    Borg, G.: Anstrengungsempfinden und körperliche aktivität. Deutsches Ärzteblatt 101(15), 1016–1021 (2004)Google Scholar
  29. 29.
    Hoque, M.E., Picard, R.W.: Acted vs. natural frustration and delight: Many people smile in natural frustration. In: Automatic Face & Gesture Recognition and Workshops, pp. 354–359. IEEE (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Larissa Müller
    • 1
    • 3
  • Sebastian Zagaria
    • 1
  • Arne Bernin
    • 1
    • 3
  • Abbes Amira
    • 3
  • Naeem Ramzan
    • 3
  • Christos Grecos
    • 4
  • Florian Vogt
    • 1
    • 2
  1. 1.Department InformatikUniversity of Applied Sciences (HAW) HamburgHamburgGermany
  2. 2.Innovations Kontakt Stelle (IKS) HamburgHamburgGermany
  3. 3.School of Engineering and ComputingUniversity of the West of ScotlandSouth LanarkshireUK
  4. 4.Independent Imaging ConsultantGlasgowUK

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