Journal on Multimodal User Interfaces

, Volume 9, Issue 1, pp 31–42 | Cite as

A comparative study of game mechanics and control laws for an adaptive physiological game

  • Avinash ParnandiEmail author
  • Ricardo Gutierrez-Osuna
Original Paper


We present an adaptive biofeedback game that aims to maintain the player’s arousal by modifying game difficulty in response to the player’s physiological state, as measured with wearable sensors. Our approach models the interaction between human physiology and game difficulty during gameplay as a control problem, where game difficulty is the system input and player arousal its output. We validate the approach on a car-racing game with real-time adaptive game mechanics. Specifically, we use (1) car speed, road visibility, and steering jitter as three mechanisms to manipulate game difficulty, (2) electrodermal activity as physiological correlate of arousal, and (3) two types of control law: proportional (P) control, and proportional-integral-derivative (PID) control. We also propose quantitative measures to characterize the effectiveness of these game adaptations and controllers in manipulating the player’s arousal. Experimental trials with 25 subjects in both open-loop (no feedback) and closed-loop (negative feedback) conditions show statistically significant differences in effectiveness among the three game mechanics and also between the two control laws. Specifically, manipulating car speed provides higher control of arousal levels than changing road visibility or vehicle steering. Our results also confirm that PID control leads to lower error and reduced oscillations in the closed-loop response compared to proportional-only control. Finally, we discuss the theoretical and practical implications of our approach.


Physiological games Dynamic game balancing Control theory 



This publication was made possible by NPRP Grant # 5-678-2-282 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.


  1. 1.
    Kivikangas JM, Ekman I, Chanel G, Järvelä S, Salminen M, Cowley B et al (2010) Review on psychophysiological methods in game research. presented at the 1st Nordic DiGRAGoogle Scholar
  2. 2.
    Nacke LE, Kalyn M, Lough C, Mandryk RL (2011), Biofeedback game design: using direct and indirect physiological control to enhance game interaction. presented at the SIGCHI Conference on Human Factors in, Computing Systems, 103–112Google Scholar
  3. 3.
    Vilozni D, Barker M, Jellouschek H, Heimann G, Blau H (2001) An interactive computer-animated system (SpiroGame) facilitates spirometry in preschool children. Am J Respir Crit Care Med 164:2200–2205CrossRefGoogle Scholar
  4. 4.
    Pope AT, Palsson OS (2001) Helping video games rewire our minds. presented at the playing by the rules: the cultural challenges of video gamesGoogle Scholar
  5. 5.
    Rani P, Sarkar N, Liu C (2005) Maintaining optimal challenge in computer games through real-time physiological feedback. In Proceedings of the 11th International Conference on Human Computer Interaction 184–192Google Scholar
  6. 6.
    Hettinger L, Branco P, Encarnacao L, Bonato P (2003) Neuroadaptive technologies: applying neuroergonomics to the design of advanced interfaces. Theor Issues Ergon Sci 4:220–237CrossRefGoogle Scholar
  7. 7.
    Kuo BC, Golnaraghi MF (2003) Automatic control systems, vol. 1: WileyGoogle Scholar
  8. 8.
    Dorf RC (1991) Modern control systems. Addison-Wesley Longman Publishing Co., IncGoogle Scholar
  9. 9.
    Byrne EA, Parasuraman R (1996) Psychophysiology and adaptive automation. Biol Psychol 42:249–268CrossRefGoogle Scholar
  10. 10.
    Prinzel LJ, Pope AT, Freeman FG (2002) Physiological self-regulation and adaptive automation. Int J Aviat Psychol 12:179–196CrossRefGoogle Scholar
  11. 11.
    Cacioppo JT, Tassinary LG (1990) Inferring psychological significance from physiological signals. Am Psychol 45:16–28CrossRefGoogle Scholar
  12. 12.
    Cacioppo JT, Tassinary LG, Berntson GG (2007) Handbook of psychophysiology. Cambridge Univ. Press, CambridgeCrossRefGoogle Scholar
  13. 13.
    Parnandi A, Son Y, Gutierrez-Osuna R (2013) A control-theoretic approach to adaptive physiological games. In humaine association conference on affective computing and intelligent, interaction 7–12Google Scholar
  14. 14.
    Liu C, Agrawal P, Sarkar N, Chen S (2009) Dynamic difficulty adjustment in computer games through real-time anxiety-based affective feedback. Int J Hum Comput Interact 25:506–529CrossRefGoogle Scholar
  15. 15.
    Pagulayan RJ, Keeker K, Wixon D, Romero RL, Fuller T (2003) User-centered design in games. In: Sears A (ed) Jacko JA. L Erlbaum Assoc Inc, HCI handbook, pp 883–906Google Scholar
  16. 16.
    Höök K (2008) Affective Loop Experiences-What Are They? In Persuasive Technology, ed: Springer, 1–12Google Scholar
  17. 17.
    Yannakakis GN (2009) Game adaptivity impact on affective physical interaction. In International Conference on Affective Computing and Intelligent Interaction and Workshops 1–6Google Scholar
  18. 18.
    Fairclough SH (2009) Fundamentals of physiological computing. Interact Comput 21:133–145CrossRefGoogle Scholar
  19. 19.
    Pope AT, Bogart EH, Bartolome DS (1995) Biocybernetic system evaluates indices of operator engagement in automated task. Biol psychol 40:187–195CrossRefGoogle Scholar
  20. 20.
    Kuikkaniemi K, Laitinen T, Turpeinen M, Saari T, Kosunen I, Ravaja N (2010), The influence of implicit and explicit biofeedback in first-person shooter games. In SIGCHI Conference on Human Factors in, Computing Systems 859–868 Google Scholar
  21. 21.
    Boucsein W, Haarmann A, Schaefer F (2007) Combining skin conductance and heart rate variability for adaptive automation during simulated IFR flight. In Harris D (ed), Engineering psychology and cognitive ergonomics. Springer, Berlin, Heidelberg 4562:639–647Google Scholar
  22. 22.
    Boucsein W, Koglbauer I, Braunstingl R, Kallus KW (2011) The use of psychophysiological measures during complex flight manoeuvres-an expert pilot study. In Sensing emotions ed. Springer 53–63Google Scholar
  23. 23.
    Herndon CDA, Decambre M, McKenna PH (2001) Interactive computer games for treatment of pelvic floor dysfunction. J Urol 166:1893–1898CrossRefGoogle Scholar
  24. 24.
    Leahy A, Clayman C, Mason I, Lloyd G, Epstein O (1998) Computerised biofeedback games: a new method for teaching stress management and its use in irritable bowel syndrome. J R Coll Phys Lond 32:552–556Google Scholar
  25. 25. (November 24)
  26. 26.
    Sharry J, McDermott M, Condron J (2003) Relax To Win: treating children with anxiety problems with a biofeedback video game. Eisteach 2:22–26Google Scholar
  27. 27.
    Parnandi A, Ahmed B, Shipp E, Gutierrez-Osuna R (2014) Chill-out: relaxation training through respiratory biofeedback in a mobile casual game. In Mobile computing, applications, and services ed. Springer 252–260Google Scholar
  28. 28.
    Wiener N (1961) Cybernetics or control and communication in the animal and the machine. The MIT Press, CambridgeCrossRefzbMATHGoogle Scholar
  29. 29.
    Mirza-babaei P, Long S, Foley E, McAllister G (2011) Understanding the contribution of biometrics to games user research. In DIGRAGoogle Scholar
  30. 30.
    Lucas K, Sherry JL (2004) Sex differences in video game play: a communication-based explanation. Commun Res 31:499–523CrossRefGoogle Scholar
  31. 31.
    Unity (2013) Unity Car Tutorial. Available:
  32. 32.
    El-Sheikh M (2005) The role of emotional responses and physiological reactivity in the marital conflict-child functioning link. J Child Psychol Psychiatr 46:1191–1199CrossRefGoogle Scholar
  33. 33.
    Choi J, Ahmed B, Gutierrez-Osuna R (2012) Development and evaluation of an ambulatory stress monitor based on wearable sensors. IEEE Trans Inf Technol Biomed 16:279–286CrossRefGoogle Scholar
  34. 34.
  35. 35.
    ThoughtTechnology (2012) Available:
  36. 36.
    Boucsein W (2011) Electrodermal activity. Springer, BerlinGoogle Scholar
  37. 37.
    Ziegler J, Nichols N (1942) Optimum settings for automatic controllers. trans. ASME 64Google Scholar
  38. 38.
    Min B, Chung S, Park S, Kim C, Sim M, Sakamoto K (2002) Autonomic responses of young passengers contingent to the speed and driving mode of a vehicle. Int J Ind Ergon 29:187–198CrossRefGoogle Scholar
  39. 39.
    Wise K, Reeves B (2007) The effect of user control on the cognitive and emotional processing of pictures. Media Psychol 9:549–566CrossRefGoogle Scholar
  40. 40.
    Mandryk RL, Atkins MS (2007) A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. Int J Hum Comput Stud 65:329–347CrossRefGoogle Scholar
  41. 41.
    Csikszentmihalyi M (2009) Creativity: flow and the psychology of discovery. HarperCollins, New YorkGoogle Scholar

Copyright information

© OpenInterface Association 2014

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringTexas A&M UniversityCollege StationUSA

Personalised recommendations