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Psychophysiology in Games

  • Georgios N. Yannakakis
  • Hector P. Martinez
  • Maurizio Garbarino
Chapter
Part of the Socio-Affective Computing book series (SAC, volume 4)

Abstract

Psychophysiology is the study of the relationship between psychology and its physiological manifestations. That relationship is of particular importance for both game design and ultimately gameplaying. Players’ psychophysiology offers a gateway towards a better understanding of playing behavior and experience. That knowledge can, in turn, be beneficial for the player as it allows designers to make better games for them; either explicitly by altering the game during play or implicitly during the game design process. This chapter argues for the importance of physiology for the investigation of player affect in games, reviews the current state of the art in sensor technology and outlines the key phases for the application of psychophysiology in games.

Keywords

Heart Rate Variability Affective State Skin Conductance Galvanic Skin Response Intelligent Tutoring System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The work is supported, in part, by the EU-funded FP7 ICT iLearnRW project (project no: 318803).

References

  1. 1.
    AlZoubi O, Calvo R, Stevens R (2009) Classification of EEG for affect recognition: an adaptive approach. In: AI 2009: advances in artificial intelligence. Springer, pp 52–61Google Scholar
  2. 2.
    Ambinder M (2011) Biofeedback in gameplay: how Valve measures physiology to enhance gaming experience. In: Game developers conference, San FranciscoGoogle Scholar
  3. 3.
    Andreassi JL (2000) Psychophysiology: human behavior and physiological response. Psychology PressGoogle Scholar
  4. 4.
    Arroyo I, Cooper DG, Burleson W, Woolf BP, Muldner K, Christopherson R (2009) Emotion sensors go to school. In: Proceedings of conference on artificial intelligence in education (AIED). IOS Press, pp 17–24Google Scholar
  5. 5.
    Asteriadis S, Tzouveli P, Karpouzis K, Kollias S (2009) Estimation of behavioral user state based on eye gaze and head pose–application in an e-learning environment. Multimed Tools Appl 41(3):469–493CrossRefGoogle Scholar
  6. 6.
    Asteriadis S, Karpouzis K, Shaker N, Yannakakis GN (2012) Does your profile say it all? Using demographics to predict expressive head movement during gameplay. In: UMAP workshops, citeseerGoogle Scholar
  7. 7.
    Banse R, Scherer KR (1996) Acoustic profiles in vocal emotion expression. J Personal Soc Psychol 70(3):614CrossRefGoogle Scholar
  8. 8.
    Bänziger T, Tran V, Scherer KR (2005) The Geneva emotion wheel: a tool for the verbal report of emotional reactions. Poster presented at ISREGoogle Scholar
  9. 9.
    Benedek M, Kaernbach C (2010) A continuous measure of phasic electrodermal activity. J Neurosci Methods 190(1):80–91CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Bersak D, McDarby G, Augenblick N, McDarby P, McDonnell D, McDonald B, Karkun R (2001) Intelligent biofeedback using an immersive competitive environment. Paper at the designing ubiquitous computing games workshop at UbiCompGoogle Scholar
  11. 11.
    Bianchi-Berthouze N, Lisetti CL (2002) Modeling multimodal expression of users affective subjective experience. User Model User-Adapt Interact 12(1):49–84CrossRefGoogle Scholar
  12. 12.
    Boucsein W (2012) Electrodermal activity. Springer, New YorkCrossRefGoogle Scholar
  13. 13.
    Brosschot JF, Van Dijk E, Thayer JF (2007) Daily worry is related to low heart rate variability during waking and the subsequent nocturnal sleep period. Int J Psychophysiol 63(1):39–47CrossRefPubMedGoogle Scholar
  14. 14.
    Busso C, Deng Z, Yildirim S, Bulut M, Lee CM, Kazemzadeh A, Lee S, Neumann U, Narayanan S (2004) Analysis of emotion recognition using facial expressions, speech and multimodal information. In: Proceedings of international conference on multimodal interfaces (ICMI). ACM, pp 205–211Google Scholar
  15. 15.
    Cacioppo JT, Berntson GG, Larsen JT, Poehlmann KM, Ito TA et al (2000) The psychophysiology of emotion. In: Lewis M, Haviland-Jones JM (eds) Handbook of emotions, vol 2. Guilford Press, New York, pp 173–191Google Scholar
  16. 16.
    Cacioppo JT, Tassinary LG, Berntson G (2007) Handbook of psychophysiology. Cambridge University Press, Cambridge/New YorkCrossRefGoogle Scholar
  17. 17.
    Calvo RA, D’Mello S (2010) Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Trans Affect Comput 1(1):18–37CrossRefGoogle Scholar
  18. 18.
    Calvo RA, D’Mello SK (2011) New perspectives on affect and learning technologies, vol 3. Springer, New YorkCrossRefGoogle Scholar
  19. 19.
    Calvo R, Brown I, Scheding S (2009) Effect of experimental factors on the recognition of affective mental states through physiological measures. In: AI 2009: advances in artificial intelligence. Springer, pp 62–70Google Scholar
  20. 20.
    Conati C, Maclaren H (2009) Modeling user affect from causes and effects. In: User modeling, adaptation, and personalization, Trento, pp 4–15Google Scholar
  21. 21.
    Cowie R, Cornelius RR (2003) Describing the emotional states that are expressed in speech. Speech Commun 40(1):5–32CrossRefGoogle Scholar
  22. 22.
    Cowie R, Sawey M (2011) Gtrace-general trace program from queens, belfastGoogle Scholar
  23. 23.
    Cowie R, Douglas-Cowie E, Savvidou S, McMahon E, Sawey M, Schröder M (2000) ’FEELTRACE’: an instrument for recording perceived emotion in real time. In: ISCA tutorial and research workshop (ITRW) on speech and emotionGoogle Scholar
  24. 24.
    Critchley HD, Mathias CJ, Dolan RJ (2002) Fear conditioning in humans: the influence of awareness and autonomic arousal on functional neuroanatomy. Neuron 33(4):653–663CrossRefPubMedGoogle Scholar
  25. 25.
    De Melo C, Paiva A (2007) Expression of emotions in virtual humans using lights, shadows, composition and filters. In: Affective computing and intelligent interaction. Springer, Berlin/New York, pp 546–557CrossRefGoogle Scholar
  26. 26.
    Dennerlein J, Becker T, Johnson P, Reynolds C, Picard RW (2003) Frustrating computer users increases exposure to physical factors. In: Proceedings of the international ergonomics association (IEA), SeoulGoogle Scholar
  27. 27.
    Devillers L, Vidrascu L (2006) Real-life emotions detection with lexical and paralinguistic cues on human-human call center dialogs. In: Proceedings of conference of the international speech communication association (Interspeech), Pittsburgh, pp 801–804Google Scholar
  28. 28.
    D’Mello S, Graesser A (2009) Automatic detection of learner’s affect from gross body language. Appl Artif Intell 23(2):123–150CrossRefGoogle Scholar
  29. 29.
    Drachen A, Nacke L, Yannakakis GN, Pedersen AL (2010) Correlation between heart rate, electrodermal activity and player experience in first-person shooter games. In: Proceedings of the SIGGRAPH symposium on video games. ACM-SIGGRAPH Publishers, New YorkCrossRefGoogle Scholar
  30. 30.
    Eladhari M, Nieuwdorp R, Fridenfalk M (2006) The soundtrack of your mind: mind music-adaptive audio for game characters. In: Proceedings of the 2006 ACM SIGCHI international conference on advances in computer entertainment technology. ACM, p 54Google Scholar
  31. 31.
    El-Nasr MS, Vasilakos A, Rao C, Zupko J (2009) Dynamic intelligent lighting for directing visual attention in interactive 3-D scenes. IEEE Trans Comput Intell AI Games 1(2):145–153CrossRefGoogle Scholar
  32. 32.
    Farrugia VE, Martínez HP, Yannakakis GN (2015) The preference learning toolbox. arXiv preprint arXiv:1506.01709Google Scholar
  33. 33.
    Fürnkranz J, Hüllermeier E (2005) Preference learning. Künstliche Intelligenz 19(1):60–61Google Scholar
  34. 34.
    Giannatos S, Nelson MJ, Cheong Y-G, Yannakakis GN (2011) Suggesting new plot elements for an interactive story. In: Intelligent narrative technologiesGoogle Scholar
  35. 35.
    Goldberger JJ, Challapalli S, Tung R, Parker MA, Kadish AH (2001) Relationship of heart rate variability to parasympathetic effect. Circulation 103(15):1977–1983CrossRefPubMedGoogle Scholar
  36. 36.
    Grafsgaard J, Boyer K, Lester J (2011) Predicting facial indicators of confusion with hidden Markov models. In: Proceedings of international conference on affective computing and intelligent interaction (ACII). Springer, Memphis, pp 97–106CrossRefGoogle Scholar
  37. 37.
    Haykin S, Widrow B (2003) Least-mean-square adaptive filters, vol 31. Wiley, HobokenCrossRefGoogle Scholar
  38. 38.
    Hazlett RL (2006) Measuring emotional valence during interactive experiences: boys at video game play. In: Proceedings of SIGCHI conference on human factors in computing systems (CHI). ACM, New York, pp 1023–1026CrossRefGoogle Scholar
  39. 39.
    Holmgård C, Yannakakis GN, Karstoft K-I, Andersen HS (2013) Stress detection for PTSD via the startlemart game. In: 2013 humaine association conference on affective computing and intelligent interaction (ACII). IEEE, Piscataway, pp 523–528CrossRefGoogle Scholar
  40. 40.
    Holmgård C, Yannakakis GN, Martínez HP, Karstoft K-I (2015) To rank or to classify? Annotating stress for reliable PTSD profiling. In: 2015 international conference on affective computing and intelligent interaction (ACII), Xi’anGoogle Scholar
  41. 41.
    Holmgård C, Yannakakis GN, Martínez HP, Karstoft K-I, Andersen HS (2015) Multimodal PTSD characterization via the startlemart game. J Multimodal User Interfaces 9(1):3–15CrossRefGoogle Scholar
  42. 42.
    Hussain M, AlZoubi O, Calvo R, D’Mello S (2011) Affect detection from multichannel physiology during learning sessions with autotutor. In: Proceedings of international conference in artificial intelligence in education (AIED). Springer, Heidelberg, pp 131–138CrossRefGoogle Scholar
  43. 43.
    Johnstone T, Scherer KR (2000) Vocal communication of emotion. In: Lewis M, Haviland-Jones JM (eds) Handbook of emotions, vol 2. Guilford Press, New York, pp 220–235Google Scholar
  44. 44.
    Jönsson P (2007) Respiratory sinus arrhythmia as a function of state anxiety in healthy individuals. Int J Psychophysiol 63(1):48–54CrossRefPubMedGoogle Scholar
  45. 45.
    Juslin PN, Scherer KR (2005) Vocal expression of affect. Oxford University Press, OxfordGoogle Scholar
  46. 46.
    Kaliouby R, Picard R, Baron-Cohen S (2006) Affective computing and autism. Ann N Y Acad Sci 1093(1):228–248CrossRefPubMedGoogle Scholar
  47. 47.
    Kannetis T, Potamianos A (2009) Towards adapting fantasy, curiosity and challenge in multimodal dialogue systems for preschoolers. In: Proceedings of international conference on multimodal interfaces (ICMI). ACM, New York, pp 39–46Google Scholar
  48. 48.
    Kannetis T, Potamianos A, Yannakakis GN (2009) Fantasy, curiosity and challenge as adaptation indicators in multimodal dialogue systems for preschoolers. In: Proceedings of the 2nd workshop on child, computer and interaction. ACM, New York, p 1Google Scholar
  49. 49.
    Kapoor A, Burleson W, Picard RW (2007) Automatic prediction of frustration. Int J Hum-Comput Stud 65(8):724–736CrossRefGoogle Scholar
  50. 50.
    Kivikangas JM, Ekman I, Chanel G, Järvelä S, Salminen M, Cowley B, Henttonen P, Ravaja N (2010) Review on psychophysiological methods in game research. In: Procedings of Nordic digital games research association conference (Nordic DiGRA)Google Scholar
  51. 51.
    Leite I, Mascarenhas S, Pereira A, Martinho C, Prada R, Paiva A (2010) “Why can’t we be friends?” An empathic game companion for long-term interaction. In: Intelligent virtual agents. Springer, Berlin, pp 315–321CrossRefGoogle Scholar
  52. 52.
    Lisetti CL, Nasoz F (2004) Using noninvasive wearable computers to recognize human emotions from physiological signals. EURASIP J Appl Signal Process 2004:1672–1687CrossRefGoogle Scholar
  53. 53.
    Lisetti C, Nasoz F, LeRouge C, Ozyer O, Alvarez K (2003) Developing multimodal intelligent affective interfaces for tele-home health care. Int J Hum-Comput Stud 59(1):245–255CrossRefGoogle Scholar
  54. 54.
    Liu C, Conn K, Sarkar N, Stone W (2008) Physiology-based affect recognition for computer-assisted intervention of children with autism spectrum disorder. Int J Hum-Comput Stud 66(9):662–677CrossRefGoogle Scholar
  55. 55.
    Lopes P, Liapis A, Yannakakis GN (2015) Sonancia: sonification of procedurally generated game levels. In: Proceedings of the 1st computational creativity and games workshopGoogle Scholar
  56. 56.
    Lopes P, Liapis A, Yannakakis GN (2015) Targeting horror via level and soundscape generationGoogle Scholar
  57. 57.
    Malandrakis N, Potamianos A, Evangelopoulos G, Zlatintsi A (2011) A supervised approach to movie emotion tracking. In: 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, Piscataway, pp 2376–2379CrossRefGoogle Scholar
  58. 58.
    Mandryk RL, Atkins MS (2007) A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. Int J Hum-Comput Stud 65(4):329–347CrossRefGoogle Scholar
  59. 59.
    Mandryk RL, Inkpen KM, Calvert TW (2006) Using psychophysiological techniques to measure user experience with entertainment technologies. Behav Inf Technol 25(2):141–158CrossRefGoogle Scholar
  60. 60.
    Martínez HP, Yannakakis GN (2010) Genetic search feature selection for affective modeling: a case study on reported preferences. In: Proceedings of the 3rd international workshop on affective interaction in natural environments. ACM, pp 15–20Google Scholar
  61. 61.
    Martínez HP, Yannakakis GN (2011) Mining multimodal sequential patterns: a case study on affect detection. In: Proceedings of the 13th international conference on multimodal interfaces. ACM, pp 3–10Google Scholar
  62. 62.
    Martínez HP, Bengio Y, Yannakakis GN (2013) Learning deep physiological models of affect. IEEE Comput Intell Mag 9(1):20–33CrossRefGoogle Scholar
  63. 63.
    Martinez H, Yannakakis G, Hallam J (2014) Don’t classify ratings of affect; rank them!. IEEE Trans Affect Comput 5(3):314–326CrossRefGoogle Scholar
  64. 64.
    Mateas M, Stern A (2003) Façade: an experiment in building a fully-realized interactive drama. In: Game developers conference, vol 2Google Scholar
  65. 65.
    McQuiggan S, Lee S, Lester J (2007) Early prediction of student frustration. In: Proceedings of international conference on affective computing and intelligent interaction. Springer, pp 698–709Google Scholar
  66. 66.
    Mcquiggan SW, Mott BW, Lester JC (2008) Modeling self-efficacy in intelligent tutoring systems: an inductive approach. User Model User-Adapt Interact 18(1):81–123CrossRefGoogle Scholar
  67. 67.
    Messinger DS, Cassel TD, Acosta SI, Ambadar Z, Cohn JF (2008) Infant smiling dynamics and perceived positive emotion. J Nonverbal Behav 32(3):133–155CrossRefPubMedPubMedCentralGoogle Scholar
  68. 68.
    Nacke L, Lindley CA (2008) Flow and immersion in first-person shooters: measuring the player’s gameplay experience. In: Proceedings of conference on future play: research, play, share. ACM, pp 81–88Google Scholar
  69. 69.
    Nagel F, Kopiez R, Grewe O, Altenmüller E (2007) Emujoy: software for continuous measurement of perceived emotions in music. Behav Res Methods 39(2):283–290CrossRefPubMedGoogle Scholar
  70. 70.
    Nijholt A (2009) BCI for games: a ‘state of the art’ survey. In: Entertainment computing-ICEC 2008. Springer, pp 225–228Google Scholar
  71. 71.
    Pedersen C, Togelius J, Yannakakis GN (2010) Modeling player experience for content creation. IEEE Trans Comput Intell AI Games 2(1):54–67CrossRefGoogle Scholar
  72. 72.
    Picard RW (2009) Future affective technology for autism and emotion communication. Philos Trans R Soc B: Biol Sci 364(1535):3575–3584CrossRefGoogle Scholar
  73. 73.
    Picard RW, Vyzas E, Healey J (2001) Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans Pattern Anal Mach Intell 23(10):1175–1191CrossRefGoogle Scholar
  74. 74.
    Picard RW, Papert S, Bender W, Blumberg B, Breazeal C, Cavallo D, Machover T, Resnick M, Roy D, Strohecker C (2004) Affective learning – a manifesto. BT Technol J 22(4):253–269CrossRefGoogle Scholar
  75. 75.
    Poels K, de Kort Y, Ijsselsteijn W (2007) It is always a lot of fun!: exploring dimensions of digital game experience using focus group methodology. In: Proceedings of the 2007 conference on future play. ACM, pp 83–89Google Scholar
  76. 76.
    Poh M-Z, McDuff DJ, Picard RW (2010) Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt Express 18(10):10762–10774CrossRefPubMedGoogle Scholar
  77. 77.
    Qu L, Wang N, Johnson W (2005) Using learner focus of attention to detect learner motivation factors. In: Proceedings of international conference on user modeling (UM). Springer, pp 149–149Google Scholar
  78. 78.
    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, pp 184–192Google Scholar
  79. 79.
    Ravaja N, Saari T, Laarni J, Kallinen K, Salminen M, Holopainen J, Jarvinen A (2005) The psychophysiology of video gaming: phasic emotional responses to game events. In: Proceedings of digital games research association conference (DiGRA)Google Scholar
  80. 80.
    Ravaja N, Saari T, Salminen M, Laarni J, Kallinen K (2006) Phasic emotional reactions to video game events: a psychophysiological investigation. Media Psychol 8(4):343–367CrossRefGoogle Scholar
  81. 81.
    Rebolledo-Mendez G, Dunwell I, Martínez-Mirón E, Vargas-Cerdán M, De Freitas S, Liarokapis F, García-Gaona A (2009) Assessing neurosky’s usability to detect attention levels in an assessment exercise. In: Human-computer interaction. New trends, pp 149–158Google Scholar
  82. 82.
    Riedl M, Bulitko V (2012) Interactive narrative: a novel application of artificial intelligence for computer games. AAAI, CiteseerGoogle Scholar
  83. 83.
    Robison J, McQuiggan S, Lester J (2009) Evaluating the consequences of affective feedback in intelligent tutoring systems. In: Proceedings of international conference on affective computing and intelligent interaction (ACII). IEEE, pp 1–6Google Scholar
  84. 84.
    Russell JA (1980) A circumplex model of affect. J Personal Soc Psychol 39 (6):1161CrossRefGoogle Scholar
  85. 85.
    Schwarz N (2000) Emotion, cognition, and decision making. Cogn Emot 14(4):433–440CrossRefGoogle Scholar
  86. 86.
    Shaker N, Yannakakis GN, Togelius J (2010) Towards automatic personalized content generation for platform games. In: Proceedings of the AAAI conference on artificial intelligence and interactive digital entertainment (AIIDE). AAAI PressGoogle Scholar
  87. 87.
    Shaker N, Asteriadis S, Yannakakis GN, Karpouzis K (2013) Fusing visual and behavioral cues for modeling user experience in games. IEEE Trans Cybern 43(6):1519–1531CrossRefPubMedGoogle Scholar
  88. 88.
    Sharma N, Gedeon T (2012) Objective measures, sensors and computational techniques for stress recognition and classification: a survey. Comput Methods Programs Biomed 108(3):1287–1301CrossRefPubMedGoogle Scholar
  89. 89.
    Sokolov EN (1963) Higher nervous functions: the orienting reflex. Annu Rev Physiol 25(1):545–580CrossRefPubMedGoogle Scholar
  90. 90.
    Tijs T, Brokken D, Ijsselsteijn W (2008) Dynamic game balancing by recognizing affect. In: Proceedings of international conference on fun and games. Springer, pp 88–93Google Scholar
  91. 91.
    Togelius J, Schmidhuber J (2008) An experiment in automatic game design. In: IEEE symposium on computational intelligence and games, CIG’08. IEEE, pp 111–118Google Scholar
  92. 92.
    Togelius J, De Nardi R, Lucas SM (2007) Towards automatic personalised content creation for racing games. In: IEEE symposium on computational intelligence and games, CIG 2007. IEEE, pp 252–259Google Scholar
  93. 93.
    Togelius J, Preuss M, Beume N, Wessing S, Hagelback J, Yannakakis GN (2010) Multiobjective exploration of the starcraft map space. In: 2010 IEEE symposium on computational intelligence and games (CIG). IEEE, pp 265–272Google Scholar
  94. 94.
    Togelius J, Yannakakis GN, Stanley KO, Browne C (2011) Search-based procedural content generation: a taxonomy and survey. IEEE Trans Comput Intell AI Games 3(3):172–186CrossRefGoogle Scholar
  95. 95.
    Tognetti S, Garbarino M, Bonarini A, Matteucci M (2010) Modeling enjoyment preference from physiological responses in a car racing game. In: Proceedings of IEEE conference on computational intelligence and games (CIG). IEEE, pp 321–328Google Scholar
  96. 96.
    van den Hoogen WM, IJsselsteijn WA, de Kort YAW (2008) Exploring behavioral expressions of player experience in digital games. In: Proceedings of the workshop on facial and bodily expression for control and adaptation of games (ECAG), pp 11–19Google Scholar
  97. 97.
    Vogt T, André E (2005) Comparing feature sets for acted and spontaneous speech in view of automatic emotion recognition. In: Proceedings of IEEE international conference on multimedia and expo (ICME). IEEE, pp 474–477Google Scholar
  98. 98.
    Yang Y-H, Chen HH (2011) Ranking-based emotion recognition for music organization and retrieval. IEEE Trans Audio Speech Lang Process 19(4):762–774CrossRefGoogle Scholar
  99. 99.
    Yannakakis GN (2009) Preference learning for affective modeling. In: 3rd international conference on affective computing and intelligent interaction and workshops, ACII 2009, Amsterdam, Sept 2009. IEEE, pp 1–6Google Scholar
  100. 100.
    Yannakakis GN, Hallam J (2007) Towards optimizing entertainment in computer games. Appl Artif Intell 21(10):933–971CrossRefGoogle Scholar
  101. 101.
    Yannakakis GN, Hallam J (2008) Entertainment modeling through physiology in physical play. Int J Hum-Comput Stud 66(10):741–755CrossRefGoogle Scholar
  102. 102.
    Yannakakis GN, Hallam J (2009) Real-time game adaptation for optimizing player satisfaction. IEEE Trans Comput Intell AI Games 1(2):121–133CrossRefGoogle Scholar
  103. 103.
    Yannakakis G, Hallam J (2011) Rating vs. preference: a comparative study of self-reporting. In: Proceedings of international conference on affective computing and intelligent interaction (ACII). Springer, pp 437–446Google Scholar
  104. 104.
    Yannakakis GN, Martínez HP (2015) Grounding truth via ordinal annotation. In: 2015 international conference on affective computing and intelligent interaction (ACII)Google Scholar
  105. 105.
    Yannakakis GN, Martínez HP (2015) Ratings are overrated! Front ICT 2:13CrossRefGoogle Scholar
  106. 106.
    Yannakakis GN, Paiva A (2013) Emotion in games. In: Handbook on affective computing, p 20Google Scholar
  107. 107.
    Yannakakis GN, Togelius J (2011) Experience-driven procedural content generation. IEEE Trans Affect Comput 2(3):147–161CrossRefGoogle Scholar
  108. 108.
    Yannakakis GN, Hallam J, Lund HH (2008) Entertainment capture through heart rate activity in physical interactive playgrounds. User Model User-Adapt Interact 18(1):207–243CrossRefGoogle Scholar
  109. 109.
    Yannakakis GN, Martínez HP, Jhala A (2010) Towards affective camera control in games. User Model User-Adapt Interact 20(4):313–340CrossRefGoogle Scholar
  110. 110.
    Yildirim S, Narayanan S, Potamianos A (2011) Detecting emotional state of a child in a conversational computer game. Comput Speech Lang 25(1):29–44CrossRefGoogle Scholar
  111. 111.
    Zeng Z, Pantic M, Roisman G, Huang TS et al (2009) A survey of affect recognition methods: audio, visual, and spontaneous expressions. IEEE Trans Pattern Anal Mach Intell 31(1):39–58CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Georgios N. Yannakakis
    • 1
  • Hector P. Martinez
    • 1
  • Maurizio Garbarino
    • 2
  1. 1.Institute of Digital GamesUniversity of MaltaMsidaMalta
  2. 2.EmpaticaMilanItaly

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