Abstract
In this study, we tackle the integration of stressors and voice interaction in a Virtual Reality game to assess players’ arousal and stress levels. The selected game genre and its characteristic components are used as a basis to create stress-inducing elements. Additionally, a voice interaction module has been created using a voice assistant called Minerva. The module allows for real-time detection and recording of players’ emotional responses based on variations in pitch and intensity of speech. The game consists of a single level divided into four areas with increasing levels of stress. The experiment involved 16 volunteer students who played the game while their prosodic and behavioral movement data were collected. Participants also completed questionnaires and produced ratings to assess their perceived stress and arousal levels. The collected data were analyzed to evaluate the effectiveness of the real-time estimation of arousal and stress.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akçay, M.B., Oğuz, K.: Speech emotion recognition: emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. Speech Commun. 116, 56–76 (2020)
Balzarotti, S., Piccini, L., Andreoni, G., Ciceri, R.: “I know that you know how I feel’’: Behavioral and physiological signals demonstrate emotional attunement while interacting with a computer simulating emotional intelligence. J. Nonverbal Behav. 38, 283–299 (2014)
Banse, R., Scherer, K.R.: Acoustic profiles in vocal emotion expression. J. Pers. Soc. Psychol. 70(3), 614 (1996)
Bänziger, T., Scherer, K.R.: The role of intonation in emotional expressions. Speech Commun. 46(3–4), 252–267 (2005)
Barrett, L.F.: The theory of constructed emotion: an active inference account of interoception and categorization. Soc. Cogn. Affect. Neurosci. 12(1), 1–23 (2017)
Barrett, L.F., Bliss-Moreau, E.: Affect as a psychological primitive. Adv. Exp. Soc. Psychol. 41, 167–218 (2009)
Barrett, L.F., Satpute, A.B.: Historical pitfalls and new directions in the neuroscience of emotion. Neurosci. Lett. 693, 9–18 (2019)
Boccignone, G., Conte, D., Cuculo, V., Lanzarotti, R.: Amhuse: a multimodal dataset for humour sensing. In: Proceedings of the 19th ACM International Conference on Multimodal Interaction, pp. 438–445 (2017)
Boersma, P.: Praat, a system for doing phonetics by computer. Glot. Int. 5(9), 341–345 (2001)
Bone, D., Lee, C.C., Narayanan, S.: Robust unsupervised arousal rating: a rule-based framework with knowledge-inspired vocal features. IEEE Trans. Affect. Comput. 5(2), 201–213 (2014)
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)
Brambilla, S., Boccignone, G., Borghese, N., Ripamonti, L.A.: Between the buttons: stress assessment in video games using players’ behavioural data. In: Proceedings of the 6th International Conference on Computer-Human Interaction Research and Applications - CHIRA, pp. 59–69. INSTICC, SciTePress (2022). https://doi.org/10.5220/0011546400003323
Burkhart, M.C., Brandman, D.M., Franco, B., Hochberg, L.R., Harrison, M.T.: The discriminative Kalman filter for Bayesian filtering with nonlinear and nongaussian observation models. Neural Comput. 32(5), 969–1017 (2020)
Busso, C., Lee, S., Narayanan, S.: Analysis of emotionally salient aspects of fundamental frequency for emotion detection. IEEE Trans. Audio Speech Lang. Process. 17(4), 582–596 (2009)
Cohen, S., Kamarck, T., Mermelstein, R.: A global measure of perceived stress. J. Health Soc. Beh. 385–396 (1983)
Corcoran, A.W., Pezzulo, G., Hohwy, J.: From allostatic agents to counterfactual cognisers: active inference, biological regulation, and the origins of cognition. Biol. Philos. 35(3), 32 (2020)
Csikszentmihalyi, M., Csikszentmihalyi, M.: Toward a psychology of optimal experience. Flow and the foundations of positive psychology: the collected works of Mihaly Csikszentmihalyi, pp. 209–226 (2014)
D’Amelio, A., Patania, S., Buršić, S., Cuculo, V., Boccignone, G.: Inferring causal factors of core affect dynamics on social participation through the lens of the observer. Sensors 23(6), 2885 (2023)
Friston, K.: The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11(2), 127–138 (2010)
Frommel, J., Fischbach, F., Rogers, K., Weber, M.: Emotion-based dynamic difficulty adjustment using parameterized difficulty and self-reports of emotion. In: Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play, pp. 163–171 (2018)
Frommel, J., Schrader, C., Weber, M.: Towards emotion-based adaptive games: emotion recognition via input and performance features. In: Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play, pp. 173–185 (2018)
Hirst, D.J., de Looze, C.: Measuring Speech. Fundamental Frequency and Pitch., pp. 336–361. Cambridge University Press (2021)
Juslin, P.N., Scherer, K.R.: Vocal Expression of Affect. Oxford University Press, Oxford (2005)
Khalil, R.A., Jones, E., Babar, M.I., Jan, T., Zafar, M.H., Alhussain, T.: Speech emotion recognition using deep learning techniques: a review. IEEE Access 7, 117327–117345 (2019)
Kim, Y., Provost, E.M.: Emotion classification via utterance-level dynamics: a pattern-based approach to characterizing affective expressions. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3677–3681. IEEE (2013)
Lebois, L.A., Hertzog, C., Slavich, G.M., Barrett, L.F., Barsalou, L.W.: Establishing the situated features associated with perceived stress. Acta Psychol. 169, 119–132 (2016)
Linson, A., Parr, T., Friston, K.J.: Active inference, stressors, and psychological trauma: a neuroethological model of (mal) adaptive explore-exploit dynamics in ecological context. Behav. Brain Res. 380, 112421 (2020)
Nogueira, P.A., Torres, V., Rodrigues, R., Oliveira, E., Nacke, L.E.: Vanishing scares: biofeedback modulation of affective player experiences in a procedural horror game. J. Multimodal User Interfaces 10(1), 31–62 (2016)
Pallavicini, F., Ferrari, A., Pepe, A., Garcea, G., Zanacchi, A., Mantovani, F.: Effectiveness of virtual reality survival horror games for the emotional elicitation: preliminary insights using resident evil 7: biohazard. In: Antona, M., Stephanidis, C. (eds.) UAHCI 2018. LNCS, vol. 10908, pp. 87–101. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92052-8_8
Peters, A., McEwen, B.S., Friston, K.: Uncertainty and stress: why it causes diseases and how it is mastered by the brain. Prog. Neurobiol. 156, 164–188 (2017)
Quigley, K.S., Lindquist, K.A., Barrett, L.F.: Inducing and measuring emotion and affect: tips, tricks, and secrets. In: Reis, H.T., Judd, C.M. (eds.) Handbook of Research Methods in Social and Personality Psychology, pp. 220–252. Cambridge University Press, New York (2014)
Russell, J.A.: Core affect and the psychological construction of emotion. Psychol. Rev. 110(1), 145 (2003)
Schell, J.: The Art of Game Design: A book of lenses. CRC Press (2008)
Scherer, K.R.: Vocal communication of emotion: a review of research paradigms. Speech Commun. 40(1–2), 227–256 (2003)
Schulkin, J., Sterling, P.: Allostasis: a brain-centered, predictive mode of physiological regulation. Trends Neurosci. 42(10), 740–752 (2019)
Schuller, D.M., Schuller, B.W.: A review on five recent and near-future developments in computational processing of emotion in the human voice. Emot. Rev. 13(1), 44–50 (2021)
Shah Fahad, M., Ranjan, A., Yadav, J., Deepak, A.: A survey of speech emotion recognition in natural environment. Digit. Signal Proc. 110, 102951 (2021)
Singh, Y.B., Goel, S.: A systematic literature review of speech emotion recognition approaches. Neurocomputing 492, 245–263 (2022)
Solms, M., Friston, K.: How and why consciousness arises: some considerations from physics and physiology. J. Conscious. Stud. 25(5–6), 202–238 (2018)
Vachiratamporn, V., Legaspi, R., Moriyama, K., Numao, M.: Towards the design of affective survival horror games: an investigation on player affect. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, pp. 576–581 (2013)
Yang, W., Rifqi, M., Marsala, C., Pinna, A.: Physiological-based emotion detection and recognition in a video game context. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2018)
Acknowledgement
This work has been partially supported by EC H2020 ESSENCE project, Grant number 101016112.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Brambilla, S., Boccignone, G., Borghese, N.A., Chitti, E., Lombardi, R., Ripamonti, L.A. (2023). Tracing Stress and Arousal in Virtual Reality Games Using Players’ Motor and Vocal Behaviour. In: da Silva, H.P., Cipresso, P. (eds) Computer-Human Interaction Research and Applications. CHIRA 2023. Communications in Computer and Information Science, vol 1996. Springer, Cham. https://doi.org/10.1007/978-3-031-49425-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-49425-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49424-6
Online ISBN: 978-3-031-49425-3
eBook Packages: Computer ScienceComputer Science (R0)