Modelling and Control of Human Response to a Dynamic Virtual 3D Face

  • Vytautas Kaminskas
  • Edgaras ŠčiglinskasEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 869)


This chapter of the monograph introduces the application of identification and predictor-based control techniques for modelling and design control schemes of human response as reaction to a dynamic virtual 3D face. Two experiment plans were used, the first one—3D face was observed without virtual reality headset and the second one—with virtual reality headset. A human response to the stimulus (virtual 3D face with changing distance-between-eyes) is observed using EEG-based emotion signals (excitement and frustration). Experimental data is obtained when stimulus and response are observed in real time. Cross-correlation analysis of data is demonstrated that exists dynamic linear dependency between stimuli and response signals. The technique of dynamic systems identification which ensures stability and possible higher gain for building a one-step prediction models is applied. Predictor-based control schemes with a minimum variance or a generalized minimum variance controllers and constrained control signal magnitude and change speed are developed. The numerical technique of selection the weight coefficient in the generalized minimum variance control criterion is based on closed-loop stability condition and admissible value of systematic control error. High prediction accuracy and control quality are demonstrated by modelling results.


Dynamic virtual 3D face Human response EEG-based emotion signals Identification Prediction Predictor-based control with constraints Closed-loop stability Control error 


  1. Astrom KJ (1970) Introduction to stochastic control theory. Academic Press, New YorkzbMATHGoogle Scholar
  2. Astrom KJ, Wittenmark B (1984) Computer controlled systems—theory and design (3rd edition 1997). Prentice Hall, Upper Saddle RiverGoogle Scholar
  3. Clarke DW (1994) Advances in model predictive control. Oxford Science Publications, UKzbMATHGoogle Scholar
  4. Clarke DW, Mohtiedi C, Tuffs PS (1987) Generalized predictive control: parts I and II. Automatica 23:137–160Google Scholar
  5. Hatamikia S, Maghooli K, Nasrabadi AM (2014) The emotion recognition system based on autoregressive model and sequential forward feature selection of electroencephalography signals. J Med Signals Sens 4(3):194–201CrossRefGoogle Scholar
  6. Hondrou C, Caridakis G (2012) Affective, natural interaction using EEG: sensors, application and future directions. In: Artificial intelligence: theories and applications, vol 7297. Springer, Berlin, pp 331–338Google Scholar
  7. Isermann R (1981) Digital control systems. Springer, BerlinCrossRefGoogle Scholar
  8. Kaminskas V (1982, 1985) Dynamic system identification via discrete-time-observations: Part 1—Statistical method foundations. Estimation in linear systems (1982). Part 2—Estimation in nonlinear systems (1985). Vilnius, Mokslas (in Russian)Google Scholar
  9. Kaminskas V (1988) Predictor-based self-tuning control systems. In: 33 Internationales Wissenschaftliches Kolloquium, Ilmenau, 24–28, 10.1988, Heft 1, Vortragsreiche A1, Technische Kybernetik/Automatisierungstechnik, Ilmenau, Germany, Technische Hochschule Ilmenau, pp 153–156Google Scholar
  10. Kaminskas V (2007) Predictor-based self tuning control with constraints. In: Model and algorithms for global optimization, optimization and its applications, vol 4. Springer, Berlin, pp 333–341Google Scholar
  11. Kaminskas V, Ščiglinskas E (2016) Minimum variance control of human emotion as reactions to a dynamic virtual 3D face. In: AIEEE 2016: Proceedings of the 4th workshop on advances in information, electronic and electrical engineering, Lithuania, Vilnius, pp 1–6Google Scholar
  12. Kaminskas V, Ščiglinskas E (2018) Predictor-based control of human response to a dynamic 3D face using virtual reality. Informatica 29(2):251–264MathSciNetCrossRefGoogle Scholar
  13. Kaminskas V, Vidugirienė A (2016) A comparison of Hammerstein-type nonlinear models for identification of human response to virtual 3D face stimuli. Informatica 27(2):283–297CrossRefGoogle Scholar
  14. Kaminskas V, Tallat-Kelpša Č, Šidlauskas K (1987) Adaptive minimum variance control of extreme plants. In: Automation and remote control, vol 48, no 9. Consultants Bureu, New York, pp 1188–1195Google Scholar
  15. Kaminskas V, Tallat-Kelpša Č, Šidlauskas K (1989) Self-tuning minimum variance control of nonlinear Wiener-Hammerstein type systems. In: Identification and parameter estimation: selected papers from the 8th IFAC/IFORS symposium, Beijing, PRC, 27–31 Aug 1988. Pergamon Press, Oxford, pp 384–389Google Scholar
  16. Kaminskas V, Janickienė D, Vitkutė D (1990) Self-tuning control of a stochastic nonlinear object. Adaptive systems in control and signal processing: 5th IFAC symposium, Glasgow, 19–21 Apr 1989. Pergamon Press, Oxford, pp 171–175Google Scholar
  17. Kaminskas V, Janickienė D, Vitkutė D (1991a) Self-tuning control of the nuclear reactor power. In: Automatic control in the service of mankind: proceedings of the 11th world congress of the IFAC, Tallinn, Estonia, 13–17 Aug 1990, vol 11. Pergamon Press, Oxford, pp 91–96Google Scholar
  18. Kaminskas V, Šidlauskas K, Tallat-Kelpša Č (1991b) Constrained self-tuning control of stochastic extremal systems. Vilnius: Inst Math Inform, Informatica 2(1):33–51MathSciNetzbMATHGoogle Scholar
  19. Kaminskas V, Janickienė D, Vitkutė D (1992) Self-tuning constrained control of a power plant. Control of a power plants and power systems: selected papers from the IFAC symposium, Munich, Germany, 9–11 Mar. Pergamon Press, Oxford, pp 87–92Google Scholar
  20. Kaminskas V, Janickienė D, Šidlauskas K, Vitkutė D (1993) Practical issues in the implementation of predictor-based self-tuning control systems. Vilnius: Inst Math Inform, Informatica 4(1–2):3–20Google Scholar
  21. Kaminskas V, Vaškevičius E, Vidugirienė A (2014) Modeling human emotions as reactions to a dynamical virtual 3D face. Informatica 25(3):425–437CrossRefGoogle Scholar
  22. Kaminskas V, Ščiglinskas E, Vidugirienė A (2015) Predictor-based control of human emotions when reacting to a dynamic virtual 3D face stimulus. In: Proceedings of the 12th international conference on informatics in control, automation and robotics, France, Colmar, vol 1, pp 582–587Google Scholar
  23. Khushaba RN, Wise Ch, Kodagoda S, Louviere J, Kahn BE, Townsend C (2013) Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Syst Appl 40(9):3803–3812CrossRefGoogle Scholar
  24. Lin YP, Wang CH, Jung TP (2010) EEG-based emotion recognition in music listening. IEEE Trans Biomed Eng 57(7):1798–1806CrossRefGoogle Scholar
  25. Ljung LV (1987) System identification: theory for the user (2nd edition 1999). Prentice-Hall Inc., Upper Saddle RiverzbMATHGoogle Scholar
  26. Mattioli F, Caetano D, Cardoso A, Lamounier E (2015) On the agile development of virtual reality systems. In: Proceedings of the international conference on software engineering research and practice (SERP), pp 10–16Google Scholar
  27. Mikhail M, Allen J, Coan J (2013) Emotion detection using noisy EEG data. Auton Adapt Commun Syst 6(1):80–97CrossRefGoogle Scholar
  28. Peterka V (1984) Predictor-based self-tuning control. Automatica 19(5):471–486zbMATHGoogle Scholar
  29. Sari L, Nadhira V (2009) Development system for emotion detection based on brain signals and facial images. World Acad Sci Eng Technol Int J Psychol Behav Sci 3(2):13–19Google Scholar
  30. Soderstrom T, Stoica P (1989) System identification. Prentice Hall, Int., LondonGoogle Scholar
  31. Soeterboek ARM (1992) Predictive control: a unified approach. Prentice Hall International, LondonzbMATHGoogle Scholar
  32. Sourina O, Liu Y (2011) A fractal-based algorithm of emotion recognition from EEG using arousal valence model. In: Proceedings of biosignals, pp 209–214Google Scholar
  33. Vaškevičius E, Vidugirienė A, Kaminskas V (2014a) Identification of human response to virtual 3D face stimuli. Inf Technol Control 43(1):47–56Google Scholar
  34. Vaškevičius E, Vidugirienė A, Kaminskas V (2014b) Modelling excitement as a reaction to a virtual 3D face. In: Proceedings of the 11th international conference on informatics in control, automation and robotics, Viena, Austria, 1–3 Sept 2014, vol 1. Setubal, Portugal, SCITEPRESS, pp 734–740Google Scholar
  35. Vidugirienė A, Vaškevičius E, Kaminskas V (2013) Modeling of affective state response to a virtual 3D face. In: Proceedings of the 17th European modelling symposium on computer modelling and simulation, 20–22 Nov 2013, Manchester, United Kingdom. Los Alamitos, CA; Washington: IEEE Press, pp 167–172Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of InformaticsVytautas Magnus UniversityKaunasLithuania

Personalised recommendations