Advertisement

Beyond Anthropometry and Biomechanics: Digital Human Models for Modeling Realistic Behaviors of Virtual Humans

  • Thomas AlexanderEmail author
  • Lisa Fromm
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 780)

Abstract

Spatial layout of workplaces and geometric analysis of future products is a prominent application of Digital Human Models (DHMs). DHMs describe characteristics of body dimensions, body shape and motions of the future workers and users. Further applications of DHM are animated, computer-generated and photo-realistic figures for populating computer games and movies. In contrast to these applications, other areas of human modeling, e.g. human behavior modeling or cognitive modeling, have not been applied broadly.

These models address different levels of human behavior, including human information processing. This paper presents several of these models and uses them as a basis for generating the idea of a comprehensive digital human model. Such a model is applicable for optimizing complex work processes as well as populating virtual environments. It is concluded that there will be no single solution for modeling and simulation all variations and aspects of human behavior, but that there is a need for a reference architecture as a generic interface between the different models.

Keywords

Digital human modeling Modeling Simulation Human behavior 

References

  1. 1.
    Reilly, E.D.: Simulation. In: Reilly, E.D. (ed.) Concise Encyclopedia of Computer, pp. 690–695. Whiley, West Sussex (2004)Google Scholar
  2. 2.
    Alexander, T.: Methoden der anthropometrischen Cockpitgestaltung. In: Gärtner (ed.) Anthropometrische Cockpitgestaltung. DGLR, Bonn (1995)Google Scholar
  3. 3.
    Chaffin, D.B.: Human motion simulation for vehicle and workplace design. Hum. Factors Ergon. Manuf. 17(5), 475–484 (2007)CrossRefGoogle Scholar
  4. 4.
    Muehlstedt, J., Kaussler, H., Spanner-Ulmer, B.: The software incarnate: digital human models for CAx and PLM systems. Zeitschrift fuer Arbeitswissenschaft 62(2), 79–86 (2008)Google Scholar
  5. 5.
    Human Solutions: RAMSIS NextGen 1.1, Ergonomics User Guide. Kaiserslautern (2015)Google Scholar
  6. 6.
  7. 7.
  8. 8.
    Andersson, A., Nordgren, B., Hall, J.: Measurement of movements during highly repetitive industrial work. Appl. Ergon. 27(5), 343–344 (1996)CrossRefGoogle Scholar
  9. 9.
    Cerveri, P., Pedotti, A., Ferrigno, G.: Evolutionary optimization for robust hierarchical computation of the rotation centres of kinematic chains from reduced ranges of motion the lower spine case. J. Biomech. 37(12), 1881–1890 (2004)CrossRefGoogle Scholar
  10. 10.
    Rohmert, W., Laurig, W., Philipp, U., Luczak, H.: Heart rate variability and work load measurement. Ergonomics 16(1), 33–44 (1973)CrossRefGoogle Scholar
  11. 11.
    Winter, D.A. (ed.): Biomechanics of Human Movement. Wiley, New York, Chichester, Brisbane, Toronto (1997)Google Scholar
  12. 12.
    Mori, M., MacDorman, K.F., Kageki, N.: The uncanny valley. IEEE Rob. Autom. Mag. 19, 98–100 (2012)CrossRefGoogle Scholar
  13. 13.
    Wickens, C.D.: Multiple resources and performance prediction. Theor. Issues Ergon. Sci. 3(2), 159–177 (2002)CrossRefGoogle Scholar
  14. 14.
    Swain, J.J.: Simulation software survey. OR/MS Today, vol. 38, no. 5. Informs, Baltimore (2011)Google Scholar
  15. 15.
    Archer, S., Headley, D., Allender, L.: Manpower, personnel, and training integration methods and tools. In: Booher, H.R. (Hg.) Handbook of Human Systems Integration. Wiley-Interscience (Wiley Series in Systems Engineering and Management), Hoboken (2003)Google Scholar
  16. 16.
    Gore, B.F.: Man-machine integration design and analysis system (MIDAS) v5: augmentations, motivations, and directions for aeronautics applications. Technical report, NASA Ames Research Center, Moffett Fileds, CA (2011)Google Scholar
  17. 17.
    Leidholdt, W.: Der “Editor menschlicher Arbeit - EMA” - ein Planungsinstrument für manuelle Arbeit: 2. Symposium Produktionstechnik innovativ und interdisziplinär - im Fokus des Automobil- und Maschinenbaus. Zwickau (2007)Google Scholar
  18. 18.
    Leschner, K.: Benutzerhandbuch ema V5: Version 1.4.1.0: imk automotive GmbH (2014)Google Scholar
  19. 19.
    Chomsky, N.: Syntactic Structures. De Gruyter, Berlin (2002)CrossRefGoogle Scholar
  20. 20.
    Johnson-Laird, P.N.: Mental models in cognitive science. Cogn. Sci. 4(1), 71–115 (1980)CrossRefGoogle Scholar
  21. 21.
    Samuelson, W., Zeckhauser, R.: Status quo bias in decision making. J. Risk Uncertain. 1(1), 7–59 (2004)CrossRefGoogle Scholar
  22. 22.
    Anderson, J.R., Matessa, M., Lebiere, C.: ACT-R: a theory of higher level cognition and its relation to visual attention. Hum.-Comput. Interact. 12, 439–462 (1997)CrossRefGoogle Scholar
  23. 23.
  24. 24.
    Ellis, S.R.: Preface, Conference Proceedings of the Symposium on Intelligent Motion and Interaction in Virtual Environments. NASA CP, Moffett Fields (2005)Google Scholar
  25. 25.
    Gunzelmann, G., Gaughan, C., Huiskamp, W., van den Bosch, K., de Jong, S., Alexander, T., Bruzzone, A.G., Tremori, A.: In search of interoperability standards for human behaviour representation. In: Proceedings of the I/ITSEC, Orlando, FL, 1–4 December 2014. National Training and Simulation Association, Arlington (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Human FactorsFraunhofer-FKIEBonnGermany

Personalised recommendations