Skip to main content
Springer Nature Link
Log in

Artificial Neural Networks for Motion Emulation in Virtual Environments

  • Conference paper
  • First Online:
Modelling and Motion Capture Techniques for Virtual Environments (CAPTECH 1998)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1537))

Included in the following conference series:

  • 797 Accesses

Abstract

Simulation of natural human movement has proven to be a challenging problem, difficult to be solved by more or less traditional bio-inspired strategies. In opposition to several existing solutions, mainly based upon deterministic algorithms, a data-driven approach is presented herewith, which is able to grasp not only the natural essence of human movements, but also their intrinsic variability, the latter being a necessary feature for many ergonomic applications. For these purposes a recurrent Artificial Neural Network with some novel features (recurrent RPROP, state neurons, weighted cost function) has been adopted and combined with an original pre-processing step on experimental data, resulting in a new hybrid approach for data aggregation. Encouraging results on human hand reaching movements are also presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Y. Bellan, M. Costa, L. Macchiarulo, E. Pasero, “Task-Oriented Estimation Of Trajectories in Human Reaches”, Proc. of the NN&B, Beijing, CN, 1998.

    Google Scholar 

  2. S. Brandt, Statistical and Computational Methods in Data Analysis, North-Holland Publishing Company, Amsterdam, pp. 191–239, 1976.

    Google Scholar 

  3. M. Costa, F. Lombardi, A. Montuori, “A data driven methodology for the simulation of human movement in CAD environments”, Proc. of the CIRP International Seminar on Intelligent Computation in Manufacturing Engineering-ICME 98, Capri (Naples), IT, 1998.

    Google Scholar 

  4. H. Cruse, “On the cost functions for the control of the human arm movement”, Biol. Cybernetics, 62, pp. 519–528, 1990.

    Article  Google Scholar 

  5. G. Ferrigno, A. Pedotti, “ELITE: a digital dedicated hardware system for movement analysis via real-time TV signal processing”, IEEE Trans. Biomed. Eng. (BME), 32, pp. 943–949, 1985.

    Article  Google Scholar 

  6. G. Ferrigno, A. Pedotti, “Modularly expansible system for real-time processing of a TV display, useful in particular for the acquisition of coordinates of known shapes objects”, US Patent No. 4,706,296 (1987).

    Google Scholar 

  7. G. Ferrigno, N. A. Borgese, A. Pedotti, “Pattern recognition in 3D automatic human motion analysis”, ISPRS Journal of Photogrammetry and Remote Sensing, 45, pp. 227–246, 1990.

    Article  Google Scholar 

  8. J. C. Fiala, “A network for learning kinematics with application to human reaching models”, Proc. of the ICNN, vol. 5, pp. 2759–2764, Orlando, FL, 1994.

    Google Scholar 

  9. T. Flash, N. Hogan, “The co-ordination of the arm movements: an experimentally confirmed mathematical model”, Journal of neuroscience, 7, pp. 1688–1703, 1985.

    Google Scholar 

  10. J. M. Hollerbach, T. Flash, “Dynamic interactions between limb segments during planar movements”, Biol. Cybernetics, 44, pp. 66–77, 1982.

    Article  Google Scholar 

  11. D.W. Marquardt, Journal of the Society for Industrial and Applied Mathematics, vol. 11, 1963.

    Google Scholar 

  12. A. Pedotti, G. Ferrigno, “Opto-electronic based system”, in Three dimensional analysis of human movement, P. Allard, I.A.F. Stokes and J.P. Bianchi, pp. 57–77, Human Kinetics Publishers, 1995.

    Google Scholar 

  13. W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P Flannery, Numerical Recipes in C: The Art of Scientific Computing, Cambridge University Press, pp. 681–688, 1988.

    Google Scholar 

  14. M. Riedmiller, H. Braun, “A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm”, Proc. of the ICNN, San Francisco, CA, 1993.

    Google Scholar 

  15. Y. Uno, M. Kawato, R. Suzuki, “Formation and control of optimal trajectory in human arm movement”, Biol. Cybernetics, 61, pp. 89–101, 1989.

    Article  Google Scholar 

  16. P. J. Werbos, “Backpropagation Through Time: What It Does and How to Do It”, Proc. of the IEEE, vol. 78, n. 10, 1990.

    Google Scholar 

  17. Wolf, “Elements of photogrammetry”, McGraw-Hill, New York, 1974.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Electronics, Politecnico di Torino, Italy

    Y. Bellan, M. Costa, L. Macchiarulo & E. Pasero

  2. Dept. of Bio-Engineering, Politecnico di Milano, Italy

    G. Ferrigno & C. Rigotti

  3. Dept. of Manufacturing Systems and Economics, Politecnico di Torino, Italy

    F. Lombardi & A. Montuori

Authors
  1. Y. Bellan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Costa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. G. Ferrigno
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. F. Lombardi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. L. Macchiarulo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. A. Montuori
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. E. Pasero
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. C. Rigotti
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Information Systems, University of Geneva, 24 Rue du Général Dufour, CH-1211, Geneva 4, Switzerland

    Nadia Magnenat-Thalmann

  2. Swiss Federal Institute of Technology (EPFL), CH-1015, Lausanne, Switzerland

    Daniel Thalmann

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg1998

About this paper

Cite this paper

Bellan, Y. et al. (1998). Artificial Neural Networks for Motion Emulation in Virtual Environments. In: Magnenat-Thalmann, N., Thalmann, D. (eds) Modelling and Motion Capture Techniques for Virtual Environments. CAPTECH 1998. Lecture Notes in Computer Science(), vol 1537. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49384-0_7

Download citation

  • DOI: https://doi.org/10.1007/3-540-49384-0_7

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65353-0

  • Online ISBN: 978-3-540-49384-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics