Multiple Assessment for Multiple Users in Virtual Reality Training Environments

  • Ronei M. Moraes
  • Liliane S. Machado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)


With recent computational advances, several interaction devices can be used by different users who share the same virtual world, allowing the simulation of realistic environments, such as surgical rooms. In order to deal with this feature, assessment systems must be generalized to evaluate, individually, all users of the simulation and to make the aspects of their interactions known. In this paper we propose a new assessment system for training based on virtual reality which can evaluate more than one user at a time. The methodology proposed uses data collected from user interaction and group interactions during training to create user profile and group profile. The main advantages of that approach are: both of reports can be used to increase group performance and the interactions among users, during training, can be monitored to correct and improve group tasks in procedure such as sequential, simultaneous or collaborative tasks.


Multiple Assessment System Training Based on Virtual Reality Fuzzy Expert System Statistical Measures Statistical Models 


  1. 1.
    Alcañiz, M., et al.: GeRTiSS: Generic Real Time Surgery Simulation. Studies in Health Technology and Informatics 94, 16–18 (2003)Google Scholar
  2. 2.
    Baier, H.: Distributed PC-based haptic, visual and acoustic telepresence system - experiments in virtual and remote environments. In: Proc. of IEEE VR, USA, pp. 118–125 (1999)Google Scholar
  3. 3.
    Blanchard, C., et al.: Reality built for two: a virtual reality tool. ACM CG 24(2), 35–36 (1992)Google Scholar
  4. 4.
    Burdea, G., Coiffet, P.: Virtual Reality Technology, 2nd edn. Wiley, Chichester (2003)Google Scholar
  5. 5.
    Carlsson, C., Hagsand, O.: DIVE: a platform for multi-user virtual environments. Computers & Graphics 17(6), 663–669 (1993)CrossRefGoogle Scholar
  6. 6.
    Gande, A., Devarajan, V.: Instructor station for virtual laparoscopic surgery: requirements and design. In: Proc. of Computer Graphics and Imaging, USA, pp. 85–90 (2003)Google Scholar
  7. 7.
    Low, K-L., et al.: Combining head-mounted and projector-based displays for surgical training. In: Proc. of the IEEE VR, USA, pp. 110–117 (2003)Google Scholar
  8. 8.
    Machado, L.S., et al.: Fuzzy Rule-Based Evaluation for a Haptic and Stereo Simulator for Bone Marrow Harvest for Transplant. In: 5th PUG Workshop Proc. USA (2000)Google Scholar
  9. 9.
    Machado, L.S., et al.: A Virtual Reality Simulator for Bone Marrow Harvest for Pediatric Transplant. Studies in Health Technology and Informatics 81, 293–297 (2001)Google Scholar
  10. 10.
    Machado, L.S., Moraes, R.M.: Online Training Evaluation in Virtual Reality Simulators Using Evolving Fuzzy Neural Networks. In: Proc. 6th FLINS Conference. Belgium, pp. 314–317 (2004)Google Scholar
  11. 11.
    Machado, L.S., Valdek, M.C.O., Moraes, R.M.: Assessement of Gynecological Procedures in a Simulator Based on Virtual Reality. In: Proc. of the 7th FLINS Conference. Italy, pp. 799–804 (2006)Google Scholar
  12. 12.
    Moraes, R.M., Machado, L.S.: Using Fuzzy Hidden Markov Models for Online Training Evaluation and Classification in Virtual Reality Simulators. International Journal of General Systems 33(2-3), 281–288 (2004)zbMATHCrossRefGoogle Scholar
  13. 13.
    Moraes, R.M., Machado, L.S.: Fuzzy Gaussian Mixture Models for On-line Training Evaluation in Virtual Reality Simulators. In: FIP 2003, China, vol. 2, pp. 733–740 (2003)Google Scholar
  14. 14.
    Moraes, R.M., Machado, L.S.: Evaluation System Based on EFuNN for On-line Training Evaluation in Virtual Reality. In: Sanfeliu, A., Cortés, M.L. (eds.) CIARP 2005. LNCS, vol. 3773, pp. 778–785. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Moraes, R.M., Machado, L.S.: On-line Training Evaluation in Virtual Reality Simulators using Fuzzy Bayes Rule. In: Proc. 7th FLINS Conference, Italy, pp. 791–798 (2006)Google Scholar
  16. 16.
    Morris, et al.: A Collaborative Virtual Environment for the Simulation of Temporal Bone Surgery. In: Proc. MICCAI, France, pp. 319–327 (2004)Google Scholar
  17. 17.
    Morris, et al.: Visuohaptic simulation of bone surgery for training and evaluation. IEEE Computer Graphics and Applications 26(6), 48–57 (2006)CrossRefGoogle Scholar
  18. 18.
    Park, K-S., Kenyon, R.V.: Effects of network characteristics on human performance in a collaborative virtual environment. In: Proc. of IEEE Virtual Reality, USA, pp. 104–111 (1999)Google Scholar
  19. 19.
    Rosen, J., Solazzo, M., Hannaford, B., Sinanan, M.: Objective Laparoscopic Skills Assessments of Surgical Residents Using Hidden Markov Models Based on Haptic Information and Tool/Tissue Interactions. Studies in Health Technology and Informatics 8, 417–423 (2001)Google Scholar
  20. 20.
    Shaw, C., Green, M.M.: Toolkit peers package and experiment. Proc. IEEE VRAIS, USA, pp. 463–469 (1993)Google Scholar
  21. 21.
    Sternberg, R.J., Grigorenko, E.: Dynamic Testing: The Nature and Measurement of Learning Potential. Cambridge University Press, Cambridge (2001)Google Scholar
  22. 22.
    Terano, T., Asai, K., Sugeno, M.: Fuzzy systems theory and it’s applications. Academic Press Inc. San Diego (1987)Google Scholar
  23. 23.
    Voss, G., et al.: Intelligent Training System for Laparoscopy and Hysteroscopy. Studies in Health Technology and Informatics (70), 359–364 (2000)Google Scholar
  24. 24.
    Yoshida, S., Noma, H., Hosaka, K., Proactive Desk, I.I.: Development of a New Multi-object Haptic Display Using a Linear Induction Motor. In: Proc. IEEE VR Conference, USA, pp. 269–272 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ronei M. Moraes
    • 1
  • Liliane S. Machado
    • 2
  1. 1.Department of Statistics 
  2. 2.Department of Informatics, Universidade Federal da Paraíba, Cidade Universitária s/n – João Pessoa/PBBrazil

Personalised recommendations