Skills Assessment of Users in Medical Training Based on Virtual Reality Using Bayesian Networks

  • Ronei M. Moraes
  • Liliane S. Machado
  • Leandro C. Souza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)


Virtual reality allows the development of digital environments that can explore users’ senses to provide realistic and immersive experiences. When used for training purposes, interaction data can be used to verify users skills. In order to do that, intelligent methodologies must be coupled to the simulations to classify users´ skills into N a priori defined classes of expertise. To reach that, models based on intelligent methodologies are composed from data provided by experts. However, online Single User’s Assessment System (SUAS) for training must have low complexity algorithms to do not compromise the performance of the simulator. Several approaches to perform it have been proposed. In this paper, it is made an analysis of performance of SUAS based on a Bayesian Network and also a comparison between that SUAS and another methodology based on Classical Bayes Rule.


Medical Training User’s Assessment Bayesian Networks Virtual Reality 


  1. 1.
    Burdea, G., Coiffet, P.: Virtual Reality Technology, 2nd ed. Wiley Interscience (2003)Google Scholar
  2. 2.
    Cheng, J., Greiner, R.: Learning Bayesian Belief Network Classifiers: Algorithms and System. In: Proc. of the Fourteenth Canadian Conference on Artificial Intelligence (2001)Google Scholar
  3. 3.
    Dinsmore, M.: VR simulation: training for palpation of subsurface tumors. Master’s Thesis, Dep. Mechanical and Aerospace Eng., Rutgers University (October 1996)Google Scholar
  4. 4.
    Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. Wiley-Interscience (2000)Google Scholar
  5. 5.
    Gaba, D.M., et al.: Assessment of clinical performance during simulated crises using both technical and behavioral ratings. Anesthesiology 89, 8–18 (1998)CrossRefGoogle Scholar
  6. 6.
    Krause, P.J.: Learning Probabilistic Networks. Knowledge Engineering Review 13, 321–351 (1998)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    Machado, L.S., et al.: A Framework for Development of Virtual Reality-Based Training Simulators. Studies in Health Technology and Informatics 142, 174–176 (2009)Google Scholar
  9. 9.
    Machado, L.S., Moraes, R.M.: Intelligent Decision Making in Training Based on VR. In: Ruan, D. (org.) Computational Intelligence in Complex Decision Systems, ch. 4, pp. 85–123. Atlantis Press (2010)Google Scholar
  10. 10.
    Moraes, R.M., Machado, L.S.: Fuzzy Gaussian Mixture Models for On-line Training Evaluation in Virtual Reality Simulators. In: Int. Conference on Fuzzy Information Processing, China, pp. 733–740 (2003)Google Scholar
  11. 11.
    Moraes, R.M., Machado, L.S., Souza, L.C.: Online Assessment of Training in Virtual Reality Simulators Based on General Bayesian Networks. In: VI International Conference on Engineering and Computer Education (CDROM) (2009)Google Scholar
  12. 12.
    Moraes, R., Machado, L.: Development of a Medical Training System with Integration of Users’ Assessment. In: Kim, J.-J. (ed.) Virtual Reality, pp. 325–348. Intech, Vienna (2011)Google Scholar
  13. 13.
    Moraes, R.M., Rocha, A.V., Machado, L.S.: Intelligent Assessment Based on Beta Regression for Realistic Training in Medical Simulators. Knowledge-Based Systems (in press, 2012)Google Scholar
  14. 14.
    Neapolitan, R.E.: Learning Bayesian Networks. Prentice Hall Series in Artificial Intelligence (2003)Google Scholar
  15. 15.
    Scott, et al.: Measuring Operative Performance after Laparoscopic Skills Training: Edited Videotape versus Direct Observation. Journal of Laparoendoscopic & Advanced Surgical Techniques 10(4), 183–190 (2000)CrossRefGoogle Scholar
  16. 16.
    Wilson, M.S., et al.: MIST VR: a virtual reality trainer for surgery assesses performance. Annals of the Royal College of Surgeons of England 79, 4034 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ronei M. Moraes
    • 1
  • Liliane S. Machado
    • 1
  • Leandro C. Souza
    • 2
  1. 1.Federal University of ParaíbaJoão PessoaBrazil
  2. 2.Federal University of PernambucoRecifeBrazil

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