An Online Evaluation of Training in Virtual Reality Simulators using Fuzzy Gaussian Mixture Models and Fuzzy Relaxation Labeling

L. Dos Santos Machado and R. Marcos de Moraes (Brazil)


Virtual Reality, Online Evaluation Training, Fuzzy Gaussian Mixture Models, Fuzzy Relaxation Labeling, Fuzzy Sets.


Nowadays, virtual reality environments have been constructed with training objectives to provide realistic training. In this kind of system is important to know the quality of training to give a status of the user performance. Online evaluation allows the user to improve his learning because he can identify, immediately after the training, where he committed mistakes or presented low efficiency. In this paper we propose the use of a two-stage evaluator and to generalize the approach proposed by Moraes and Machado [1] using fuzzy extensions of those algorithms: Fuzzy Gaussian Mixture Models (FGMM) in the first stage and Fuzzy Relaxation Labeling (FRL) in the second stage.

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