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Online Assessment in Medical Simulators Based on Virtual Reality Using Fuzzy Gaussian Naive Bayes
Ronei Marcos de Moraes and Liliane dos Santos Machado

Several approaches have been proposed to perform online or offline assessment in medical training simulators based on virtual reality. The goal is to collect interaction data during a realistic simulation of procedures in order to provide to trainees feedback about their skills. In this paper, we present a new approach to online training assessment based on Fuzzy Gaussian Naive Bayes (FGNB) for modeling and classification of simulation in M pre-defined classes, which is a generalization of Gaussian Bayes Networks. The results obtained showed that FGNB presents significant better assessment when compared to other two methods.

Keywords: On-line training assessment, Fuzzy gaussian naive bayes, Medical simulators, Virtual reality

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