Skills assessment of users in medical training based on virtual reality using Bayesian networks

Abstract

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 userś 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. © 2012 Springer-Verlag.