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

机译:贝叶斯网络基于虚拟现实的医学培训用户技能评估

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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.
机译:虚拟现实允许开发数字环境,该数字环境可以探索用户的感觉,以提供逼真的和身临其境的体验。当用于培训目的时,交互数据可用于验证用户的技能。为此,必须将智能方法论与模拟相结合,以将用户的技能分类为N个先验定义的专业知识类别。为此,专家提供的数据构成了基于智能方法的模型。但是,用于培训的在线单用户评估系统(SUAS)必须具有低复杂度的算法,才能不损害模拟器的性能。已经提出了几种执行它的方法。本文对基于贝叶斯网络的SUAS的性能进行了分析,并将其与基于经典贝叶斯规则的另一种方法进行了比较。

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