Virtual reality (VR) is an imminent trend in games, education, entertainment, military, and health applications, as the use of head-mounted displays is becoming accessible to the mass market. Virtual reality provides immersive experiences but still does not offer an entirely perfect situation, mainly due to Cybersickness (CS) issues. In this work, we first present a detailed review about possible causes of CS. Following, we propose a novel CS prediction solution. Our system is able to suggest if the user may be entering in the next moments of the application into an illness situation. We use Random Forest classifiers, based on a dataset we have produced. The CSPQ (Cybersickness Profile Questionnaire) is also proposed, which is used to identify the player's susceptibility to CS and the dataset construction. In addition, we designed two immersive environments for empirical studies where participants are asked to complete the questionnaire and describe (orally) the degree of discomfort during their gaming experience. Our data was achieved through 84 individuals on different days, using VR devices. Our proposal also allows us to identify which are the most frequent attributes (causes) in the observed discomfort situations.

中文翻译：

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将虚拟环境中的不适感降至最低的策略的自动推荐

虚拟现实 (VR) 是游戏、教育、娱乐、军事和健康应用领域的一个迫在眉睫的趋势，因为大众市场可以使用头戴式显示器。虚拟现实提供了身临其境的体验，但仍然不能提供完全完美的情况，这主要是由于 Cyber​​sickness (CS) 问题。在这项工作中，我们首先详细回顾了 CS 的可能原因。接下来，我们提出了一种新颖的 CS 预测解决方案。我们的系统能够建议用户是否可能在应用程序的下一个时刻进入疾病状态。我们使用随机森林分类器，基于我们生成的数据集。还提出了CSPQ（Cyber​​sickness Profile Questionnaire），用于识别玩家对CS的易感性和数据集构建。此外，我们为实证研究设计了两个沉浸式环境，要求参与者完成问卷并（口头）描述他们在游戏体验中的不适程度。我们的数据是通过 84 个人在不同的日子使用 VR 设备获得的。我们的提议还使我们能够确定在观察到的不适情况中哪些是最常见的属性（原因）。