论文标题
自动推荐策略，以最大程度地减少虚拟环境中的不适
Automatic Recommendation of Strategies for Minimizing Discomfort in Virtual Environments
论文作者
论文摘要
虚拟现实（VR）是游戏，教育，娱乐，军事和健康应用中的迫在眉睫的趋势，因为大众市场的使用越来越可以访问头部安装的显示器。虚拟现实提供了沉浸式的体验，但仍然没有提供完全完美的情况，这主要是由于Cybersickness（CS）问题。在这项工作中，我们首先介绍了有关CS的可能原因的详细评论。随后，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以在应用程序的下一刻进入疾病情况。我们根据我们生成的数据集使用随机的森林分类器。还提出了CSPQ（Cybersickness Profile问卷），用于识别玩家对CS和数据集构建的敏感性。此外，我们为实证研究设计了两个沉浸式环境，要求参与者填写问卷，并在游戏体验中描述（口头）不适的程度。我们的数据是在不同日期使用VR设备在不同日期通过的84个人实现的。我们的建议还使我们能够确定哪些是观察到的不舒服情况下最常见的属性（原因）。
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.
