利用游戏玩法和用户数据预测及识别VR游戏中晕动症（CS）表现的原因
Using the gameplay and user data to predict and identify causes of cybersickness manifestation in virtual reality games
AI总结：
本研究提出基于随机森林分类器的新方法，结合自研CS概况问卷构建的数据集，预测VR用户的晕动症症状并识别相关影响属性，验证采用16种机器学习技术取得最佳结果。
AI中文摘要：
虚拟现实（VR）是游戏、教育、娱乐、军事及健康应用领域的新兴趋势，因为头戴式显示器已普及至大众。尽管VR能提供沉浸式体验，但仍存在缺陷，主要问题是晕动症（CS）。本研究提出一种预测即将出现的CS症状的新方法，该方法可判断VR用户是否将进入发病状态。我们采用随机森林分类器，并使用16种不同机器学习技术对方案进行验证，这些技术取得了最佳结果。为训练模型，我们构建了自有数据集，所用CS概况问卷亦为本研究提出，该问卷聚焦于记录和识别用户的CS易感性，同时考虑用户的历史状况及其对我们开发的沉浸式环境的反应。本研究选取86名受试者，在不同天数向其发放所开发问卷，将答案整理为数据集。我们的方案还能识别导致观察到的压力及不适状况的属性（原因与个体参数）。
英文摘要：
Virtual reality (VR) is an imminent trend in games, education, entertainment, military, and health applications, as the use of head-mounted displays is accessible to everyone. While VR provides immersive experiences, it still does not offer an entirely perfect situation, mainly due to cybersickness (CS) issues. In this work, we propose a novel approach for predicting upcoming CS symptoms. Our solution is able to suggest whether the user of VR is entering into an illness situation. We adopted random forest classifiers and validated our solution using 16 different machine-learning techniques, which presented the best results. For training purposes, we built our own dataset through a CS profile questionnaire that we also propose in the present work. The questionnaire is focused on registering and identifying the user's susceptibility to CS, considering their historical conditions and also their response to the immersive environment developed by us. In this method, 86 individuals are selected and the developed questionnaire was put to them on different days, and the answers are compiled as dataset. Our proposal also identifying attributes responsible (causes and individual's parameters) for the observed stressful and uncomfortable situations.
