Virtual reality (VR) technology has progressed rapidly and is used in various domains, particularly games. Simulator sickness (SS) still represents a significant problem for its wider adoption. The most common way to detect SS is using the simulator sickness questionnaire (SSQ). SSQ is a subjective measurement and is inadequate for real-time applications such as VR games. This research aims to develop a model to predict SS in real time using in-game characters’ movement and users’ eye motion data during gameplay in VR games. To achieve this, we designed an experiment to collect such data with three types of games. We trained a long short-term memory neural network with the eye-tracking and character movement data to predict SS. Our model can predict SS in real time with an accuracy of 83.4% for players who suffer from severe sensitivity to SS. Our results indicate that, in VR games, our model is an accurate and efficient method to predict SS in real time.

中文翻译：

---

虚拟现实游戏中模拟器晕眩的实时预测

虚拟现实（VR）技术发展迅速，并应用于各个领域，特别是游戏。模拟器晕眩（SS）仍然是其广泛采用的一个重大问题。检测 SS 最常见的方法是使用模拟器晕眩问卷 (SSQ)。SSQ 是一种主观测量，不适用于 VR 游戏等实时应用。本研究旨在开发一种模型，利用 VR 游戏中游戏角色的运动和用户眼部运动数据来实时预测 SS。为了实现这一目标，我们设计了一个实验，通过三种类型的游戏收集此类数据。我们使用眼球追踪和角色运动数据训练了一个长短期记忆神经网络来预测 SS。我们的模型可以实时预测 SS，对于 SS 严重敏感的玩家，准确率高达 83.4%。