Real-Time Prediction of Simulator Sickness in Virtual Reality Games
Real-Time Prediction of Simulator Sickness in Virtual Reality Games
复制标题
虚拟现实游戏中模拟器晕眩的实时预测
DOI：
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发表时间：
2023
影响因子：
2.3
通讯作者：
Jimin Xiao
中科院分区：
文献类型：
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作者：
Jialin Wang;Hai;D. Monteiro;Wenge Xu;Jimin Xiao
虚拟现实(VR)技术发展迅速，广泛应用于各个领域，尤其是游戏领域。模拟器病(SS)仍然是广泛采用它的一个重要问题。检测SS最常见的方法是使用模拟器疾病问卷(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在游戏过程中的眼动数据来实时预测SS。为了实现这一点，我们设计了一个实验来收集三种类型的游戏的数据。我们用眼球跟踪和人物运动数据训练了一个长短期记忆神经网络来预测SS。对于对SS有严重敏感性的玩家，我们的模型可以实时预测SS，准确率达到83.4%。我们的结果表明，在VR游戏中，我们的模型是一种准确有效的实时预测SS的方法。
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.