基于心理生理数据的虚拟现实游戏模拟器疾病实时的检测
Real-Time Detection of Simulator Sickness in Virtual Reality Games Based on Players' Psychophysiological Data during Gameplay
摘要：
虚拟现实（VR）技术在过去的十年中，特别是在最近几年中得到了迅速发展。然而，模拟器疾病（SS）仍然是其更广泛采用的一个重要问题。目前，检测SS最常用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游戏等实时的【形】应用。本研究旨在探讨如何利用机器学习技术，在虚拟现实游戏中，基于游戏角色和用户的生理数据来检测SS。为了实现这一点，我们设计了一个实验，用三种类型的游戏来收集这些数据。我们用眼动数据集和人物运动数据训练了一个长短期记忆神经网络来实时的【形】检测SS。我们的研究结果表明，在VR游戏中，我们的模型是一个准确和有效的方式来检测SS在实时的【形】。
英文摘要：
Virtual Reality (VR) technology has been proliferating in the last decade, especially in the last few years. However, Simulator Sickness (SS) still represents a significant problem for its wider adoption. Currently, 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 investigate how to use machine learning techniques to detect SS based on in-game characters' and users' physiological 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 dataset eye-tracking and character movement data to detect SS in real-time. Our results indicate that, in VR games, our model is an accurate and efficient way to detect SS in real-time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
