Abstract
With the rapid advancement of virtual reality (VR) technology, its adoption across domains such as healthcare, education, and entertainment has grown significantly. However, the persistent issue of cybersickness, marked by symptoms resembling motion sickness, continues to hinder widespread acceptance of VR. While recent research has explored multimodal deep learning approaches leveraging data from integrated VR sensors like eye and head tracking, there remains limited investigation into the use of video-based features for predicting cybersickness. In this study, we address this gap by utilizing transfer learning to extract high-level visual features from VR gameplay videos using the InceptionV3 model pretrained on the ImageNet dataset. These features are then passed to a Long Short-Term Memory (LSTM) network to capture the temporal dynamics of the VR experience and predict cybersickness severity over time. Our approach effectively leverages the time-series nature of video data, achieving a 68.4% classification accuracy for cybersickness severity. This surpasses the performance of existing models trained solely on video data, providing a practical tool for VR developers to evaluate and mitigate cybersickness in virtual environments. Furthermore, this work lays the foundation for future research on video-based temporal modeling for enhancing user comfort in VR applications.
Abstract (translated)
随着虚拟现实（VR）技术的迅速发展，它在医疗保健、教育和娱乐等领域的应用显著增加。然而，持续存在的网络眩晕问题——其症状类似于运动病——继续阻碍着VR的广泛接受度。尽管最近的研究探讨了多模态深度学习方法，这些方法利用集成的VR传感器（如眼睛跟踪和头部跟踪）的数据，但对于基于视频特征预测网络眩晕的应用研究仍较为有限。 在这项研究中，我们填补了这一空白，通过使用InceptionV3模型从预训练于ImageNet数据集的VR游戏视频中提取高级视觉特征，并采用迁移学习技术。然后，这些特征被传递给长短期记忆（LSTM）网络以捕捉VR体验的时间动态性并预测随着时间变化的网络眩晕严重程度。 我们的方法有效地利用了视频数据的时间序列特性，在网络眩晕严重度分类上达到了68.4%的准确率。这超过了仅基于视频数据训练的现有模型的表现，为VR开发者提供了一种实用工具，用于评估和缓解虚拟环境中的网络眩晕问题。此外，这项工作还为未来研究打下了基础，即通过基于视频的时间建模来增强VR应用中用户的舒适度。
URL
https://arxiv.org/abs/2510.10422
