The occurrence of cybersickness in virtual reality (VR) significantly impairs users' perception and sense of immersion. Therefore, timely detection of cybersickness and the application of appropriate intervention strategies are crucial for enhancing the user experience. However, existing cybersickness detection methods often suffer from issues such as poor detection reliability across different levels of cybersickness and unnecessary model complexity. Furthermore, while cybersickness exhibits significant inter-user variability, most existing approaches aggregate all data from users and lack user-specific solutions. In this paper, we investigate a lightweight approach for cybersickness detection incorporating an ensemble learning model and user-specific eye and head tracking data. Our experiments using the open-source dataset Simulation 2021 demonstrate that feature engineering and training set construction are critical for determining detection performance. Models trained with data from similar-content segments achieve the best results, attaining detection accuracies of 93% in the cross-user setting and 88% in the user-personalized setting, using only 23-dimensional eye and head features. Moreover, by using user-specific data, well-tuned ensemble learning models with shorter training and inference times can be feasibly applied to real-world cybersickness detection, offering superior time efficiency and outstanding detection performance. This work offers useful evidence toward the development of lightweight and user-adaptive cybersickness detection models for VR applications.

翻译：虚拟现实（VR）中晕动症的发生会显著削弱用户的感知体验与沉浸感。因此，及时检测晕动症并采取适当的干预策略对提升用户体验至关重要。然而，现有晕动症检测方法常存在不同晕动症等级下的检测可靠性不足、模型过度复杂等问题。此外，尽管晕动症具有显著的用户间差异性，但现有方法大多聚合所有用户数据，缺乏针对特定用户的解决方案。本文提出一种轻量级晕动症检测方法，该方法集成集成学习模型与用户特定的眼部和头部追踪数据。基于开源数据集Simulation 2021的实验表明，特征工程与训练集构建对检测性能具有决定性作用。采用相似内容片段数据训练的模型表现最优，在跨用户场景下检测准确率达93%，用户个性化场景下达88%，且仅需23维眼部和头部特征。此外，通过利用用户特定数据，经过良好调优的集成学习模型能以更短的训练与推理时间实现现实场景中晕动症的检测，兼具卓越的时间效率与检测性能。本研究为开发面向VR应用的轻量化、自适应晕动症检测模型提供了有效依据。