This systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly popular and is an important part of current advances in human training, therapies, entertainment, and access to the metaverse. Usage of this technology is limited by cybersickness, a common debilitating condition experienced upon VR immersion. Cybersickness is accompanied by a mix of symptoms including nausea, dizziness, fatigue and oculomotor disturbances. Machine learning can be used to identify cybersickness and is a step towards overcoming these physiological limitations. Practical implementation of this is possible with optimised data collection from wearable devices and appropriate algorithms that incorporate advanced machine learning approaches. The present systematic review focuses on 26 selected studies. These concern machine learning of biometric and neuro-physiological signals obtained from wearable devices for the automatic identification of cybersickness. The methods, data processing and machine learning architecture, as well as suggestions for future exploration on detection and prediction of cybersickness are explored. A wide range of immersion environments, participant activity, features and machine learning architectures were identified. Although models for cybersickness detection have been developed, literature still lacks a model for the prediction of first-instance events. Future research is pointed towards goal-oriented data selection and labelling, as well as the use of brain-inspired spiking neural network models to achieve better accuracy and understanding of complex spatio-temporal brain processes related to cybersickness.

中文翻译：

---

用于研究晕机症的机器学习方法：系统回顾

这篇系统综述提供了世界首创的机器学习方法和系统批判性分析，以及虚拟现实 (VR) 引起的晕机症研究的未来方向。VR 正变得越来越流行，并且是当前人类训练、治疗、娱乐和进入元宇宙方面取得进步的重要组成部分。这项技术的使用受到晕机症的限制，晕机症是沉浸在 VR 中时常见的衰弱状况。电子病伴随着多种症状，包括恶心、头晕、疲劳和眼球运动障碍。机器学习可用于识别晕机症，是克服这些生理限制的一步。通过优化可穿戴设备的数据收集和结合高级机器学习方法的适当算法，可以实际实现这一点。本系统评价重点关注 26 项选定的研究。这些涉及从可穿戴设备获得的生物特征和神经生理信号的机器学习，用于自动识别晕机症。探讨了方法、数据处理和机器学习体系结构，以及对未来网络病检测和预测探索的建议。确定了广泛的沉浸式环境、参与者活动、功能和机器学习架构。尽管已经开发了晕动症检测模型，但文献中仍然缺乏用于预测一审事件的模型。