KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Canada; Ryerson University, Department of Psychology, Toronto, Canada.
KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, Canada.
Int J Psychophysiol. 2022 Jun;176:14-26. doi: 10.1016/j.ijpsycho.2022.03.006. Epub 2022 Mar 16.
Visually induced motion sickness (VIMS) is a common sensation when using visual displays such as smartphones or Virtual Reality. In the present study, we investigated whether Machine Learning (ML) techniques in combination with physiological measures (ECG, EDA, EGG, respiration, body and skin temperature, and body movements) could be used to detect and predict the severity of VIMS in real-time, minute-by-minute. A total of 43 healthy younger adults (25 female) were exposed to a 15-minute VIMS-inducing video. VIMS severity was subjectively measured during the video using the Fast Motion Sickness Scale (FMS) as well as before and after the video using the Simulator Sickness Questionnaire (SSQ). Thirty-one participants (72%) experienced VIMS in the present study. Results showed that changes in facial skin temperature and body movement had the strongest relationship with VIMS. On a minute-by-minute basis, ML models revealed a medium correlation between the physiological measures and the FMS scores. An acceptable classification score distinguishing between sick and non-sick participants was found. Our findings suggest that physiological measures may be useful for measuring VIMS, but they are not a reliable standalone method to detect or predict VIMS severity in real-time.
视觉诱导运动病（VIMS）是使用智能手机或虚拟现实等视觉显示器时常见的感觉。在本研究中，我们研究了机器学习（ML）技术结合生理测量（ECG、EDA、EGG、呼吸、身体和皮肤温度以及身体运动）是否可以实时、逐分钟检测和预测 VIMS 的严重程度。共有 43 名健康的年轻成年人（25 名女性）暴露于 15 分钟的 VIMS 诱导视频中。在视频期间，使用快速运动病量表（FMS）以及在视频之前和之后使用模拟器病问卷（SSQ）对 VIMS 严重程度进行主观测量。在本研究中，31 名参与者（72%）经历了 VIMS。结果表明，面部皮肤温度和身体运动的变化与 VIMS 关系最密切。在逐分钟的基础上，ML 模型显示生理测量与 FMS 评分之间存在中等相关性。发现了一个可接受的分类评分，可区分患病和非患病参与者。我们的研究结果表明，生理测量可能有助于测量 VIMS，但它们不是实时检测或预测 VIMS 严重程度的可靠独立方法。
