Using Nonlinear Kinematic Parameters as a Means of Predicting Motion Sickness in Real-Time in Virtual Environments
Using Nonlinear Kinematic Parameters as a Means of Predicting Motion Sickness in Real-Time in Virtual Environments
复制标题
使用非线性运动学参数作为虚拟环境中实时预测晕动病的方法
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
PhD Eric Bachmann
中科院分区：
文献类型：
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作者：
PhD L. James Smart;MS Anthony Drew;MS Tyler Hadidon;M. Teaford;PhD Eric Bachmann
目的本文介绍了两项研究（一个模拟和一个试点），评估自定义的计算机算法，旨在实时预测晕动病。背景虚拟现实具有广泛的应用;然而，许多用户经历视觉诱发的晕动病。先前的研究已经表明，运动（行为）参数的变化是运动病的预测。然而，还没有研究表明这些措施可以在实时应用中使用。方法进行了两项研究，以评估设计用于实时预测晕动病的算法。研究1是一项模拟研究，使用了Smart等人（2014）的数据。研究2采用该算法对28名新参与者的运动，同时暴露于虚拟运动。结果研究1显示，该算法能够以100%的准确率对晕动病参与者进行分类。研究2显示，该算法可以预测参与者是否会晕车，准确率为57%。结论本研究的结果表明，运动病预测算法可以预测一个人是否会经历运动病，但需要进一步完善，以提高性能。该算法可用于各种VR设备，以预测晕动病的可能性，并有足够的时间进行干预。
Objective This article presents two studies (one simulation and one pilot) that assess a custom computer algorithm designed to predict motion sickness in real-time. Background Virtual reality has a wide range of applications; however, many users experience visually induced motion sickness. Previous research has demonstrated that changes in kinematic (behavioral) parameters are predictive of motion sickness. However, there has not been research demonstrating that these measures can be utilized in real-time applications. Method Two studies were performed to assess an algorithm designed to predict motion sickness in real-time. Study 1 was a simulation study that used data from Smart et al. (2014). Study 2 employed the algorithm on 28 new participants’ motion while exposed to virtual motion. Results Study 1 revealed that the algorithm was able to classify motion sick participants with 100% accuracy. Study 2 revealed that the algorithm could predict if a participant would become motion sick with 57% accuracy. Conclusion The results of the present study suggest that the motion sickness prediction algorithm can predict if an individual will experience motion sickness but needs further refinement to improve performance. Application The algorithm could be used for a wide array of VR devices to predict likelihood of motion sickness with enough time to intervene.