VR Sickness Prediction for Navigation in Immersive Virtual Environments using a Deep Long Short Term Memory Model
VR Sickness Prediction for Navigation in Immersive Virtual Environments using a Deep Long Short Term Memory Model
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使用深度长短期记忆模型对沉浸式虚拟环境中的导航进行 VR 疾病预测
DOI：
10.1109/vr.2019.8798213
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
F. Mérienne
中科院分区：
文献类型：
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作者：
Yuyang Wang;J. Chardonnet;F. Mérienne
本文提出了一种新的客观度量的视觉诱导晕动病（VIMS）的背景下，在虚拟环境（VE）中的导航。与物理环境中的晕动病类似，VIMS可引起许多生理症状，如全身不适、恶心、定向障碍、呕吐、头晕和疲劳。为了提高用户对VR应用的满意度，开发用于VIMS的客观指标具有重要意义，该指标可以分析和估计用户暴露于VE时的VR疾病水平。众所周知的客观指标之一是姿势不稳定。在本文中，我们使用暴露前捕获的正常状态姿势信号为每个参与者训练了一个LSTM模型，如果暴露后的姿势摇摆信号与暴露前的信号完全不同，模型将无法正确编码和解码信号;重建误差的跳跃被称为损失，并被提议作为模拟器疾病的客观度量。该指标的有效性进行了分析，并与主观评估方法的基础上模拟器疾病问卷（SSQ）在VR环境中，实现了皮尔逊相关系数。89.最后，我们证明了所提出的方法有可能在闭环系统中部署，并获得实时性能来预测VR疾病，为开发基于生理反馈的以用户为中心的定制VR应用程序提供了新的见解。
This paper proposes a new objective metric of visually induced motion sickness (VIMS) in the context of navigation in virtual environments (VEs). Similar to motion sickness in physical environments, VIMS can induce many physiological symptoms such as general discomfort, nausea, disorientation, vomiting, dizziness and fatigue. To improve user satisfaction with VR applications, it is of great significance to develop objective metrics for VIMS that can analyze and estimate the level of VR sickness when a user is exposed to VEs. One of the well-known objective metrics is the postural instability. In this paper, we trained a LSTM model for each participant using a normal-state postural signal captured before the exposure, and if the postural sway signal from post-exposure was sufficiently different from the pre-exposure signal, the model would fail at encoding and decoding the signal properly; the jump in the reconstruction error was called loss and was proposed as the proposed objective measure of simulator sickness. The effectiveness of the proposed metric was analyzed and compared with subjective assessment methods based on the simulator sickness questionnaire (SSQ) in a VR environment, achieving a Pearson correlation coefficient of. 89. Finally, we showed that the proposed method had the potential to be deployed within a closed-loop system and get real-time performance to predict VR sickness, opening new insights to develop user-centered and customized VR applications based on physiological feedback.