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首页> 外文会议>IEEE Conference on Virtual Reality and 3D User Interfaces >VR Sickness Prediction for Navigation in Immersive Virtual Environments using a Deep Long Short Term Memory Model
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VR Sickness Prediction for Navigation in Immersive Virtual Environments using a Deep Long Short Term Memory Model

机译：使用深长短期记忆模型在沉浸式虚拟环境中导航的VR疾病预测

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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.
机译：本文提出了一种在虚拟环境（VEs）导航环境下视觉诱发晕动病（VIMS）的新客观指标。与物理环境中的晕车类似，VIMS可以诱发许多生理症状，例如全身不适，恶心，迷失方向，呕吐，头晕和疲劳。为了提高用户对VR应用程序的满意度，开发针对VIMS的客观指标具有重要意义，该指标可以分析和估算用户暴露于VE时的VR疾病水平。姿势不稳定性是众所周知的客观指标之一。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LSTM模型，如果来自暴露后的姿势摇摆信号与暴露前信号有足够的差异，则该模型将无法进行编码并正确解码信号;重建误差的跳跃称为损失，并被提议作为拟议的模拟器疾病的客观度量。对拟议指标的有效性进行了分析，并与基于仿真器疾病问卷（SSQ）的主观评估方法在VR环境中进行了比较，得出Pearson相关系数。 89.最后，我们证明了所提出的方法有可能部署在闭环系统中，并具有实时性能来预测VR疾病，为基于生理反馈开发以用户为中心和定制VR应用提供了新的见解。

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