Users would experience individually different sickness symptoms during or after navigating through an immersive virtual environment, generally known as cybersickness. Previous studies have predicted the severity of cybersickness based on physiological and/or kinematic data. However, compared with kinematic data, physiological data rely heavily on biosensors during the collection, which is inconvenient and limited to a few affordable VR devices. In this work, we proposed a deep neural network to predict cybersickness through kinematic data. We introduced the encoded physiological representation to characterize the individual susceptibility; therefore, the predictor could predict cybersickness only based on a user's kinematic data without counting on biosensors. Fifty-three participants were recruited to attend the user study to collect multimodal data, including kinematic data (navigation speed, head tracking), physiological signals (e.g., electrodermal activity, heart rate), and Simulator Sickness Questionnaire (SSQ). The predictor achieved an accuracy of 98.3\% for cybersickness prediction by involving the pre-computed physiological representation to characterize individual differences, providing much convenience for the current cybersickness measurement.

翻译：用户在沉浸式虚拟环境中导航期间或之后会经历个体差异明显的晕动症状，即通常所称的晕动症。已有研究基于生理和/或运动数据预测晕动症的严重程度。然而，与运动数据相比，生理数据在采集过程中严重依赖生物传感器，这不仅不便且仅适用于少数可负担的VR设备。本文提出了一种深度神经网络，通过运动数据预测晕动症。我们引入编码生理表征来描述个体易感性；因此，该预测器能够仅基于用户运动数据进行晕动预测，无需依赖生物传感器。研究招募了53名参与者进行用户实验，收集了多模态数据，包括运动数据（导航速度、头部追踪）、生理信号（如皮肤电活动、心率）以及模拟器晕动问卷(SSQ)。通过引入预计算的生理表征来描述个体差异，该预测器在晕动预测中达到了98.3%的准确率，为当前晕动测量提供了极大便利。