It is well known that locomotion-dominated navigation tasks may highly provoke cybersickness effects. Past research has proposed numerous approaches to tackle this issue based on offline considerations. In this work, a novel approach to mitigate cybersickness is presented based on online adaptative navigation. Considering the Proportional-Integral-Derivative (PID) control method, we proposed a mathematical model for online adaptive navigation parameterized with several parameters, taking as input the users' electro-dermal activity (EDA), an efficient indicator to measure the cybersickness level, and providing as output adapted navigation accelerations. Therefore, minimizing the cybersickness level is regarded as an argument optimization problem: find the PID model parameters which can reduce the severity of cybersickness. User studies were organized to collect non-adapted navigation accelerations and the corresponding EDA signals. A deep neural network was then formulated to learn the correlation between EDA and navigation accelerations. The hyperparameters of the network were obtained through the Optuna open-source framework. To validate the performance of the optimized online adaptive navigation developed through the PID control, we performed an analysis in a simulated user study based on the pre-trained deep neural network. Results indicate a significant reduction of cybersickness in terms of EDA signal analysis and motion sickness dose value. This is a pioneering work which presented a systematic strategy for adaptive navigation settings from a theoretical point.

翻译：众所周知，以运动为主导的导航任务极易引发晕动症。以往研究提出了许多基于离线处理的方法来解决这一问题。本文提出了一种基于在线自适应导航的新型缓解晕动症方法。采用比例-积分-微分(PID)控制方法，我们构建了一个参数化的在线自适应导航数学模型，该模型以用户的皮肤电活动(EDA)——一种衡量晕动症水平的有效指标——作为输入，并输出自适应导航加速度。因此，将晕动症水平最小化视为一个参数优化问题：寻找能够降低晕动症严重程度的PID模型参数。我们组织了用户研究以采集非自适应导航加速度及其对应的EDA信号，随后构建了一个深度神经网络来学习EDA与导航加速度之间的相关性，并通过Optuna开源框架获取网络超参数。为验证基于PID控制优化的在线自适应导航性能，我们利用预训练的深度神经网络在模拟用户研究中进行了分析。结果表明，基于EDA信号分析和运动病剂量值，晕动症得到了显著缓解。这是一项开创性工作，从理论角度提出了自适应导航设置的系统性策略。