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

Modeling online adaptive navigation in virtual environments based on PID control