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
