  Virtual Reality (VR) can cause an unprecedented immersion and feeling of
presence yet a lot of users experience motion sickness when moving through a
virtual environment. Rollercoaster rides are popular in Virtual Reality but
have to be well designed to limit the amount of nausea the user may feel. This
paper describes a novel framework to get automated ratings on motion sickness
using Neural Networks. An application that lets users create rollercoasters
directly in VR, share them with other users and ride and rate them is used to
gather real-time data related to the in-game behaviour of the player, the track
itself and users' ratings based on a Simulator Sickness Questionnaire (SSQ)
integrated into the application. Machine learning architectures based on deep
neural networks are trained using this data aiming to predict motion sickness
levels. While this paper focuses on rollercoasters this framework could help to
rate any VR application on motion sickness and intensity that involves camera
movement. A new well defined dataset is provided in this paper and the
performance of the proposed architectures are evaluated in a comparative study.
