Detection and prediction of cybersickness in virtual and mixed reality environments using wearables

Virtual and mixed reality (VR/MR) systems have burgeoned over the last couple of years with applications in healthcare, gaming, telepresence, and skills training, to name a few. Within the skills training sector, for example, police/law enforcement training is an important application domain which has seen increased adoption worldwide. In Canada, Public Safety and the Royal Canadian Mounted Police (RCMP) have invested millions of dollars to set up state-of-the-art VR/MR facilities to train the next generation of law enforcement agents. VR allows for different scenes, conditions, and maneuvers to be tested in one single physical location, thus not only reducing training costs, but better equipping trainees to handle unknowns. While the potential is there, existing VR/MR systems are known to induce motion sickness – known as cybersickness – on a large proportion of the trainees, especially females. As such, cybersickness detection and prevention methods are drastically needed in order to provide a more inclusive training environment. This project aims to solve this problem via the use of multimodal signal processing of wearable device signals.

Faculty Supervisor:

Tiago H Falk

Student:

Partner:

Thales Recherche et Technologie

Discipline:

Engineering

Sector:

Management of companies and enterprises; Manufacturing; Professional, scientific and technical services

University:

Université du Québec : Institut national de la recherche scientifique

Program: