Exploring the Feasibility of Head-Tracking Data for Cybersickness Prediction in Virtual Reality

Cybersickness remains a significant barrier to the widespread adoption of virtual reality (VR) technology. Traditional methods for predicting cybersickness rely on self-reported questionnaires or physiological signals from specialized sensors, which have their limitations. This study explores the potential of using real-time, easily acquired head-tracking data (HTD) from standard VR headsets as a scalable alternative for estimating cybersickness. Twelve participants engaged in a VR session using an Oculus Quest 2 headset while their HTD were recorded. Kinematic metrics such as linear and angular velocity, acceleration, and jerk were computed from the HTD, including positional and angular parameters. Participants’ cybersickness levels were assessed using the Virtual Reality Sickness Questionnaire. While exploratory data analysis revealed no significant direct correlation between individual kinematic variables and cybersickness scores, machine learning models were employed to identify predictive patterns. Subsequently, four regression models, including Random Forest, Gradient Boosting, K-Nearest Neighbors, and Support Vector Machines, were trained and evaluated using the computed kinematic features to predict the cybersickness score. Among these, the Gradient Boosting model demonstrated superior performance, accurately predicting cybersickness scores with normalized differences less than 3.08% on unseen data. This approach offers a scalable and practical solution for real-time cybersickness prediction in VR applications and compliments other techniques that rely on physiological sensors, hardware, or user profiles.