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Description
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. Twenty-eight participants engaged in a VR session using an Oculus Quest 2 headset while their HTD was 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. The Gradient Boosting model demonstrated superior performance, accurately predicting cybersickness scores. Among these, the Gradient Boosting model demonstrated superior performance, accurately predicting cybersickness scores with prediction normalized differences of less than 4.8% 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.
Publication Date
2-27-2025
Document Type
Poster
Publisher
Grand Forks, ND
Disciplines
Computer Sciences
Recommended Citation
Ramaseri-Chandra, Ananth; Reza, Hassan; and Pothana, Prasad, "Exploring the Feasibility of Head‐Tracking Data for Cybersickness Prediction in Virtual Reality" (2025). Graduate Research Achievement Day Posters. 11.
https://commons.und.edu/grad-posters/11
Comments
Presented at the 2025 UND Graduate Research Achievement Day.