Individualized Calibration of Rotation Gain Thresholds for Redirected Walking

Abstract

Redirected walking allows the exploration of large virtual environments within a limited physical space. To achieve this, redirected walking algorithms must maximize the rotation gains applied while remaining imperceptible to the user. Previous research has established population averages for redirection thresholds, including rotation gains. However, these averages do not account for individual variation in tolerance of and susceptibility to redirection. This paper investigates methodologies designed to quickly and accurately calculate rotation gain thresholds for an individual user. This new method is straightforward to implement, requires a minimal amount of space, and takes only a few minutes to estimate a user's personal threshold for rotation gains. Results from a user study support the wide variability in detection thresholds and indicate that the method of parameter estimation through sequential testing (PEST) is viable for efficiently calibrating individual thresholds.