Larger step faster speed: Investigating gesture-amplitude-based locomotion in place with different virtual walking speed in virtual reality

Abstract

In this paper, we present a series of user studies to investigate the technique of gesture-amplitude-based walking-speed control for locomotion in place (LIP) in virtual reality (VR). Our 1st study suggested that compared to tapping and goose-stepping, the gesture of marching in place was significantly preferred by users across three different virtual walking speed (i.e., 1 ×, 3 ×, and 10 ×) while sitting and standing, and it yielded larger motion difference across the three speed levels. With the tracker data recorded in the 1st study, we trained a Support- Vector-Machine classification model for LIP speed control based on users' leg/foot gestures in marching in place. The overall accuracy for classifying three speed levels was above 90% for sitting and standing. With the classification model, we then compared the marching-in-place speed-control technique with the controller-based teleportation approach on a target-reaching task where users were sitting and standing. We found no significant difference between the two conditions in terms of target-reaching accuracy. More importantly, the technique of marching in place yielded significantly higher user ratings in terms of naturalness, realness, and engagement than the controller-based teleportation did.