Evaluating the Minimum Jerk Motion Model for Redirected Reach in Virtual Reality

Eric J. Gonzalez, Parastoo Abtahi, Sean Follmer

Abstract

Reach redirection in virtual reality uses spatial distortion to augment interaction with passive props as well as active haptic devices. For such dynamic physical systems, motion modeling is needed to update the interface based on users’ predicted targets & trajectories. However, it remains unclear how well standard predictive models hold under redirection. In this work we evaluate one such commonly used model, the Minimum-Jerk (MJ) model, during redirected reach at various lateral offsets up to 16 cm. Results show that larger redirection significantly worsens MJ model fit, suggesting that models should be adjusted for reaches with considerable redirection. Predicted arrival times, based on fitting an MJ model on the first half of reach data, led to an average error of -0.29 s for redirected reach, compared to -0.03 s for normal reach.

Extended Abstract:

Eric J. Gonzalez, Parastoo Abtahi, and Sean Follmer. 2019. Evaluating the Minimum Jerk Motion Model for Redirected Reach in Virtual Reality. In the Adjunct Publication of the 32nd Annual ACM Symposium on User Interface Software and Technology (UIST ’19). ACM, New Orleans, LA, USA. DOI: http://dx.doi.org/10.1145/3332167.3357096