Step on it: asymmetric gain functions improve starting and stopping in virtual reality walking

Abstract Transfer functions with a high translational gain can increase the range of walking in virtual reality. These determine how much movements are amplified compared to corresponding physical movements. However, it is unclear design these influences user’s gait and experience when values. In mixed-methods study 20 users, we find that their best transfer nonlinear asymmetrical for starting stopping. We use an optimization approach individually optimized significantly better than common using constant gain. Based on interviews, also discuss what qualities matter users vary across different functions. Our work shows possible create high-gain techniques offer dramatically increased motion speed but still feel like normal walking.