Advanced Optimization of Data Analysis for Walking-in-Place Locomotion Using Inertial Sensors in VR

Locomotion in virtual reality (VR) can be implemented by different methods. In the simplest cases, the motion is triggered by using a joystick or by using head movement tracking. More advanced approaches enable natural mapping movements to transform it into VR locomotion. But providing users to walk through a virtual environment in a limited physical space is often a challenge. Walking-in-place (WIP) offers a natural and immersive virtual locomotion that can greatly reduce symptoms of simulation sickness. In this paper, we consider the optimization of data analysis for WIP locomotion using inertial sensors. The algorithms, which detect the user's steps and controls the user's motions in the virtual worlds are proposed. We provide the method of analysis of experimental data for the suppression of false steps and the general usability of the system.