How far along the future path do individuals recognize the path for stepping on multiple footfall targets? A new evaluation method under virtual reality.

INTRODUCTION

The ability to visually recognize the path ahead during walking is essential for adjusting gait patterns in an anticipatory manner to mitigate perturbations induced by tripping. In this study, we aimed to develop a walking task within a virtual reality (VR) environment, termed the VR multi-target stepping (VR-MTS) task, as a method to evaluate the extent to which individuals can recognize the path ahead while continuously stepping on footfall targets.

METHODS

As an initial study for the development of the VR multi-target stepping (VR-MTS) task, we tested a sample of young individuals (8 males and 5 females, aged 26.2 ± 3.7 years). Participants donned a head-mounted display (HMD) and walked for a distance of 4 m, under four distinct conditions. Participants were instructed to step on squares of specific color and that participants were instructed to step onto white squares that had been of the footfall target color for all conditions. In three of these conditions, all three colored squares present in the same row-located either one, two, or three rows ahead of the participants-were programmed to change to white (i.e., N + 1, N + 2, and N + 3 conditions). This setup was designed to evaluate the participant's ability to recognize the colors of the footfall targets at varying distances. In the control condition, no changes occurred in the colored squares during the walking task.

RESULTS AND DISCUSSION

The rate of stepping failure was significantly higher under the N + 3 condition compared to the other three conditions. This finding suggests that young individuals are capable of recognizing footfall targets approximately two rows ahead when performing the VR multi-target stepping (VR-MTS) task but encounter difficulties when attempting to recognize targets located three rows ahead. Under the N + 3 condition, participants frequently stepped onto distractor squares, indicating a failure to recognize the stepping target situated three rows ahead, resulting in a random selection of the square to step on. Based on these findings, we conclude that the VR-MTS task is a valid method for evaluating visual recognition of the future path while stepping on multiple footfall targets.