Navigation behavior during visual wayfinding in people with ultra-low vision using virtual reality

Visual wayfinding is essential for safe navigation but remains poorly characterized in people with ultra-low vision (ULV). Because assessing complex environments in the real world carries safety risks, this study utilized a calibrated virtual reality (VR) platform to safely quantify navigation. Participants with ULV, normal vision (NV), and simulated ULV (sULV) completed tasks across three environments (street crossing, cafeteria, and metro station) of increasing complexity to determine which metrics best capture task difficulty. Navigation metrics included motion onset latency, walking speed, path efficiency, and turn deviation derived from head position data. Participants with ULV showed longer onset latency, slower walking speed, reduced path efficiency, and greater turn deviation compared with NV, while sULV showed intermediate performance. These metrics successfully reflected increasing task difficulty across environments, with the metro station posing the greatest challenge. Path efficiency consistently detected differences between environments across groups, whereas turn deviation provided insight into complex tasks. Findings indicate that diverse virtual environments capture distinct aspects of navigation that cannot be safely studied in the real world, and trajectory-based metrics capture navigation behavior more effectively than conventional measures. VR-based assessment offers a useful approach for evaluating functional navigation and guiding rehabilitation strategies in profound vision loss.