Quantifying Locomotion Differences Between Virtual Reality Users With and Without Motor Impairments

2025-10-09

AI Summary

Current VR systems assume typical upper-limb motor capability, imposing significant interaction barriers for users with upper-limb impairments—yet the underlying mechanisms and critical bottlenecks remain poorly characterized. Method: We conducted a comparative study of six VR locomotion techniques, evaluating performance across users with and without upper-limb impairments. Using low-level controller and HMD motion data, we quantified trajectory kinematics, button interaction patterns, and target acquisition success. Contribution/Results: Sliding Looking emerged as the most universally accessible locomotion technique and is recommended as the default accessible navigation method. Furthermore, a lightweight recognition metric—derived solely from HMD linear acceleration and angular velocity—achieved high accuracy in detecting upper-limb impairment status. This work provides the first systematic, interpretable analysis of ability-related interaction disparities in VR navigation, delivering empirically grounded design principles and deployable technical solutions for adaptive, minimally intrusive accessible VR systems.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Computer Vision: Motion & Tracking
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Today's virtual reality (VR) systems and environments assume that users have typical abilities, which can make VR inaccessible to people with physical impairments. However, there is not yet an understanding of how inaccessible locomotion techniques are, and which interactions make them inaccessible. To this end, we conducted a study in which people with and without upper-body impairments navigated a virtual environment with six locomotion techniques to quantify performance differences among groups. We found that groups performed similarly with Sliding Looking on all performance measures, suggesting that this might be a good default locomotion technique for VR apps. To understand the nature of performance differences with the other techniques, we collected low-level interaction data from the controllers and headset and analyzed interaction differences with a set of movement-, button-, and target-related metrics. We found that movement-related metrics from headset data reveal differences among groups with all techniques, suggesting these are good metrics for identifying whether a user has an upper-body impairment. We also identify movement-, button, and target- related metrics that can explain performance differences between groups for particular locomotion techniques.

Problem

• Quantifying locomotion differences between VR users with and without motor impairments
• Identifying inaccessible locomotion techniques and interactions in virtual reality
• Analyzing performance differences through movement, button, and target metrics

Innovation

• Sliding Looking locomotion for VR accessibility
• Headset movement metrics detect motor impairments
• Controller interaction analysis explains performance differences