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    Area of Science:

    • Human-Computer Interaction
    • Virtual Reality
    • Usability Engineering

    Background:

    • Virtual steering techniques allow navigation in large virtual environments (VEs) beyond physical workspace limits.
    • Users may exhibit Unintended Positional Drift (UPD) during virtual steering, posing safety risks by approaching physical boundaries.

    Purpose of the Study:

    • To analyze and model Unintended Positional Drift (UPD) during virtual navigation tasks.
    • To provide a foundational understanding for designing safer virtual steering techniques.

    Main Methods:

    • Collected positional and orientation data from 18 users performing a virtual slalom task with torso-steering.
    • Developed two UPD models: linear regression and Gaussian Mixture Model (GMM).
    • Evaluated models using simulation-based assessments with virtual agents.

    Main Results:

    • Characterized UPD patterns during virtual steering across different trajectory curvatures.
    • Demonstrated the feasibility of simulation-based evaluations for studying UPD.
    • Identified distinct UPD behaviors influenced by virtual environment navigation.

    Conclusions:

    • The study successfully models Unintended Positional Drift (UPD) in virtual steering.
    • Simulation-based evaluations are effective for analyzing UPD.
    • Findings can inform the design of virtual reality navigation systems to mitigate UPD and enhance user safety.