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Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
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Learning Speed-Adaptive Walking Agent Using Imitation Learning with Physics-Informed Simulation.

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    This study introduces a digital twin framework for human gait, creating a virtual agent that adapts to different walking speeds with biomechanically realistic movements. This advances virtual mobility studies and applications in rehabilitation and design.

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    Area of Science:

    • Biomechanics
    • Computer Science
    • Robotics

    Background:

    • Virtual models of human gait (digital twins) offer efficient mobility study methods.
    • Challenges include the sim-to-real gap and limited adaptability to diverse walking conditions.

    Purpose of the Study:

    • Develop and validate a framework for a skeletal humanoid agent.
    • Enable adaptation to varying walking speeds while maintaining biomechanically realistic motions.

    Main Methods:

    • Combined a synthetic data generator for plausible gait kinematics.
    • Utilized adversarial imitation learning to train the agent's walking policy.
    • Validated against open-source biomechanics data and ground-truth kinematics.

    Main Results:

    • The agent demonstrated adaptability to varying walking speeds.
    • Achieved a root mean square error of $5.24 \pm 0.09$ degrees compared to ground-truth kinematics.
    • Validated the framework's ability to generate biomechanically plausible motions.

    Conclusions:

    • Represents a significant step toward a digital twin of human locomotion.
    • Potential applications in biomechanics research, exoskeleton design, and rehabilitation.
    • The developed framework enhances the adaptability and realism of virtual gait models.