Robot-assisted Gait Rehabilitation using the Variable Stiffness Treadmill

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project

Project Goal

This project develops a novel post-stroke rehabilitation platform using the Variable Stiffness Treadmill (VST) to deliver targeted unilateral surface perturbations, evoking therapeutic motor responses in the impaired leg to increase step length and reduce drop-foot in mobility impaired individuals.

Key Contributions

  • Demonstration of Lasting Motor Aftereffects: Proved that robot-assisted surface stiffness perturbations induce persistent sensorimotor adaptations, facilitating neural plasticity and improving inter-leg coordination post-stroke.
  • Patient-Specific Rehabilitation Personalization: Established a framework for real-time, dynamic stiffness adjustment to tailor mechanical perturbations to an individual’s unique gait deficits and training needs.
  • Mechanistic Insights into Mechanical Interventions: Advanced fundamental understanding of how targeted physical/mechanical perturbations engage neural pathways to correct post-stroke motor impairments.
  • Translational Clinical Impact: Demonstrated a viable robotic intervention to improve functional mobility, step symmetry, and gait mechanics for hemiparetic patients.

Media & Experimental Demos

Experimental Setup Neural Control Interface

Selected Publications

(see Publications for a complete list)

  • The Variable Stiffness Treadmill (VST) 2: Development and Validation of a Unique Tool to Investigate Locomotion on Compliant Terrain
    Vaughn Chambers, Bradley Hobbs, William Gaither, Zachary The, Anthony Zhou, Chrysostomos Karakasis, Panagiotis Artemiadis
    J. Mechanisms Robotics, 2025.
    [PDF]

  • Using robot-assisted stiffness perturbations to evoke aftereffects useful to post-stroke gait rehabilitation
    Vaughn Chambers and Panagiotis Artemiadis
    Frontiers in Robotics and AI, 9, 2023. [PDF]

  • Variable Stiffness Treadmill (VST): System Development, Characterization and Preliminary Experiments
    Jeffrey Skidmore, Andrew Barkan and Panagiotis Artemiadis
    IEEE/ASME Transactions on Mechatronics, vol. 20, issue 4, pp. 1717-1724, 2015. [PDF]

Funding & Acknowledgments

This work has been supported by the following grants:

  • NSF: 2020009, 2015786, 2025797, 201890, 2415093
  • NIH: NIH 1R01HD111071-01
  • Any opinions, findings, and conclusions expressed are those of the authors.