Virtual reality feedback to boost push-off from the weaker leg during robot-assisted walking after stroke
Virtual Reality-Integrated Limb Propulsion Visual Feedback System for End-Effector Robot-Assisted Stroke Rehabilitation
This project will test whether VR visual feedback during robot-assisted walking helps people who had a stroke increase push-off from their weaker leg.
Quick facts
| Phase | Not applicable |
|---|---|
| Study type | Interventional |
| Enrollment | 30 (estimated) |
| Ages | 20 Years and up |
| Sex | All |
| Sponsor | The University of Texas Medical Branch, Galveston Academic / other |
| Locations | 1 site (Galveston, Texas) |
| Trial ID | NCT07087743 on ClinicalTrials.gov |
What this trial studies
The team uses a VR-integrated visual feedback system embedded in the Morning Walk® end-effector robot to encourage more symmetrical use of the paretic and non-paretic limbs during robot-assisted walking. A total of 30 adults (15 post-stroke, 15 healthy controls) will complete a single-session gait training while investigators record spatiotemporal gait parameters, muscle activity, foot pressure, and vertical ground reaction forces. Stroke participants must be at least one month post-stroke and able to walk 10 meters with or without assistive devices, and the protocol includes safety measures such as a saddle-type weight support and real-time heart monitoring. Outcomes in the post-stroke group will be compared with healthy controls to see if real-time VR propulsion feedback improves paretic limb propulsion and gait symmetry.
Who should consider this trial
Good fit: Adults aged 20 years or older who had a stroke at least one month ago and can walk at least 10 meters with or without an assistive device are ideal candidates.
Not a fit: Patients with severe cognitive impairment or inability to follow commands, unstable medical or cardiac conditions, progressive neurological disease, lower limb amputation, severe musculoskeletal impairment, or uncontrolled diabetes/foot ulcers are unlikely to benefit or may be excluded.
Why it matters
Potential benefit: If successful, the approach could increase paretic leg propulsion and reduce gait asymmetry, potentially making walking more efficient and less effortful for people after stroke.
How similar studies have performed: Previous robot-assisted gait training and visual or biofeedback approaches have shown promising improvements in gait symmetry, but embedding VR-driven propulsion feedback in an end-effector robot is a relatively novel approach with limited prior data.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Adults aged 20 years or older. * For post-stroke participants: * Diagnosis of stroke at least 1 month prior to participation. * Able to walk at least 10 meters with or without assistive devices. For healthy participants: ° Must walk independently without assistive devices. Exclusion Criteria: * Individuals with a life expectancy of less than one year. * Comatose individuals. * Individuals unable to follow three-step commands. * Individuals with lower limb amputation. * Individuals with poorly controlled diabetes (e.g., foot ulceration). * Individuals with legal blindness. * Individuals with progressive neurological conditions. * Medically unstable individuals. * Individuals with significant musculoskeletal impairments. * Individuals with congestive heart failure or unstable angina. * Individuals with peripheral vascular disease. * Individuals with severe neuropsychiatric disorders (e.g., dementia, cognitive deficits, or severe depression).
Where this trial is running
Galveston, Texas
- University of Texas Medical Branch — Galveston, Texas, United States (Recruiting)
Study contacts
- Study coordinator: Mansoo Ko, PhD
- Email: mako@utmb.edu
- Phone: 409-747-1617
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.