Virtual Reality-Integrated Propulsion Feedback for Stroke Rehab
Purpose
This study evaluates a novel Virtual Reality (VR)-integrated visual feedback system designed to enhance limb propulsion during robot-assisted gait rehabilitation in individuals post-stroke. In collaboration with CUREXO, a rehabilitation robotics company, the system is embedded within the Morning Walk® end-effector robot and provides real-time visual feedback to facilitate symmetrical use of the paretic and non-paretic limbs. The goal is to address gait asymmetry commonly observed in hemiparetic stroke survivors by promoting improved paretic leg propulsion, which is a key contributor to forward movement during walking. A total of 30 participants (15 stroke, 15 healthy controls) aged 20 years or older will undergo single-session gait training using the VR-robot system. Participants will be assessed using spatiotemporal gait parameters, muscle activity, foot pressure, and vertical ground reaction forces. Additional safety measures-including a saddle-type weight support and real-time heart rate monitoring via smartwatch-are implemented to ensure a safe and controlled training environment. This study aims to test the feasibility and effectiveness of this VR-based system in improving gait symmetry and functional walking capacity in people recovering from stroke.
Conditions
- Stroke
- Robot Assisted Gait Training
- Virtual Reality
Eligibility
- Eligible Ages
- Over 20 Years
- Eligible Sex
- All
- Accepts Healthy Volunteers
- Yes
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).
Study Design
- Phase
- N/A
- Study Type
- Interventional
- Allocation
- Non-Randomized
- Intervention Model
- Parallel Assignment
- Intervention Model Description
- This is a mixed design study with two distinct populations: individuals post-stroke and healthy controls. All participants undergo a single session of VR-based gait training with an end-effector robot. Pre- and post-intervention measures are collected to evaluate within-subject changes and between-group differences.
- Primary Purpose
- Basic Science
- Masking
- None (Open Label)
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
|
Experimental Post-Stroke Group |
Individuals who have had a stroke will complete a single session of VR-integrated gait training using the Morning Walk® end-effector robot. Gait performance will be evaluated before and after training. |
|
|
Experimental Healthy Group |
Healthy participants will undergo the same single-session VR-integrated gait training using the Morning Walk® end-effector robot. Pre- and post-training assessments will be conducted. |
|
Recruiting Locations
Galveston, Texas 77555
Mansoo Ko
409-747-1617
More Details
- Status
- Recruiting
- Sponsor
- The University of Texas Medical Branch, Galveston
Detailed Description
Introduction and Purpose: People with hemiparetic stroke often exhibit gait asymmetry due to reduced propulsion from the paretic leg. This contributes to overreliance on the non-paretic leg and leads to inefficient, energy-consuming walking patterns. Traditional rehabilitation, including robot-assisted gait training, typically emphasizes repetitive motion but lacks a specific focus on propulsion, limiting its potential to promote neuroplasticity and symmetrical gait. To address this limitation, the research team has developed a Virtual Reality (VR)-based visual feedback system that delivers real-time, individualized cues aimed at improving paretic limb propulsion. This system is integrated into the Morning Walk® end-effector rehabilitation robot developed by CUREXO. By encouraging active use of the paretic limb, the intervention is designed to reduce compensatory movement strategies and improve gait symmetry. Study Objectives: The primary objective is to evaluate the feasibility and effectiveness of this VR-enhanced limb propulsion training system. The study compares spatiotemporal gait parameters between individuals post-stroke and healthy controls, with the goal of determining whether real-time visual feedback can improve bilateral coordination and reduce asymmetry. Participants: A total of 30 participants (15 post-stroke, 15 healthy) aged 20 years or older will be recruited. Stroke participants must have experienced a stroke at least one month prior to enrollment and be able to walk at least 10 meters with or without assistive devices. Healthy controls must walk independently without assistance. Methods: Each participant will complete a single-session gait training protocol with pre- and post-assessments. Equipment used includes: Zeno Walkway: Overground gait mat to assess spatiotemporal gait parameters before and after training. Morning Walk® Robot: End-effector rehabilitation device with integrated VR propulsion visual feedback system. Delsys EMG Sensors: For analysis of bilateral lower extremity muscle activity. Tekscan In-Shoe Sensors: To measure ground reaction forces and foot pressure during walking. Smartwatch Monitoring: To track heart rate during training as an indicator of exertion (data not stored or transmitted). Procedures: Baseline Assessments: Collection of demographic data, health history, physical function, height, and weight. Pre-Training Gait Assessment: Overground walking trials using the Zeno Walkway. VR Robot Gait Training: Participants walk with the Morning Walk® robot while receiving propulsion-related visual feedback in VR. Post-Training Gait Assessment: Re-evaluation using the Zeno Walkway to assess changes in gait performance. Data Collection: Real-time biomechanical data (spatiotemporal parameters, EMG, and foot pressure/GRFs) will be collected and analyzed. Smartwatch data will only be viewed during the session and will not be stored. Risks and Safety: Risks are minimal. The Morning Walk® robot features a saddle-type support system and protective surrounds to prevent falls. Minor skin irritation may occur from EMG electrodes. All data will be stored securely on password-protected devices. Significance: This study is the first to integrate a VR-based propulsion feedback system into an end-effector gait training robot. It is expected to enhance paretic limb engagement, promote symmetrical gait patterns, and support motor learning through individualized feedback and neuroplastic adaptation.