Virtual Reality-Integrated Propulsion Feedback for Stroke Rehab
- Conditions
- StrokeRobot Assisted Gait TrainingVirtual Reality
- Registration Number
- NCT07087743
- Brief Summary
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.
- 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.
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 30
- 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.
- 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
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Primary Outcome Measures
Name Time Method Change in Spatiotemporal Gait Symmetry using the Zeno Walkway GaitMat Baseline (pre-training) and immediately post-training (same session) Spatiotemporal gait parameters will be collected using the Zeno Walkway GaitMat before and after a single session of VR-based gait training. Gait spatiotemporal parameters will be calculated from the ratio of step lengths (paretic/non-paretic in stroke group; left/right in healthy group). The step length will be compared pre- and post-training.
- Secondary Outcome Measures
Name Time Method Change in Peak Propulsive Force (Paretic vs. Non-Paretic Limb) Baseline and immediately post-training (same session) Ground reaction forces and foot pressure data will be collected using the Tekscan In-Shoe system during walking trials. Ground reaction force from each foot will be compared pre- and post-training
Change in Lower Extremity Muscle Activity Baseline and immediately post-training (same session) Bilateral lower extremity muscle activity (e.g., tibialis anterior, soleus) will be recorded using Delsys wireless EMG sensors. EMG data during walking will be analyzed to detect neuromuscular adaptation after VR-based gait training.
Trial Locations
- Locations (1)
University of Texas Medical Branch
🇺🇸Galveston, Texas, United States
University of Texas Medical Branch🇺🇸Galveston, Texas, United StatesMansoo KoContact409-747-1617