StrokeNCT07087743
Virtual Reality-Integrated Propulsion Feedback for Stroke Rehab
A study testing VR-Based Gait Training with End-Effector Robot in people with Stroke and Robot Assisted Gait Training, run by The University of Texas Medical Branch, Galveston at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 20 years and olderHealthy volunteers can take part
- Conditions the study is for
- Stroke, Robot Assisted Gait Training, Virtual Reality
- What is being tested?
- VR-Based Gait Training with End-Effector Robot
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 30 participants
- Where
- 1 siteUnited States
- Study dates
- 25 Jul 2025to 30 Dec 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
Show all 12Show fewer
- 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).
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
University of Texas Medical Branch
Galveston, Texas, 77555
Mansoo Ko, 409-747-1617
About this study
Official title: Virtual Reality-Integrated Limb Propulsion Visual Feedback System for End-Effector Robot-Assisted Stroke Rehabilitation
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.
Read the 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.
- Conditions
- Stroke, Robot Assisted Gait Training, Virtual Reality
- Being tested
- VR-Based Gait Training with End-Effector Robot
- Keywords
- Propulsion Feedback, Gait Rehabilitation, Paretic Leg, Virtual Reality, End-Effector Robot
- Medical subject headings
- Stroke
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
VR-Based Gait Training with End-Effector RobotBehavioural
A single-session gait training protocol using the Morning Walk® end-effector robot with real-time virtual reality visual feedback to encourage paretic limb propulsion and symmetrical gait. This intervention is intended to study participants' behavioral and biomechanical responses to the VR feedback, not to evaluate the robot as a device.
| Study group | Role | What they receive |
|---|---|---|
| Post-Stroke Group | Experimental | 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. Receives: Behavioral: VR-Based Gait Training with End-Effector Robot |
| Healthy Group | Experimental | 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. Receives: Behavioral: VR-Based Gait Training with End-Effector Robot |
What is being measured
The primary outcome is the main question the study is designed to answer.
Change in Spatiotemporal Gait Symmetry using the Zeno Walkway GaitMat
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.
Measured: Baseline (pre-training) and immediately post-training (same session)
Secondary outcomes (2)
Change in Peak Propulsive Force (Paretic vs. Non-Paretic Limb)
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
Measured: Baseline and immediately post-training (same session)
Change in Lower Extremity Muscle Activity
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.
Measured: Baseline and immediately post-training (same session)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Jul 2025
- Study start25 Jul 2025
- Primary completion27 Dec 2026 (estimated)
- Study completion30 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for StrokeStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Basic science
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- 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.
- Masking
- None (open label)
- Enrolment
- 30 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The University of Texas Medical Branch, Galveston
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07087743
- Sponsor's protocol ID
- 25-0065
- Status last verified
- Mar 2026
- First submitted
- 9 Jul 2025
- Last updated
- 1 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 1 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Virtual Reality-Integrated Limb Propulsion Visual Feedback System for End-Effector Robot-Assisted Stroke Rehabilitation
- ClinicalTrials.gov ID
- NCT07087743
- Acronym / sponsor ID
- 25-0065
- Conditions
- Stroke, Robot Assisted Gait Training, Virtual Reality
- Keywords
- Propulsion Feedback, Gait Rehabilitation, Paretic Leg, Virtual Reality, End-Effector Robot
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 30 participants (estimated)
- Lead sponsor
- The University of Texas Medical Branch, GalvestonUniversity, hospital or other
- Study start
- 25 Jul 2025 (actual)
- Primary completion
- 27 Dec 2026 (estimated)
- Study completion
- 30 Dec 2026 (estimated)
- First posted
- 28 Jul 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Basic science
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- 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.
| Arm | Type | Receives |
|---|---|---|
| Post-Stroke Group | Experimental | Behavioral: VR-Based Gait Training with End-Effector RobotIndividuals 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. |
| Healthy Group | Experimental | Behavioral: VR-Based Gait Training with End-Effector RobotHealthy 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. |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- 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.
- ° Must walk independently without assistive devices.
Key exclusion criteria (12 listed)
- 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).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| VR-Based Gait Training with End-Effector RobotA single-session gait training protocol using the Morning Walk® end-effector robot with real-time virtual reality visual feedback to encourage paretic limb propulsion and symmetrical gait. This intervention is intended to study participants' behavioral and biomechanical responses to the VR feedback, not to evaluate the robot as a device. | Behavioural | Healthy Group, Post-Stroke Group |
Primary outcome
Change in Spatiotemporal Gait Symmetry using the Zeno Walkway GaitMat
Time frame: 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.
2 secondary outcomes
Change in Peak Propulsive Force (Paretic vs. Non-Paretic Limb)
Time frame: Baseline and immediately post-training (same session)
Change in Lower Extremity Muscle Activity
Time frame: Baseline and immediately post-training (same session)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 1 Apr 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing VR-Based Gait Training with End-Effector Robot in people with Stroke and Robot Assisted Gait Training, run by The University of Texas Medical Branch, Galveston at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 12 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 1 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Mansoo Ko, PhD (409-747-1617, mako@utmb.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Change in Spatiotemporal Gait Symmetry using the Zeno Walkway GaitMat (time frame: Baseline (pre-training) and immediately post-training (same session)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 1 Apr 2026.