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.
