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.