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Updated: Jun 8, 2025

A Rehabilitation Program of Exoskeleton-assisted Body Weight-Supported Treadmill Training with Non-immersive Virtual Reality for Stroke Patients
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Immersive virtual reality for learning exoskeleton-like virtual walking: a feasibility study.

Antonio Rodríguez-Fernández1,2, Alex van den Berg3, Salvatore Luca Cucinella4,5

  • 1Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Center for Biomedical Engineering, Universitat Politècnica de Catalunya, Barcelona, 08028, Spain. antonio.rodriguez.fernandez@upc.edu.

Journal of Neuroengineering and Rehabilitation
|November 2, 2024
PubMed
Summary
This summary is machine-generated.

Virtual reality (VR) training for exoskeleton gait tasks requires tailoring perspective and visual feedback to individual sub-tasks for optimal motor learning. This approach enhances rehabilitation for individuals with sensorimotor disorders.

Keywords:
Augmented feedbackHead-mounted displayImmersive virtual realityLower-limbMotor learningPerson perspectiveSpinal cord injuryVirtual realityVisual feedbackWalkingWearable exoskeleton

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Area of Science:

  • Rehabilitation robotics
  • Motor learning
  • Virtual reality (VR) applications

Background:

  • Virtual Reality (VR) and Immersive Virtual Reality (IVR) are effective for motor (re)learning, with low-cost head-mounted displays (HMDs) making them viable rehabilitation tools.
  • Designing effective IVR environments for complex motor tasks, such as gait initiation with lower-limb exoskeletons, remains a challenge.
  • Complex gait tasks involve sub-tasks like weight shifting, trunk stabilization, and step initiation, requiring specific training strategies.

Purpose of the Study:

  • To investigate the optimal design elements within VR to enhance motor learning of complex virtual gait tasks.
  • To determine the effectiveness of different virtual reality (VR) perspectives and visual feedback for training exoskeleton-assisted walking.

Main Methods:

  • Developed an HMD-IVR system simulating virtual walking for controlling wearable lower-limb exoskeletons.
  • Conducted an experiment with 40 healthy participants comparing first-person (1PP) vs. third-person perspective (3PP) and concurrent visual feedback.
  • Assessed walking performance (steps, trunk inclination, stride length), embodiment, usability, cybersickness, and perceived workload.

Main Results:

  • All participants successfully learned the virtual walking task.
  • No single combination of perspective and feedback universally improved all sub-task learning; optimal training is sub-task specific.
  • High levels of avatar embodiment and low cybersickness were reported, but cognitive load remained high, impacting usability.

Conclusions:

  • Sequential training of sub-tasks, using tailored combinations of perspective and visual feedback, maximizes motor learning for complex gait tasks.
  • Findings provide crucial insights for developing advanced IVR systems to aid individuals with sensorimotor disorders in learning to walk with exoskeletons.
  • Optimizing VR design elements is key to improving the efficacy of exoskeleton-based rehabilitation.