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<p>Clinical use of Virtual Reality (VR)-based treadmill training to improve post-stroke gait is limited by a lack of adherence to motor learning and control principles, and user-centred design consideration. This study aims to outline the development and evaluation of a VR-based treadmill training system for post-stroke patients, aligning with currently accepted rehabilitation concepts and principles. The proposed system involves several modules and algorithms (e.g., biomechanical model, feedback controller, adaptive difficulty controller) along with portable motion capture technology to provide individualised rehabilitation focused training. Eight physiotherapists and eleven post-stroke participants were recruited to evaluate the system’s acceptability, usability, and adherence to motor learning and control principles. The physiotherapists reported high ratings in terms of the design’s adherence to motor learning and control principles, and both physiotherapists and post-stroke patients reported positive intention to use the system. The proposed system promotes individualised and progressive practice and feedback for post-stroke gait training in VR environments. Balancing technical complexity, practical considerations, and adherence to motor learning and control principles in this system can enhance the application of VR interventions for post-stroke patients in both clinical and remote rehabilitation settings.</p>

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A clinically oriented VR-based treadmill training system for post-stroke rehabilitation: integration of motor learning and control principles

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月16日

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  • Yiran Jiao,
  • Peter Fermin Dajime,
  • Qi Zhang,
  • Stacey Reading,
  • Marie-Claire Smith,
  • Yanxin Zhang

摘要

Clinical use of Virtual Reality (VR)-based treadmill training to improve post-stroke gait is limited by a lack of adherence to motor learning and control principles, and user-centred design consideration. This study aims to outline the development and evaluation of a VR-based treadmill training system for post-stroke patients, aligning with currently accepted rehabilitation concepts and principles. The proposed system involves several modules and algorithms (e.g., biomechanical model, feedback controller, adaptive difficulty controller) along with portable motion capture technology to provide individualised rehabilitation focused training. Eight physiotherapists and eleven post-stroke participants were recruited to evaluate the system’s acceptability, usability, and adherence to motor learning and control principles. The physiotherapists reported high ratings in terms of the design’s adherence to motor learning and control principles, and both physiotherapists and post-stroke patients reported positive intention to use the system. The proposed system promotes individualised and progressive practice and feedback for post-stroke gait training in VR environments. Balancing technical complexity, practical considerations, and adherence to motor learning and control principles in this system can enhance the application of VR interventions for post-stroke patients in both clinical and remote rehabilitation settings.