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This paper presents an innovative augmented reality system designed to support lower limb rehabilitation for neurological patients, such as stroke survivors. Using machine learning driven pose estimation algorithms and Unity-based software, the system enables patients to perform exercises autonomously without the need for external sensors or constant supervision. Relying solely on RGB camera data from AR glasses, the system offers interactive and engaging rehabilitation sessions that can be performed at home. Experimental results demonstrate feasibility of this cost-effective solution.

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Machine Learning-Enhanced AR for Independent Lower Limb Rehabilitation

  • 会议论文

  • 发表日期：

    2026年

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  • Andrzej Ziemnicki,
  • Norbert Mostowski,
  • Piotr Skrzypczyński

摘要

This paper presents an innovative augmented reality system designed to support lower limb rehabilitation for neurological patients, such as stroke survivors. Using machine learning driven pose estimation algorithms and Unity-based software, the system enables patients to perform exercises autonomously without the need for external sensors or constant supervision. Relying solely on RGB camera data from AR glasses, the system offers interactive and engaging rehabilitation sessions that can be performed at home. Experimental results demonstrate feasibility of this cost-effective solution.