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The development of e-rehabilitation systems relies heavily on accurate biomechanical movement analysis. Telerehabilitation has emerged as a viable solution, but challenges persist in remotely assessing patients. Intelligent motion analysis using deep learning algorithms shows promise, yet dataset acquisition remains challenging. This paper proposes a dataset of biomechanical movements and an open catalog regarding motor rehabilitation. The dataset includes recordings from Kinect V1, Kinect V2, Astra, RealSense, and Qualisys devices and an invariance dataset with an RGB camera, covering various movements defined by the International Society of Biomechanics. The open catalog’s interface facilitates access to the dataset, allowing users to filter and preview videos before downloading them. Future contributions from the community are encouraged to enhance the dataset’s diversity and usability.

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A Biomechanical Movement Analysis Dataset and Development of an Open Catalog for Electronic Rehabilitation

  • 会议论文

  • 发表日期：

    2025年

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  • E. V. S. Siqueira,
  • L. S. Amorim,
  • T. B. G. Lafayette,
  • J. M. Teixeira,
  • P. J. L. da Silva,
  • F. L. S. Nunes,
  • F. F. F. Peres,
  • V. Teichrieb,
  • A. E. F. da Gama

摘要

The development of e-rehabilitation systems relies heavily on accurate biomechanical movement analysis. Telerehabilitation has emerged as a viable solution, but challenges persist in remotely assessing patients. Intelligent motion analysis using deep learning algorithms shows promise, yet dataset acquisition remains challenging. This paper proposes a dataset of biomechanical movements and an open catalog regarding motor rehabilitation. The dataset includes recordings from Kinect V1, Kinect V2, Astra, RealSense, and Qualisys devices and an invariance dataset with an RGB camera, covering various movements defined by the International Society of Biomechanics. The open catalog’s interface facilitates access to the dataset, allowing users to filter and preview videos before downloading them. Future contributions from the community are encouraged to enhance the dataset’s diversity and usability.