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<p>Three-dimensional motion capture is a powerful tool in clinical and engineering applications, but it can be time consuming, expensive, and difficult to access patient populations. Here, we present an open-access dataset comprising 3D motion capture and ground reaction force data from 137 post-operative total hip replacement patients performing eight activities of daily living (ADLs): normal walking, fast walking, stair ascent, stair descent, sit-to-stand, stand-to-sit, lunge, and squat. Data were collected using a 10-camera Vicon system and two AMTI force plates, and are provided in C3D and txt file format, compatible with inverse dynamic analysis platforms (e.g Visual 3D, MOKKA) and musculoskeletal modelling platforms (e.g. OpenSim, AnyBody). Each file includes synchronised marker trajectories, analog force data, and participant metadata (e.g. age, sex, BMI, operated limb, time since surgery). This dataset offers a unique opportunity to study functional biomechanics across a range of ADLs, enabling applications in musculoskeletal modelling, rehabilitation assessment, and the development of machine learning algorithms.</p>

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Motion capture dataset of 137 post-operative total hip replacement patients performing activities of daily living

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月6日

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  • David E. Lunn,
  • Enrico De Pieri,
  • Graham J. Chapman,
  • Morten E. Lund,
  • Stephen J. Ferguson,
  • Anthony C. Redmond

摘要

Three-dimensional motion capture is a powerful tool in clinical and engineering applications, but it can be time consuming, expensive, and difficult to access patient populations. Here, we present an open-access dataset comprising 3D motion capture and ground reaction force data from 137 post-operative total hip replacement patients performing eight activities of daily living (ADLs): normal walking, fast walking, stair ascent, stair descent, sit-to-stand, stand-to-sit, lunge, and squat. Data were collected using a 10-camera Vicon system and two AMTI force plates, and are provided in C3D and txt file format, compatible with inverse dynamic analysis platforms (e.g Visual 3D, MOKKA) and musculoskeletal modelling platforms (e.g. OpenSim, AnyBody). Each file includes synchronised marker trajectories, analog force data, and participant metadata (e.g. age, sex, BMI, operated limb, time since surgery). This dataset offers a unique opportunity to study functional biomechanics across a range of ADLs, enabling applications in musculoskeletal modelling, rehabilitation assessment, and the development of machine learning algorithms.