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<p>This dataset (named <i>CeTI-Age-Kinematics</i>) fills the gap in existing motion capture (MoCap) data by recording kinematics of full-body movements during daily tasks in an age-comparative sample with 32 participants in two groups: older adults (66–75 years) and younger adults (19–28 years). The data were recorded using sensor suits and gloves with inertial measurement units (IMUs). The dataset features 30 common elemental daily tasks that are grouped into nine categories, including simulated interactions with imaginary objects. Kinematic data were recorded under well-controlled conditions, with repetitions and well-documented task procedures and variations. It also entails anthropometric body measurements and spatial measurements of the experimental setups to enhance the interpretation of IMU MoCap data in relation to body characteristics and situational surroundings. This dataset can contribute to advancing machine learning, virtual reality, and medical applications by enabling detailed analyses and modeling of naturalistic motions and their variability across a wide age range. Such technologies are essential for developing adaptive systems for applications in tele-diagnostics, rehabilitation, and robotic motion planning that aim to serve broad populations.</p>

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A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月29日

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  • Loreen Pogrzeba,
  • Evelyn Muschter,
  • Simon Hanisch,
  • Veronica Y. P. Wardhani,
  • Thorsten Strufe,
  • Frank H. P. Fitzek,
  • Shu-Chen Li

摘要

This dataset (named CeTI-Age-Kinematics) fills the gap in existing motion capture (MoCap) data by recording kinematics of full-body movements during daily tasks in an age-comparative sample with 32 participants in two groups: older adults (66–75 years) and younger adults (19–28 years). The data were recorded using sensor suits and gloves with inertial measurement units (IMUs). The dataset features 30 common elemental daily tasks that are grouped into nine categories, including simulated interactions with imaginary objects. Kinematic data were recorded under well-controlled conditions, with repetitions and well-documented task procedures and variations. It also entails anthropometric body measurements and spatial measurements of the experimental setups to enhance the interpretation of IMU MoCap data in relation to body characteristics and situational surroundings. This dataset can contribute to advancing machine learning, virtual reality, and medical applications by enabling detailed analyses and modeling of naturalistic motions and their variability across a wide age range. Such technologies are essential for developing adaptive systems for applications in tele-diagnostics, rehabilitation, and robotic motion planning that aim to serve broad populations.