A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults
Abstract 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...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04818-y |
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| author | Loreen Pogrzeba Evelyn Muschter Simon Hanisch Veronica Y. P. Wardhani Thorsten Strufe Frank H. P. Fitzek Shu-Chen Li |
| author_facet | Loreen Pogrzeba Evelyn Muschter Simon Hanisch Veronica Y. P. Wardhani Thorsten Strufe Frank H. P. Fitzek Shu-Chen Li |
| author_sort | Loreen Pogrzeba |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-2f7f968f8bfa44e0bedafbfc6d8d3d7b |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
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| series | Scientific Data |
| spelling | doaj-art-2f7f968f8bfa44e0bedafbfc6d8d3d7b2025-08-20T02:49:29ZengNature PortfolioScientific Data2052-44632025-03-0112112010.1038/s41597-025-04818-yA Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger AdultsLoreen Pogrzeba0Evelyn Muschter1Simon Hanisch2Veronica Y. P. Wardhani3Thorsten Strufe4Frank H. P. Fitzek5Shu-Chen Li6Technische Universität Dresden, Centre for Tactile Internet with Human-in-the-LoopTechnische Universität Dresden, Centre for Tactile Internet with Human-in-the-LoopTechnische Universität Dresden, Centre for Tactile Internet with Human-in-the-LoopTechnische Universität Dresden, Research Hub 6G-lifeTechnische Universität Dresden, Centre for Tactile Internet with Human-in-the-LoopTechnische Universität Dresden, Centre for Tactile Internet with Human-in-the-LoopTechnische Universität Dresden, Centre for Tactile Internet with Human-in-the-LoopAbstract 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.https://doi.org/10.1038/s41597-025-04818-y |
| spellingShingle | Loreen Pogrzeba Evelyn Muschter Simon Hanisch Veronica Y. P. Wardhani Thorsten Strufe Frank H. P. Fitzek Shu-Chen Li A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults Scientific Data |
| title | A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults |
| title_full | A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults |
| title_fullStr | A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults |
| title_full_unstemmed | A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults |
| title_short | A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults |
| title_sort | full body imu based motion dataset of daily tasks by older and younger adults |
| url | https://doi.org/10.1038/s41597-025-04818-y |
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