CeTI-Age-Kinematics: A Full-Body IMU-Based Motion Dataset of Daily Tasks in an Age-Comparative Sample
Catalog Summary
"CeTI-Age-Kinematics is a full-body IMU kinematics dataset of 30 daily tasks recorded with a 19-IMU sensor suit in an age-comparative sample of 32 participants (older adults and younger controls), intended for motion analysis and activity recognition research."
Catalog Notes
Overview
CeTI-Age-Kinematics is a full-body inertial motion dataset that records kinematics of common daily tasks using a wearable IMU sensor suit. The dataset is designed to help researchers study age-related differences in movement, build robust human activity recognition models, and develop digital biomarkers from inertial kinematics.
Participants and Tasks
- 32 participants split into two age groups (older adults and younger controls).
- 30 elemental daily tasks grouped into nine categories, including simulated interactions with imaginary objects.
- Repeated trials with well-documented task procedures and variations to support reproducibility.
Instrumentation
- Recorded using a sensor suit and gloves equipped with IMUs.
- The suit includes 19 pre-installed IMU sensors integrated into a textile suit to maintain stable sensor positioning during movements.
- Includes anthropometric body measurements and spatial measurements of experimental setups for contextual interpretation.
Use Cases
- Full-body kinematics modeling from IMU data.
- Human activity recognition (HAR) for daily tasks.
- Studying movement variability and age-related motor patterns.
Access and License
The dataset is described in an open-access publication; licensing and download links are provided by the publisher and any associated repository referenced by the article.
View Data Structure
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Pogrzeba, L., Muschter, E., Hanisch, S., Wardhani, V. Y. P., Strufe, T., Fitzek, F. H. P., & Li, S. C. (2025). A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults. Scientific Data, 12(1), 531. https://doi.org/10.1038/s41597-025-04818-y
Source metadata: Nature Publishing Group (2025) · DOI: 10.1038/s41597-025-04818-y
Indexed by IoTDataset.com on Feb 05, 2026
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