A Multimodal Dataset of Full-Body Kinematics and Kinetics from Laboratory- and Wearable-Based Systems
Chang June Lee, Jung Keun Lee · 2026
Read A Multimodal Dataset of Full-Body Kinematics and Kinetics from Laboratory- and Wearable-Based Systems by Chang June Lee, Jung Keun Lee (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13572550.txt.
Description
Chang June Lee and Jung Keun Lee released a dataset that bridges lab-grade and wearable movement measurement. Optical motion capture and instrumented treadmills are accurate but confined to laboratories, while wearable sensors work anywhere yet need validation against those gold standards. Their 2026 dataset synchronises four systems, optical capture, inertial capture, an instrumented treadmill, and pressure-sensing insoles, on the same movements.
Twelve healthy young adults performed postures, treadmill walking and running at varied speeds and inclines, squats, weight shifts, and jumps. Raw signals and model-derived biomechanics are included together. The authors present the resource for validating wearables, training machine-learning models, and studying gait and movement beyond simple level walking.
Catalog record
| CREATOR | Chang June Lee, Jung Keun Lee |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 57 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |