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One-Legged Stand Test: Synchronized Motion Capture, Force Plate, and Radar Dataset for Fall-Risk.

Daniel Copeland1, Xiang Zhang1, Evan Linton2

  • 1Center for Clinical and Translational Research, MIT, Cambridge, MA, USA.

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|February 25, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new dataset combining motion capture, force plate, and radar data for fall risk assessment using the One-Legged Stand Test (OLST). The data aids research in biomechanics and digital biomarker development for improved fall risk characterization.

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Area of Science:

  • Biomechanics
  • Digital Health
  • Radar Signal Processing

Background:

  • The One-Legged Stand Test (OLST) is a clinically validated assessment for fall risk.
  • Synchronized, multi-modal data acquisition is crucial for detailed biomechanical analysis.

Purpose of the Study:

  • To present a novel, synchronized dataset for fall risk assessment research.
  • To support the development of digital biomarkers using advanced sensing technologies.
  • To facilitate research in biomechanics and radar signal processing.

Main Methods:

  • Collected synchronized motion capture, force plate, and 24 GHz FMCW radar data from 32 healthy participants (younger and older adults).
  • Participants performed the One-Legged Stand Test (OLST).
  • Labeled key events (foot-lift, stability phases, foot-touchdown) using motion capture and force plate data.

Main Results:

  • A comprehensive dataset featuring synchronized biomechanical and radar recordings is now available.
  • Labeled events provide precise temporal markers for stability analysis.
  • The dataset includes data from both young (≤32y) and old (≥64y) healthy individuals.

Conclusions:

  • This dataset provides a valuable resource for advancing fall risk characterization.
  • It enables novel research at the intersection of biomechanics, radar technology, and digital health.
  • Provided Python utilities facilitate data accessibility and analysis for researchers.