Lower-limb kinematics and kinetics during continuously varying human locomotion

Emma Reznick, Kyle R. Embry, Ross Neuman, Edgar Bolívar-Nieto, Nicholas P. Fey, Robert D. Gregg*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

72 Scopus citations

Abstract

Human locomotion involves continuously variable activities including walking, running, and stair climbing over a range of speeds and inclinations as well as sit-stand, walk-run, and walk-stairs transitions. Understanding the kinematics and kinetics of the lower limbs during continuously varying locomotion is fundamental to developing robotic prostheses and exoskeletons that assist in community ambulation. However, available datasets on human locomotion neglect transitions between activities and/or continuous variations in speed and inclination during these activities. This data paper reports a new dataset that includes the lower-limb kinematics and kinetics of ten able-bodied participants walking at multiple inclines (±0°; 5° and 10°) and speeds (0.8 m/s; 1 m/s; 1.2 m/s), running at multiple speeds (1.8 m/s; 2 m/s; 2.2 m/s and 2.4 m/s), walking and running with constant acceleration (±0.2; 0.5), and stair ascent/descent with multiple stair inclines (20°; 25°; 30° and 35°). This dataset also includes sit-stand transitions, walk-run transitions, and walk-stairs transitions. Data were recorded by a Vicon motion capture system and, for applicable tasks, a Bertec instrumented treadmill.

Original languageEnglish (US)
Article number282
JournalScientific Data
Volume8
Issue number1
DOIs
StatePublished - Dec 2021

Funding

This work was supported by the National Institute of Child Health & Human Development of the NIH under Award Number R01HD094772. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Robert D. Gregg, IV, Ph.D., holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund. The authors would like to thank Lizbeth Zamora for her help with data acquisition and data post-processing, Rebecca Macaluso for her help adapting the treadmill remote control, and Shihao Cheng, Vamsi Peddinti, Erica Santos, Kevin Best, and Nikhil Divekar for their help debugging the dataset.

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

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