A public dataset of running biomechanics and the effects of running speed on lower extremity kinematics and kinetics
- Published
- Accepted
- Subject Areas
- Bioengineering, Neuroscience, Kinesiology, Computational Science
- Keywords
- Sports, Physical activity, Locomotion, Gait, Biomechanics
- Copyright
- © 2017 Fukuchi et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. A public dataset of running biomechanics and the effects of running speed on lower extremity kinematics and kinetics. PeerJ Preprints 5:e2888v1 https://doi.org/10.7287/peerj.preprints.2888v1
Abstract
Background. The goals of this study were (1) to present the set of data evaluating running biomechanics (kinematics and kinetics), including data on running habits, demographics, and levels of muscle strength and flexibility made available at Figshare (DOI: 10.6084/m9.figshare.4543435); and (2) to examine the effect of running speed on selected gait-biomechanics variables related to both running injuries and running economy. Methods. The lower-extremity kinematics and kinetics data of 28 regular runners were collected using a three-dimensional (3D) motion-capture system and an instrumented treadmill while the subjects ran at 2.5 m/s, 3.5 m/s, and 4.5 m/s wearing standard neutral shoes. Results. A dataset comprising raw and processed kinematics and kinetics signals pertaining to this experiment is available in various file formats. In addition, a file of metadata, including demographics, running characteristics, foot-strike patterns, and muscle strength and flexibility measurements is provided. Overall, there was an effect of running speed on most of the gait-biomechanics variables selected for this study. However, the foot-strike patterns were not affected by running speed. Discussion. Several applications of this dataset can be anticipated, including testing new methods of data reduction and variable selection; for educational purposes; and answering specific research questions. This last application was exemplified in the study’s second objective.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Matlab script
This supplemental material presents a script that exemplifies the basic data analysis steps taken to calculate the discrete variables presented in the companion manuscript.