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A Pressure Plate-Based Method for the Automatic Assessment of Foot Strike Patterns During Running

Overview of attention for article published in Annals of Biomedical Engineering, October 2015
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Title
A Pressure Plate-Based Method for the Automatic Assessment of Foot Strike Patterns During Running
Published in
Annals of Biomedical Engineering, October 2015
DOI 10.1007/s10439-015-1484-3
Pubmed ID
Authors

Alessandro Santuz, Antonis Ekizos, Adamantios Arampatzis

Abstract

The foot strike pattern (FSP, description of how the foot touches the ground at impact) is recognized to be a predictor of both performance and injury risk. The objective of the current investigation was to validate an original foot strike pattern assessment technique based on the numerical analysis of foot pressure distribution. We analyzed the strike patterns during running of 145 healthy men and women (85 male, 60 female). The participants ran on a treadmill with integrated pressure plate at three different speeds: preferred (shod and barefoot 2.8 ± 0.4 m/s), faster (shod 3.5 ± 0.6 m/s) and slower (shod 2.3 ± 0.3 m/s). A custom-designed algorithm allowed the automatic footprint recognition and FSP evaluation. Incomplete footprints were simultaneously identified and corrected from the software itself. The widely used technique of analyzing high-speed video recordings was checked for its reliability and has been used to validate the numerical technique. The automatic numerical approach showed a good conformity with the reference video-based technique (ICC = 0.93, p < 0.01). The great improvement in data throughput and the increased completeness of results allow the use of this software as a powerful feedback tool in a simple experimental setup.

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Geographical breakdown

Country Count As %
Germany 1 1%
Unknown 89 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 17 19%
Student > Ph. D. Student 15 17%
Student > Bachelor 11 12%
Researcher 8 9%
Other 8 9%
Other 10 11%
Unknown 21 23%
Readers by discipline Count As %
Sports and Recreations 23 26%
Medicine and Dentistry 13 14%
Engineering 9 10%
Nursing and Health Professions 6 7%
Biochemistry, Genetics and Molecular Biology 4 4%
Other 5 6%
Unknown 30 33%