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Identifying generalised segmental acceleration patterns that contribute to ground reaction force features across different running tasks.

Authors
  • Verheul, Jasper1
  • Warmenhoven, John2
  • Lisboa, Paulo3
  • Gregson, Warren4
  • Vanrenterghem, Jos5
  • Robinson, Mark A4
  • 1 Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom. Electronic address: [email protected] , (United Kingdom)
  • 2 Department of Exercise and Sports Science, The University of Sydney, Lidcombe, Australia; Performance People & Teams, Australian Institute of Sport, Canberra, Australia. , (Australia)
  • 3 Department of Applied Mathematics, Liverpool John Moores University, Liverpool, United Kingdom. , (United Kingdom)
  • 4 Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom. , (United Kingdom)
  • 5 Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium. , (Belgium)
Type
Published Article
Journal
Journal of science and medicine in sport
Publication Date
Dec 01, 2019
Volume
22
Issue
12
Pages
1355–1360
Identifiers
DOI: 10.1016/j.jsams.2019.07.006
PMID: 31445948
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

To support future developments of field-based biomechanical load monitoring tools, this study aimed to identify generalised segmental acceleration patterns and their contribution to ground reaction forces (GRFs) across different running tasks. Exploratory experimental design. A multivariate principal component analysis (PCA) was applied to a combination of segmental acceleration data from all body segments for 15 team-sport athletes performing accelerated, decelerated and constant low-, moderate- and high-speed running, and 90° cutting trials. Segmental acceleration profiles were then reconstructed from each principal component (PC) and used to calculate their specific GRF contributions. The first PC explained 48.57% of the acceleration variability for all body segments and was primarily related to the between-task differences in the overall magnitude of the GRF impulse. Magnitude and timing of high-frequency acceleration and GRF features (i.e. impact related characteristics) were primarily explained by the second PC (12.43%) and also revealed important between-task differences. The most important GRF characteristics were explained by the first five PCs, while PCs beyond that primarily contained small contributions to the overall GRF impulse. These findings show that a multivariate PCA approach can reveal generalised acceleration patterns and specific segmental contributions to GRF features, but their relative importance for different running activities are task dependent. Using segmental acceleration to assess whole-body biomechanical loading generically across various movements may thus require task identification algorithms and/or advanced sensor or data fusion approaches. Copyright © 2019 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

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