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

Verheul, J, Warmenhoven, J, Lisboa, P, Gregson, W, Vanrenterghem, J and Robinson, MA (2019) Identifying generalised segmental acceleration patterns that contribute to ground reaction force features across different running tasks. Journal of Science and Medicine in Sport. ISSN 1878-1861

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Abstract

Objective: 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. Design: Exploratory experimental design. Methods: A multivariate principal component analysis (PCA) was applied to a combination of segmental acceleration data from all body segments for fifteen 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. Results: 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. Conclusions: 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.

Item Type: Article
Uncontrolled Keywords: 1106 Human Movement and Sports Sciences, 1117 Public Health and Health Services
Subjects: R Medicine > RC Internal medicine > RC1200 Sports Medicine
Divisions: Applied Mathematics
Sports & Exercise Sciences
Publisher: Sports Medicine Australia. Elsevier
Date Deposited: 17 Jul 2019 10:35
Last Modified: 31 Jul 2019 10:00
DOI or Identification number: 10.1016/j.jsams.2019.07.006
URI: http://researchonline.ljmu.ac.uk/id/eprint/11063

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