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Classification of continuous vertical ground reaction forces

Richter, Chris orcid logoORCID: 0000-0001-6017-1520, Marshall, Brendan, O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 and Moran, Kieran orcid logoORCID: 0000-0003-2015-8967 (2014) Classification of continuous vertical ground reaction forces. Journal of Biomechanics, 47 (10). pp. 2385-2390. ISSN 0021-9290

Abstract
The aim of this study is to assess and compare the performance of com- monly used hierarchical, partitional (k-means) and Gaussian model-based (Expectation-Maximization algorithm) clustering techniques to appropriately identify subgroup patterns within vertical ground reaction force data, using a continuous waveform analysis. In addition, we also compared the perfor- mance across each technique using normalized and non-normalization input scores. Both generated and real data (one hundred-and twenty two verti- cal jumps) were analyzed. The performance of each cluster technique was measured by assessing the ability to explain variances in jump height using a stepwise regression analysis. Only k-means (normalized scores; 82 %) and hierarchical clustering (normalized scores; 85 %) were able to extend the ability to describe variances in jump height beyond that achieved using the group analysis (i.e. one cluster; 78 %). Further, our findings strongly indicate the need to normalize the input data (similarity measure) when clustering. In contrast to the group analysis, the subgroup analysis was able to iden- tify cluster specific phases of variance, which improved the ability to explain variances in jump height, due to the identification of cluster specific predictor variables. Our findings therefore highlight the benefit of performing a subgroup analysis and may explain, at least in part, the contrasting findings between previous studies that used a single group level of analysis.
Metadata
Item Type:Article (Published)
Refereed:Yes
Uncontrolled Keywords:Clustering; Vertical ground reaction force; Analysis of characterizing phases; Countermovement jump
Subjects:Engineering > Signal processing
Medical Sciences > Sports sciences
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Science and Health > School of Health and Human Performance
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Publisher:Elsevier
Official URL:http://dx.doi.org/10.1016/j.jbiomech.2014.04.032
Copyright Information:© 2014 Elsevier
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:SFI Grant Number 12/RC/2289
ID Code:20597
Deposited On:27 May 2015 13:06 by Kevin Fraser . Last Modified 19 Oct 2018 13:13
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