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Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach

BACKGROUND: Resilience of midsole material and the upper structure of the shoe are conceptual characteristics that can interfere in running biomechanics patterns. Artificial intelligence techniques can capture features from the entire waveform, adding new perspective for biomechanical analysis. This...

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Autores principales: Onodera, Andrea N., Gavião Neto, Wilson P., Roveri, Maria Isabel, Oliveira, Wagner R., Sacco, Isabel CN
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5333543/
https://www.ncbi.nlm.nih.gov/pubmed/28265506
http://dx.doi.org/10.7717/peerj.3026
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author Onodera, Andrea N.
Gavião Neto, Wilson P.
Roveri, Maria Isabel
Oliveira, Wagner R.
Sacco, Isabel CN
author_facet Onodera, Andrea N.
Gavião Neto, Wilson P.
Roveri, Maria Isabel
Oliveira, Wagner R.
Sacco, Isabel CN
author_sort Onodera, Andrea N.
collection PubMed
description BACKGROUND: Resilience of midsole material and the upper structure of the shoe are conceptual characteristics that can interfere in running biomechanics patterns. Artificial intelligence techniques can capture features from the entire waveform, adding new perspective for biomechanical analysis. This study tested the influence of shoe midsole resilience and upper structure on running kinematics and kinetics of non-professional runners by using feature selection, information gain, and artificial neural network analysis. METHODS: Twenty-seven experienced male runners (63 ± 44 km/week run) ran in four-shoe design that combined two resilience-cushioning materials (low and high) and two uppers (minimalist and structured). Kinematic data was acquired by six infrared cameras at 300 Hz, and ground reaction forces were acquired by two force plates at 1,200 Hz. We conducted a Machine Learning analysis to identify features from the complete kinematic and kinetic time series and from 42 discrete variables that had better discriminate the four shoes studied. For that analysis, we built an input data matrix of dimensions 1,080 (10 trials × 4 shoes × 27 subjects) × 1,254 (3 joints × 3 planes of movement × 101 data points + 3 vectors forces × 101 data points + 42 discrete calculated kinetic and kinematic features). RESULTS: The applied feature selection by information gain and artificial neural networks successfully differentiated the two resilience materials using 200(16%) biomechanical variables with an accuracy of 84.8% by detecting alterations of running biomechanics, and the two upper structures with an accuracy of 93.9%. DISCUSSION: The discrimination of midsole resilience resulted in lower accuracy levels than did the discrimination of the shoe uppers. In both cases, the ground reaction forces were among the 25 most relevant features. The resilience of the cushioning material caused significant effects on initial heel impact, while the effects of different uppers were distributed along the stance phase of running. Biomechanical changes due to shoe midsole resilience seemed to be subject-dependent, while those due to upper structure seemed to be subject-independent.
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spelling pubmed-53335432017-03-06 Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach Onodera, Andrea N. Gavião Neto, Wilson P. Roveri, Maria Isabel Oliveira, Wagner R. Sacco, Isabel CN PeerJ Bioengineering BACKGROUND: Resilience of midsole material and the upper structure of the shoe are conceptual characteristics that can interfere in running biomechanics patterns. Artificial intelligence techniques can capture features from the entire waveform, adding new perspective for biomechanical analysis. This study tested the influence of shoe midsole resilience and upper structure on running kinematics and kinetics of non-professional runners by using feature selection, information gain, and artificial neural network analysis. METHODS: Twenty-seven experienced male runners (63 ± 44 km/week run) ran in four-shoe design that combined two resilience-cushioning materials (low and high) and two uppers (minimalist and structured). Kinematic data was acquired by six infrared cameras at 300 Hz, and ground reaction forces were acquired by two force plates at 1,200 Hz. We conducted a Machine Learning analysis to identify features from the complete kinematic and kinetic time series and from 42 discrete variables that had better discriminate the four shoes studied. For that analysis, we built an input data matrix of dimensions 1,080 (10 trials × 4 shoes × 27 subjects) × 1,254 (3 joints × 3 planes of movement × 101 data points + 3 vectors forces × 101 data points + 42 discrete calculated kinetic and kinematic features). RESULTS: The applied feature selection by information gain and artificial neural networks successfully differentiated the two resilience materials using 200(16%) biomechanical variables with an accuracy of 84.8% by detecting alterations of running biomechanics, and the two upper structures with an accuracy of 93.9%. DISCUSSION: The discrimination of midsole resilience resulted in lower accuracy levels than did the discrimination of the shoe uppers. In both cases, the ground reaction forces were among the 25 most relevant features. The resilience of the cushioning material caused significant effects on initial heel impact, while the effects of different uppers were distributed along the stance phase of running. Biomechanical changes due to shoe midsole resilience seemed to be subject-dependent, while those due to upper structure seemed to be subject-independent. PeerJ Inc. 2017-02-28 /pmc/articles/PMC5333543/ /pubmed/28265506 http://dx.doi.org/10.7717/peerj.3026 Text en ©2017 Onodera et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , 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) and either DOI or URL of the article must be cited.
spellingShingle Bioengineering
Onodera, Andrea N.
Gavião Neto, Wilson P.
Roveri, Maria Isabel
Oliveira, Wagner R.
Sacco, Isabel CN
Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach
title Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach
title_full Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach
title_fullStr Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach
title_full_unstemmed Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach
title_short Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach
title_sort immediate effects of eva midsole resilience and upper shoe structure on running biomechanics: a machine learning approach
topic Bioengineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5333543/
https://www.ncbi.nlm.nih.gov/pubmed/28265506
http://dx.doi.org/10.7717/peerj.3026
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