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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2017
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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. |
format | Online Article Text |
id | pubmed-5333543 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
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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