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PeerJ. 2017 Feb 28;5:e3026. doi: 10.7717/peerj.3026. eCollection 2017.

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

Author information

Physical Therapy, Speech and Occupational Therapy Department, University of São Paulo, School of Medicine, São Paulo, Brazil.
Dass Nordeste Calçados e Artigos Esportivos Inc, Ivoti, Rio Grande do Sul, Brazil.
School of Engeneering & IT, Centro Universitário Ritter dos Reis, Porto Alegre, Rio Grande do Sul, Brazil.
Contributed equally



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.


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).


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%.


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.


Biomechanics; Kinematics; Kinetics; Machine learning; Neural networks; Running; Shoes

Conflict of interest statement

The research has been co-funded by Brazilian Agency for Research funding (CAPES) and Dass Nordeste Calçados e Artigos Esportivos S/A. Wagner Oliveira works in Dass Nordeste Calçados e Artigos Esportivos S/A. Andrea N Onodera works in Dass Nordeste Calçados e Artigos Esportivos S/A and conducts her PhD supervised by Dr Isabel CN Sacco. Mrs Maria Isabel Roveri is part of the primary authors PhD, but is not associated to Dass Nordeste Calçados e Artigos Esportivos S/A and is funded by a CAPES scholarship. Data was collected, analyzed and the paper written with no influence from the funding agencies or shoe company, and no author will receive anything of value from the commercial products included in this paper. The authors, therefore, have no competing interests.

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