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Artificial neural networks for predicting dorsal pressures on the foot surface while walking

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Artificial neural networks for predicting dorsal pressures on the foot surface while walking

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dc.contributor.author Rupérez Moreno, María José es_ES
dc.contributor.author Martín-Guerrero, J.D. es_ES
dc.contributor.author Monserrat Aranda, Carlos es_ES
dc.contributor.author Alcañiz Raya, Mariano Luis es_ES
dc.date.accessioned 2014-01-09T08:20:48Z
dc.date.issued 2012-04
dc.identifier.issn 0957-4174
dc.identifier.uri http://hdl.handle.net/10251/34823
dc.description.abstract In this work, artificial neural networks (ANNs) are proposed to predict the dorsal pressure over the foot surface exerted by the shoe upper while walking. A model that is based on the multilayer perceptron (MLP) is used since it can provide a single equation to model the exerted pressure for all the materials used as shoe uppers. Five different models are produced, one model for each one of the four subjects under study and an overall model for the four subjects. The inputs to the neural model include the characteristics of the material and the positions during a whole step of 14 pressure sensors placed on the foot surface. The goal is to find models with good generalization capabilities, (i.e.; models that work appropriately not only for the cases used to train the model but also for new cases) in order to have a useful predictor in routine practice. New cases may involve either new materials for the same subject or even new subjects and new materials. To accomplish this goal, two thirds of the patterns are trained to obtain the model (training data set) and the remaining third is kept for validation purposes. The achieved accuracy was very satisfactory since correlation coefficients between the predicted output and the actual pressure in the validation data were higher than 0.95 for those models developed for individual subjects. For the much more challenging problem of an overall prediction for all the subjects, the correlation coefficient was close to 0.9 in the validation data set (i.e.; with data not previously seen by the model). © 2011 Elsevier Ltd. All rights reserved. es_ES
dc.description.sponsorship This work has been partially funded by the Spanish Ministry of Education and Science (reference CSD2007-00018). en_EN
dc.format.extent 9 es_ES
dc.language Español es_ES
dc.publisher Elsevier es_ES
dc.relation Spanish Ministry of Education and Science (reference CSD2007-00018) es_ES
dc.relation.ispartof Expert Systems with Applications es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Artificial neural networks es_ES
dc.subject Dorsal pressures es_ES
dc.subject Multilayer perceptron es_ES
dc.subject Shoe upper es_ES
dc.subject.classification EXPRESION GRAFICA EN LA INGENIERIA es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Artificial neural networks for predicting dorsal pressures on the foot surface while walking es_ES
dc.type Artículo es_ES
dc.embargo.lift 10000-01-01
dc.embargo.terms forever es_ES
dc.identifier.doi 10.1016/j.eswa.2011.11.050
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano - Institut Interuniversitari d'Investigació en Bioenginyeria i Tecnologia Orientada a l'Ésser Humà es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Gráfica - Departament d'Enginyeria Gràfica es_ES
dc.description.bibliographicCitation Rupérez Moreno, MJ.; Martín-Guerrero, J.; Monserrat Aranda, C.; Alcañiz Raya, ML. (2012). Artificial neural networks for predicting dorsal pressures on the foot surface while walking. Expert Systems with Applications. 39(5):5349-5357. doi:10.1016/j.eswa.2011.11.050 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.eswa.2011.11.050 es_ES
dc.description.upvformatpinicio 5349 es_ES
dc.description.upvformatpfin 5357 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 39 es_ES
dc.description.issue 5 es_ES
dc.relation.senia 224009


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