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Unsupervised and scalable low train pathology detection system based on neural networks

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dc.affiliation.dptoUC3M. Departamento de Tecnología Electrónicaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Universitario de Tecnologías de Identificación (GUTI)es
dc.contributor.authorSánchez Casanova, Jorge
dc.contributor.authorLiu Jiménez, Judith
dc.contributor.authorTirado Martín, Paloma
dc.contributor.authorSanchez-Reillo, Raul
dc.date.accessioned2021-09-23T13:44:12Z
dc.date.available2021-09-23T13:44:12Z
dc.date.issued2021-02-01
dc.description.abstractCurrently, there exist different technologies applied in the world of medicine dedicated to the detection of health problems such as cancer, heart diseases, etc. However, these technologies are not applied to the detection of lower body pathologies. In this article, a Neural Network (NN)-based system capable of classifying pathologies of the lower train by the way of walking in a non-controlled scenario, with the ability to add new users without retraining the system is presented. All the signals are filtered and processed in order to extract the Gait Cycles (GCs), and those cycles are used as input for the NN. To optimize the network a random search optimization process has been performed. To test the system a database with 51 users and 3 visits per user has been collected. After some improvements, the algorithm can correctly classify the 92% of the cases with 60% of training data. This algorithm is a first approach of creating a system to make a first stage pathology detection without the requirement to move to a specific place.en
dc.format.extent11
dc.identifier.bibliographicCitationSanchez-Casanova, J., Liu-Jimenez, J., Tirado-Martin, P., & Sanchez-Reillo, R. (2021). Unsupervised and scalable low train pathology detection system based on neural networks. Heliyon, 7(2), e06270en
dc.identifier.doihttps://doi.org/10.1016/j.heliyon.2021.e06270
dc.identifier.issn2405-8440
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue2
dc.identifier.publicationlastpage11
dc.identifier.publicationtitleHeliyonen
dc.identifier.publicationvolume7
dc.identifier.urihttps://hdl.handle.net/10016/33322
dc.identifier.uxxiAR/0000028274
dc.language.isoengen
dc.publisherElsevieren
dc.rights© 2021 by the authorsen
dc.rightsAtribución 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subject.ecienciaElectrónicaes
dc.subject.ecienciaMedicinaes
dc.subject.otherBiomechanicsen
dc.subject.otherGait Analysisen
dc.subject.otherPathology Detectionen
dc.subject.otherPattern Recognitionen
dc.subject.otherRecurrent Neural Networken
dc.subject.otherSignal Processingen
dc.titleUnsupervised and scalable low train pathology detection system based on neural networksen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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