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Approaches to classify knee osteoarthritis using biomechanical data

2021Conference paper Restricted access
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dc.contributor.authorFranco, Tiago
dc.contributor.authorHenriques, Pedro Rangel
dc.contributor.authorAlves, Paulo
dc.contributor.authorPereira, Maria João
dc.date.accessioned2022-04-05T14:18:37Z
dc.date.available2022-04-05T14:18:37Z
dc.date.issued2021
dc.description.abstractKnee osteoarthritis (KOA) is a degenerative disease that mainly affects the elderly. The development of this disease is associated with a complex set of factors that cause abnormalities in motor functions. The purpose of this review is to understand the composition of works that combine biomechanical data and machine learning techniques to classify KOA progress. This study was based on research articles found in the search engines Scopus and PubMed between January 2010 and April 2021. The results were divided into data acquisition, feature engineering, and algorithms to synthesize the discovered content. Several approaches have been found for KOA classification with significant accuracy, with an average of 86% overall and three papers reaching 100%; that is, they did not fail once in their tests. The acquisition of data proved to be the divergent task between the works, the most considerable correlation in this stage was the use of the ground reaction force (GRF) sensor. Although three studies reached 100% in the classification, two did not use a gradual evaluation scale, classifying between KOA or healthy individuals. Thus, we can get out of this work that machine learning techniques are promising for identifying KOA using biomechanical data. However, the classification of pathological stages is a complex problem to discuss, mainly due to the difficult access and lack of standardization in data acquisition.pt_PT
dc.description.sponsorshipThis work was supported by FCT - Fundação para a Ciência e a Tecnologia under Projects UIDB/05757/2020, UIDB/00319/2020 and individual research grant 2020.05704.BD, funded by Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) and Fundo Social Europeu (FSE) through The Programa Operacional Regional Norte.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFranco, Tiago; Henriques, P.R.; Alves, Paulo; Pereira, Maria João (2021). Approaches to classify knee osteoarthritis using biomechanical data. In Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Pacheco, Maria F.; Alves, Paulo; Lopes, Rui Pedro (Eds.) Optimization, learning algorithms and applications: first International Conference, OL2A 2021. Cham: Springer Nature. p. 417-429. ISBN 978-3-030-91884-2pt_PT
dc.identifier.doi10.1007/978-3-030-91885-9_31pt_PT
dc.identifier.isbn978-3-030-91884-2
dc.identifier.urihttp://hdl.handle.net/10198/25352
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationALGORITMI Research Center
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectKnee osteoarthritispt_PT
dc.subjectBiomechanicalpt_PT
dc.subjectData classificationpt_PT
dc.subjectMachine learningpt_PT
dc.titleApproaches to classify knee osteoarthritis using biomechanical datapt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleALGORITMI Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00319%2F2020/PT
oaire.citation.conferencePlaceBragançapt_PT
oaire.citation.endPage429pt_PT
oaire.citation.startPage417pt_PT
oaire.citation.titleOptimization, learning algorithms and applications: first International Conference, OL2A 2021pt_PT
oaire.citation.volume1488pt_PT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
person.familyNameFranco
person.familyNameAlves
person.familyNamePereira
person.givenNameTiago
person.givenNamePaulo
person.givenNameMaria João
person.identifierUAMm8moAAAAJ&hl
person.identifier.ciencia-id7F19-C649-5DD9
person.identifier.ciencia-idC319-FC42-5B6B
person.identifier.ciencia-idC912-4A49-A3B3
person.identifier.orcid0000-0001-8574-4380
person.identifier.orcid0000-0002-0100-8691
person.identifier.orcid0000-0001-6323-0071
person.identifier.ridG-5999-2011
person.identifier.scopus-author-id57223608236
person.identifier.scopus-author-id55834442100
person.identifier.scopus-author-id13907870300
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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