Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/122999
Campo DC Valoridioma
dc.contributor.authorPandey, Chandrasenen_US
dc.contributor.authorBaghel, Neerajen_US
dc.contributor.authorDutta, Malay Kishoreen_US
dc.contributor.authorTravieso González, Carlos Manuelen_US
dc.date.accessioned2023-05-24T07:01:14Z-
dc.date.available2023-05-24T07:01:14Z-
dc.date.issued2022en_US
dc.identifier.issn0379-3962en_US
dc.identifier.otherDialnet-
dc.identifier.urihttp://hdl.handle.net/10553/122999-
dc.description.abstractBack pain is a common pain that mostly affects people of all ages and results in different types of disorders such as Obesity, Slipped disc, Scoliosis, and Osteoporosis, etc. The diagnosis of back pain disorder is difficult due to the extent affected by the disorder and exact biomechanical factors. This work presents a machine learning method to diagnose these disorders using the Gait monitoring system. It involves support vector machines that classify between lower back pain and normal, on the bases of 3 Gait patterns that are integrated pressure, the direction of progression, and CISP-ML. The proposed method uses 13 different features such as mean and standard deviation, etc. recorded from 62 subjects (30 normal and 32 with lower back pain). The features alone resulted in higher leave-one-out classification accuracy (LOOCV) 92%. The proposed method can be used for automatically diagnosing the lower back pain and its gait effects on the person. This model can be ported to small computing devices for self-diagnosis of lower back pain in a remote area.en_US
dc.languageengen_US
dc.publisherEditorial Tecnológica de Costa Rica
dc.relation.ispartofTecnología en Marchaen_US
dc.sourceTecnología en Marcha[ISSN 0379-3962],v. 35 (4), p. 93-100en_US
dc.subject220990 Tratamiento digital. Imágenesen_US
dc.subject3314 Tecnología médicaen_US
dc.subject.otherGait Analysisen_US
dc.subject.otherBack Painen_US
dc.subject.otherSupport vector machineen_US
dc.titleAutomatic diagnosis of lower back pain using gait patternsen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typeArticleen_US
dc.identifier.urlhttp://dialnet.unirioja.es/servlet/articulo?codigo=8828178-
dc.description.lastpage100en_US
dc.identifier.issue4-
dc.description.firstpage93en_US
dc.relation.volume35en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.authordialnetidNo ID-
dc.contributor.authordialnetidNo ID-
dc.contributor.authordialnetidNo ID-
dc.contributor.authordialnetid1770687-
dc.identifier.dialnet8828178ARTREV-
dc.utils.revisionNoen_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-TELen_US
dc.description.esciESCI
dc.description.miaricds8,0
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-4621-2768-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameTravieso González, Carlos Manuel-
Colección:Artículos
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