000132263 001__ 132263
000132263 005__ 20240301161207.0
000132263 0247_ $$2doi$$a10.3390/s24030831
000132263 0248_ $$2sideral$$a137416
000132263 037__ $$aART-2024-137416
000132263 041__ $$aeng
000132263 100__ $$aVillalba-Meneses, Fernando
000132263 245__ $$aClassification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning
000132263 260__ $$c2024
000132263 5060_ $$aAccess copy available to the general public$$fUnrestricted
000132263 5203_ $$aLow back pain (LBP) is a highly common musculoskeletal condition and the leading cause of work absenteeism. This project aims to develop a medical test to help healthcare professionals decide on and assign physical treatment for patients with nonspecific LBP. The design uses machine learning (ML) models based on the classification of motion capture (MoCap) data obtained from the range of motion (ROM) exercises among healthy and clinically diagnosed patients with LBP from Imbabura–Ecuador. The following seven ML algorithms were tested for evaluation and comparison: logistic regression, decision tree, random forest, support vector machine (SVM), k-nearest neighbor (KNN), multilayer perceptron (MLP), and gradient boosting algorithms. All ML techniques obtained an accuracy above 80%, and three models (SVM, random forest, and MLP) obtained an accuracy of >90%. SVM was found to be the best-performing algorithm. This article aims to improve the applicability of inertial MoCap in healthcare by making use of precise spatiotemporal measurements with a data-driven treatment approach to improve the quality of life of people with chronic LBP.
000132263 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000132263 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000132263 700__ $$aGuevara, Cesar
000132263 700__ $$aLojan, Alejandro B.
000132263 700__ $$aGualsaqui, Mario G.
000132263 700__ $$aArias-Serrano, Isaac
000132263 700__ $$aVelásquez-López, Paolo A.
000132263 700__ $$aAlmeida-Galárraga, Diego
000132263 700__ $$aTirado-Espín, Andrés
000132263 700__ $$0(orcid)0000-0003-4527-3267$$aMarín, Javier$$uUniversidad de Zaragoza
000132263 700__ $$0(orcid)0000-0003-3223-1324$$aMarín, José J.$$uUniversidad de Zaragoza
000132263 7102_ $$15002$$2720$$aUniversidad de Zaragoza$$bDpto. Ingeniería Diseño Fabri.$$cÁrea Proyectos de Ingeniería
000132263 773__ $$g24, 3 (2024), 831 [15 pp.]$$pSensors$$tSensors$$x1424-8220
000132263 8564_ $$s1658496$$uhttps://zaguan.unizar.es/record/132263/files/texto_completo.pdf$$yVersión publicada
000132263 8564_ $$s2792345$$uhttps://zaguan.unizar.es/record/132263/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000132263 909CO $$ooai:zaguan.unizar.es:132263$$particulos$$pdriver
000132263 951__ $$a2024-03-01-14:53:31
000132263 980__ $$aARTICLE