Please use this identifier to cite or link to this item: https://repositorio.uti.edu.ec//handle/123456789/6960
Title: Classification of the PathologicaMarín, Javierl Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning
Authors: Villalba-Meneses, Fernando
Guevara, Cesar
Lojan, Alejandro
Gualsaqui, Mario
Arias-Serrano, Isaac
Velásquez-López, Paolo
Almeida-Galárraga, Diego
Tirado-Espín, André
Marín, Javier
Marín, Jos
Issue Date: 2024
Publisher: Sensors. Open Access. Volume 24, Issue 3
Abstract: Low 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.
URI: https://www.mdpi.com/1424-8220/24/3/831
https://repositorio.uti.edu.ec//handle/123456789/6960
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