Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning.

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

SUBMITTER: Villalba-Meneses F 

PROVIDER: S-EPMC10857033 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature