Article ; Online: Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning.
2024 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 ...
| 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. |
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| MeSH term(s) | Humans ; Low Back Pain/diagnosis ; Quality of Life ; Machine Learning ; Algorithms ; Range of Motion, Articular ; Wearable Electronic Devices ; Support Vector Machine ; Organothiophosphates |
| Chemical Substances | ethoprop (13194-48-4) ; Organothiophosphates |
| Language | English |
| Publishing date | 2024-01-27 |
| Publishing country | Switzerland |
| Document type | Journal Article |
| ZDB-ID | 2052857-7 |
| ISSN | 1424-8220 ; 1424-8220 |
| ISSN (online) | 1424-8220 |
| ISSN | 1424-8220 |
| DOI | 10.3390/s24030831 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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