Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning
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) mo...
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MDPI AG
2024-01-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/3/831 |
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| author | Fernando Villalba-Meneses Cesar Guevara Alejandro B. Lojan Mario G. Gualsaqui Isaac Arias-Serrano Paolo A. Velásquez-López Diego Almeida-Galárraga Andrés Tirado-Espín Javier Marín José J. Marín |
| author_facet | Fernando Villalba-Meneses Cesar Guevara Alejandro B. Lojan Mario G. Gualsaqui Isaac Arias-Serrano Paolo A. Velásquez-López Diego Almeida-Galárraga Andrés Tirado-Espín Javier Marín José J. Marín |
| author_sort | Fernando Villalba-Meneses |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T03:48:57Z |
| format | Article |
| id | doaj.art-9690230e5aa04c5dbc7e3bebf0e54a76 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T03:48:57Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9690230e5aa04c5dbc7e3bebf0e54a762024-02-09T15:21:58ZengMDPI AGSensors1424-82202024-01-0124383110.3390/s24030831Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine LearningFernando Villalba-Meneses0Cesar Guevara1Alejandro B. Lojan2Mario G. Gualsaqui3Isaac Arias-Serrano4Paolo A. Velásquez-López5Diego Almeida-Galárraga6Andrés Tirado-Espín7Javier Marín8José J. Marín9IDERGO (Research and Development in Ergonomics), I3A (Instituto de Investigación en Ingeniería de Aragón), University of Zaragoza, C/Mariano Esquillor s/n, 50018 Zaragoza, SpainCentro de Investigación en Mecatrónica y Sistemas Interactivos—MIST, Universidad Tecnológica Indoamérica, Quito 170103, EcuadorSchool of Biological Sciences and Engineering, Yachay Tech University, Hacienda San José s/n, San Miguel de Urcuquí 100119, EcuadorSchool of Biological Sciences and Engineering, Yachay Tech University, Hacienda San José s/n, San Miguel de Urcuquí 100119, EcuadorSchool of Biological Sciences and Engineering, Yachay Tech University, Hacienda San José s/n, San Miguel de Urcuquí 100119, EcuadorSchool of Biological Sciences and Engineering, Yachay Tech University, Hacienda San José s/n, San Miguel de Urcuquí 100119, EcuadorSchool of Biological Sciences and Engineering, Yachay Tech University, Hacienda San José s/n, San Miguel de Urcuquí 100119, EcuadorSchool of Mathematical and Computational Sciences, Yachay Tech University, Hacienda San José s/n, San Miguel de Urcuquí 100119, EcuadorIDERGO (Research and Development in Ergonomics), I3A (Instituto de Investigación en Ingeniería de Aragón), University of Zaragoza, C/Mariano Esquillor s/n, 50018 Zaragoza, SpainIDERGO (Research and Development in Ergonomics), I3A (Instituto de Investigación en Ingeniería de Aragón), University of Zaragoza, C/Mariano Esquillor s/n, 50018 Zaragoza, SpainLow 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.https://www.mdpi.com/1424-8220/24/3/831MoCapclassificationrange of movementmachine learninglow back pain |
| spellingShingle | Fernando Villalba-Meneses Cesar Guevara Alejandro B. Lojan Mario G. Gualsaqui Isaac Arias-Serrano Paolo A. Velásquez-López Diego Almeida-Galárraga Andrés Tirado-Espín Javier Marín José J. Marín Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning Sensors MoCap classification range of movement machine learning low back pain |
| title | Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning |
| title_full | Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning |
| title_fullStr | Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning |
| title_full_unstemmed | Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning |
| title_short | Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning |
| title_sort | classification of the pathological range of motion in low back pain using wearable sensors and machine learning |
| topic | MoCap classification range of movement machine learning low back pain |
| url | https://www.mdpi.com/1424-8220/24/3/831 |
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