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Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain
This paper proposes an innovative methodology for finding how many lifting techniques people with chronic low back pain (CLBP) can demonstrate with camera data collected from 115 participants. The system employs a feature extraction algorithm to calculate the knee, trunk and hip range of motion in t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460822/ https://www.ncbi.nlm.nih.gov/pubmed/36081153 http://dx.doi.org/10.3390/s22176694 |
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author | Phan, Trung C. Pranata, Adrian Farragher, Joshua Bryant, Adam Nguyen, Hung T. Chai, Rifai |
author_facet | Phan, Trung C. Pranata, Adrian Farragher, Joshua Bryant, Adam Nguyen, Hung T. Chai, Rifai |
author_sort | Phan, Trung C. |
collection | PubMed |
description | This paper proposes an innovative methodology for finding how many lifting techniques people with chronic low back pain (CLBP) can demonstrate with camera data collected from 115 participants. The system employs a feature extraction algorithm to calculate the knee, trunk and hip range of motion in the sagittal plane, Ward’s method, a combination of K-means and Ensemble clustering method for classification algorithm, and Bayesian neural network to validate the result of Ward’s method and the combination of K-means and Ensemble clustering method. The classification results and effect size show that Ward clustering is the optimal method where precision and recall percentages of all clusters are above 90, and the overall accuracy of the Bayesian Neural Network is 97.9%. The statistical analysis reported a significant difference in the range of motion of the knee, hip and trunk between each cluster, F (9, 1136) = 195.67, p < 0.0001. The results of this study suggest that there are four different lifting techniques in people with CLBP. Additionally, the results show that even though the clusters demonstrated similar pain levels, one of the clusters, which uses the least amount of trunk and the most knee movement, demonstrates the lowest pain self-efficacy. |
format | Online Article Text |
id | pubmed-9460822 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94608222022-09-10 Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain Phan, Trung C. Pranata, Adrian Farragher, Joshua Bryant, Adam Nguyen, Hung T. Chai, Rifai Sensors (Basel) Article This paper proposes an innovative methodology for finding how many lifting techniques people with chronic low back pain (CLBP) can demonstrate with camera data collected from 115 participants. The system employs a feature extraction algorithm to calculate the knee, trunk and hip range of motion in the sagittal plane, Ward’s method, a combination of K-means and Ensemble clustering method for classification algorithm, and Bayesian neural network to validate the result of Ward’s method and the combination of K-means and Ensemble clustering method. The classification results and effect size show that Ward clustering is the optimal method where precision and recall percentages of all clusters are above 90, and the overall accuracy of the Bayesian Neural Network is 97.9%. The statistical analysis reported a significant difference in the range of motion of the knee, hip and trunk between each cluster, F (9, 1136) = 195.67, p < 0.0001. The results of this study suggest that there are four different lifting techniques in people with CLBP. Additionally, the results show that even though the clusters demonstrated similar pain levels, one of the clusters, which uses the least amount of trunk and the most knee movement, demonstrates the lowest pain self-efficacy. MDPI 2022-09-04 /pmc/articles/PMC9460822/ /pubmed/36081153 http://dx.doi.org/10.3390/s22176694 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Phan, Trung C. Pranata, Adrian Farragher, Joshua Bryant, Adam Nguyen, Hung T. Chai, Rifai Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain |
title | Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain |
title_full | Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain |
title_fullStr | Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain |
title_full_unstemmed | Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain |
title_short | Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain |
title_sort | machine learning derived lifting techniques and pain self-efficacy in people with chronic low back pain |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460822/ https://www.ncbi.nlm.nih.gov/pubmed/36081153 http://dx.doi.org/10.3390/s22176694 |
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