Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain.

Phan, TC; Pranata, A; Farragher, J; Bryant, A; Nguyen, HT; Chai, R

Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain.

Phan, TC Pranata, A Farragher, J Bryant, A Nguyen, HT Chai, R

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Sensors (Basel), 2022, 22, (17), pp. 6694
Issue Date:: 2022-09-04

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Field	Value	Language
dc.contributor.author	Phan, TC
dc.contributor.author	Pranata, A
dc.contributor.author	Farragher, J
dc.contributor.author	Bryant, A
dc.contributor.author	Nguyen, HT
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024
dc.date.accessioned	2023-04-11T05:22:09Z
dc.date.available	2022-08-31
dc.date.available	2023-04-11T05:22:09Z
dc.date.issued	2022-09-04
dc.identifier.citation	Sensors (Basel), 2022, 22, (17), pp. 6694
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/169597
dc.description.abstract	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.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Sensors (Basel)
dc.relation.isbasedon	10.3390/s22176694
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Analytical Chemistry
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Biomechanical Phenomena
dc.subject.mesh	Humans
dc.subject.mesh	Lifting
dc.subject.mesh	Low Back Pain
dc.subject.mesh	Machine Learning
dc.subject.mesh	Self Efficacy
dc.subject.mesh	Humans
dc.subject.mesh	Low Back Pain
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Self Efficacy
dc.subject.mesh	Lifting
dc.subject.mesh	Biomechanical Phenomena
dc.subject.mesh	Machine Learning
dc.subject.mesh	Bayes Theorem
dc.subject.mesh	Biomechanical Phenomena
dc.subject.mesh	Humans
dc.subject.mesh	Lifting
dc.subject.mesh	Low Back Pain
dc.subject.mesh	Machine Learning
dc.subject.mesh	Self Efficacy
dc.title	Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain.
dc.type	Journal Article
utslib.citation.volume	22
utslib.location.activity	Switzerland
utslib.for	0301 Analytical Chemistry
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
dc.date.updated	2023-04-11T05:21:20Z
pubs.issue	17
pubs.publication-status	Published online
pubs.volume	22
utslib.citation.issue	17

Abstract:

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.

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http://hdl.handle.net/10453/169597