Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain.

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

Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with 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), 2024, 24, (4), pp. 1337
Issue Date:: 2024-02-19

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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	2024-08-02T02:44:21Z
dc.date.available	2024-02-17
dc.date.available	2024-08-02T02:44:21Z
dc.date.issued	2024-02-19
dc.identifier.citation	Sensors (Basel), 2024, 24, (4), pp. 1337
dc.identifier.issn	1424-8220
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/179998
dc.description.abstract	Machine learning (ML) algorithms are crucial within the realm of healthcare applications. However, a comprehensive assessment of the effectiveness of regression algorithms in predicting alterations in lifting movement patterns has not been conducted. This research represents a pilot investigation using regression-based machine learning techniques to forecast alterations in trunk, hip, and knee movements subsequent to a 12-week strength training for people who have low back pain (LBP). The system uses a feature extraction algorithm to calculate the range of motion in the sagittal plane for the knee, trunk, and hip and 12 different regression machine learning algorithms. The results show that Ensemble Tree with LSBoost demonstrated the utmost accuracy in prognosticating trunk movement. Meanwhile, the Ensemble Tree approach, specifically LSBoost, exhibited the highest predictive precision for hip movement. The Gaussian regression with the kernel chosen as exponential returned the highest prediction accuracy for knee movement. These regression models hold the potential to significantly enhance the precision of visualisation of the treatment output for individuals afflicted with LBP.
dc.format	Electronic
dc.language	eng
dc.publisher	MDPI
dc.relation.ispartof	Sensors (Basel)
dc.relation.isbasedon	10.3390/s24041337
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.classification	3103 Ecology
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4104 Environmental management
dc.subject.classification	4606 Distributed computing and systems software
dc.subject.mesh	Humans
dc.subject.mesh	Low Back Pain
dc.subject.mesh	Lifting
dc.subject.mesh	Knee
dc.subject.mesh	Movement
dc.subject.mesh	Machine Learning
dc.subject.mesh	Biomechanical Phenomena
dc.subject.mesh	Knee
dc.subject.mesh	Humans
dc.subject.mesh	Low Back Pain
dc.subject.mesh	Movement
dc.subject.mesh	Lifting
dc.subject.mesh	Biomechanical Phenomena
dc.subject.mesh	Machine Learning
dc.subject.mesh	Humans
dc.subject.mesh	Low Back Pain
dc.subject.mesh	Lifting
dc.subject.mesh	Knee
dc.subject.mesh	Movement
dc.subject.mesh	Machine Learning
dc.subject.mesh	Biomechanical Phenomena
dc.title	Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain.
dc.type	Journal Article
utslib.citation.volume	24
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
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-08-02T02:44:14Z
pubs.issue	4
pubs.publication-status	Published online
pubs.volume	24
utslib.citation.issue	4

Abstract:

Machine learning (ML) algorithms are crucial within the realm of healthcare applications. However, a comprehensive assessment of the effectiveness of regression algorithms in predicting alterations in lifting movement patterns has not been conducted. This research represents a pilot investigation using regression-based machine learning techniques to forecast alterations in trunk, hip, and knee movements subsequent to a 12-week strength training for people who have low back pain (LBP). The system uses a feature extraction algorithm to calculate the range of motion in the sagittal plane for the knee, trunk, and hip and 12 different regression machine learning algorithms. The results show that Ensemble Tree with LSBoost demonstrated the utmost accuracy in prognosticating trunk movement. Meanwhile, the Ensemble Tree approach, specifically LSBoost, exhibited the highest predictive precision for hip movement. The Gaussian regression with the kernel chosen as exponential returned the highest prediction accuracy for knee movement. These regression models hold the potential to significantly enhance the precision of visualisation of the treatment output for individuals afflicted with LBP.

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http://hdl.handle.net/10453/179998