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Article

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

by
Trung C. Phan
1,
Adrian Pranata
1,2,3,4,
Joshua Farragher
3,4,
Adam Bryant
5,
Hung T. Nguyen
1 and
Rifai Chai
1,*
1
School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
2
School of Health Sciences, Swinburne University of Technology, Hawthorn, VIC 3122, Australia
3
College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
4
School of Health and Biomedical Sciences, RMIT University, Melbourne, VIC 3000, Australia
5
Centre for Health, Exercise and Sports Medicine, Department of Physiotherapy, The University of Melbourne, Melbourne, VIC 3010, Australia
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(4), 1337; https://doi.org/10.3390/s24041337
Submission received: 14 January 2024 / Revised: 8 February 2024 / Accepted: 17 February 2024 / Published: 19 February 2024
(This article belongs to the Special Issue Feature Papers in Wearables 2023)
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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.
Keywords: low back pain; lifting technique; camera system; sagittal plane; trunk; hip; knee; range of motion; regression machine learning; forecast low back pain; lifting technique; camera system; sagittal plane; trunk; hip; knee; range of motion; regression machine learning; forecast

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MDPI and ACS Style

Phan, T.C.; Pranata, A.; Farragher, J.; Bryant, A.; Nguyen, H.T.; Chai, R. Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain. Sensors 2024, 24, 1337. https://doi.org/10.3390/s24041337

AMA Style

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. Sensors. 2024; 24(4):1337. https://doi.org/10.3390/s24041337

Chicago/Turabian Style

Phan, Trung C., Adrian Pranata, Joshua Farragher, Adam Bryant, Hung T. Nguyen, and Rifai Chai. 2024. "Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain" Sensors 24, no. 4: 1337. https://doi.org/10.3390/s24041337

APA Style

Phan, T. C., Pranata, A., Farragher, J., Bryant, A., Nguyen, H. T., & Chai, R. (2024). Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain. Sensors, 24(4), 1337. https://doi.org/10.3390/s24041337

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