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...
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Autor*in:	Trung C. Phan [verfasserIn] Adrian Pranata [verfasserIn] Joshua Farragher [verfasserIn] Adam Bryant [verfasserIn] Hung T. Nguyen [verfasserIn] Rifai Chai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	low back pain lifting technique camera system ward clustering method K-means clustering method ensemble clustering method

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 22(2022), 17, p 6694
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:17, p 6694

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s22176694

Katalog-ID:	DOAJ033492573

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spelling	10.3390/s22176694 doi (DE-627)DOAJ033492573 (DE-599)DOAJ601b5a8bd5f445b3a52d10076849e3c8 DE-627 ger DE-627 rakwb eng TP1-1185 Trung C. Phan verfasserin aut Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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, <i<F</i< (9, 1136) = 195.67, <i<p</i< < 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. low back pain lifting technique camera system ward clustering method K-means clustering method ensemble clustering method Chemical technology Adrian Pranata verfasserin aut Joshua Farragher verfasserin aut Adam Bryant verfasserin aut Hung T. Nguyen verfasserin aut Rifai Chai verfasserin aut In Sensors MDPI AG, 2003 22(2022), 17, p 6694 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:17, p 6694 https://doi.org/10.3390/s22176694 kostenfrei https://doaj.org/article/601b5a8bd5f445b3a52d10076849e3c8 kostenfrei https://www.mdpi.com/1424-8220/22/17/6694 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 17, p 6694
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allfieldsGer	10.3390/s22176694 doi (DE-627)DOAJ033492573 (DE-599)DOAJ601b5a8bd5f445b3a52d10076849e3c8 DE-627 ger DE-627 rakwb eng TP1-1185 Trung C. Phan verfasserin aut Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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, <i<F</i< (9, 1136) = 195.67, <i<p</i< < 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. low back pain lifting technique camera system ward clustering method K-means clustering method ensemble clustering method Chemical technology Adrian Pranata verfasserin aut Joshua Farragher verfasserin aut Adam Bryant verfasserin aut Hung T. Nguyen verfasserin aut Rifai Chai verfasserin aut In Sensors MDPI AG, 2003 22(2022), 17, p 6694 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:17, p 6694 https://doi.org/10.3390/s22176694 kostenfrei https://doaj.org/article/601b5a8bd5f445b3a52d10076849e3c8 kostenfrei https://www.mdpi.com/1424-8220/22/17/6694 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 17, p 6694
allfieldsSound	10.3390/s22176694 doi (DE-627)DOAJ033492573 (DE-599)DOAJ601b5a8bd5f445b3a52d10076849e3c8 DE-627 ger DE-627 rakwb eng TP1-1185 Trung C. Phan verfasserin aut Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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, <i<F</i< (9, 1136) = 195.67, <i<p</i< < 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. low back pain lifting technique camera system ward clustering method K-means clustering method ensemble clustering method Chemical technology Adrian Pranata verfasserin aut Joshua Farragher verfasserin aut Adam Bryant verfasserin aut Hung T. Nguyen verfasserin aut Rifai Chai verfasserin aut In Sensors MDPI AG, 2003 22(2022), 17, p 6694 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:17, p 6694 https://doi.org/10.3390/s22176694 kostenfrei https://doaj.org/article/601b5a8bd5f445b3a52d10076849e3c8 kostenfrei https://www.mdpi.com/1424-8220/22/17/6694 kostenfrei https://doaj.org/toc/1424-8220 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 17, p 6694
language	English
source	In Sensors 22(2022), 17, p 6694 volume:22 year:2022 number:17, p 6694
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topic_facet	low back pain lifting technique camera system ward clustering method K-means clustering method ensemble clustering method Chemical technology
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authorswithroles_txt_mv	Trung C. Phan @@aut@@ Adrian Pranata @@aut@@ Joshua Farragher @@aut@@ Adam Bryant @@aut@@ Hung T. Nguyen @@aut@@ Rifai Chai @@aut@@
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callnumber-first	T - Technology
author	Trung C. Phan
spellingShingle	Trung C. Phan misc TP1-1185 misc low back pain misc lifting technique misc camera system misc ward clustering method misc K-means clustering method misc ensemble clustering method misc Chemical technology Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain
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topic_title	TP1-1185 Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain low back pain lifting technique camera system ward clustering method K-means clustering method ensemble clustering method
topic	misc TP1-1185 misc low back pain misc lifting technique misc camera system misc ward clustering method misc K-means clustering method misc ensemble clustering method misc Chemical technology
topic_unstemmed	misc TP1-1185 misc low back pain misc lifting technique misc camera system misc ward clustering method misc K-means clustering method misc ensemble clustering method misc Chemical technology
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title	Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain
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title_sort	machine learning derived lifting techniques and pain self-efficacy in people with chronic low back pain
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title_auth	Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain
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, <i<F</i< (9, 1136) = 195.67, <i<p</i< < 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.
abstractGer	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, <i<F</i< (9, 1136) = 195.67, <i<p</i< < 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.
abstract_unstemmed	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, <i<F</i< (9, 1136) = 195.67, <i<p</i< < 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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title_short	Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain
url	https://doi.org/10.3390/s22176694 https://doaj.org/article/601b5a8bd5f445b3a52d10076849e3c8 https://www.mdpi.com/1424-8220/22/17/6694 https://doaj.org/toc/1424-8220
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up_date	2024-07-03T18:03:34.278Z
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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, <i<F</i< (9, 1136) = 195.67, <i<p</i< < 0.0001. The results of this study suggest that there are four different lifting techniques in people with CLBP. 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Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain

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