Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices

Background Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthrit...
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Autor*in:	Li, Gege [verfasserIn] Li, Shilin Xie, Junan Zhang, Zhuodong Zou, Jihua Yang, Chengduan He, Longlong Zeng, Qing Shu, Lin Huang, Guozhi

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Knee osteoarthritis Machine learning Wearable device Gait analysis Plantar pressure

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Journal of neuroEngineering and rehabilitation - BioMed Central, 2004, 21(2024), 1 vom: 03. Apr.
Übergeordnetes Werk:	volume:21 ; year:2024 ; number:1 ; day:03 ; month:04

Links:	Volltext

DOI / URN:	10.1186/s12984-024-01337-6

Katalog-ID:	SPR055404456

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520			\|a Background Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. Methods This study included 92 participants with variable degrees of KOA. A modified Kellgren–Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). Results Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. Conclusion Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans.
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allfields	10.1186/s12984-024-01337-6 doi (DE-627)SPR055404456 (SPR)s12984-024-01337-6-e DE-627 ger DE-627 rakwb eng 610 VZ 44.09 bkl 44.52 bkl 44.90 bkl Li, Gege verfasserin aut Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. Methods This study included 92 participants with variable degrees of KOA. A modified Kellgren–Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). Results Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. Conclusion Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans. Knee osteoarthritis (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Wearable device (dpeaa)DE-He213 Gait analysis (dpeaa)DE-He213 Plantar pressure (dpeaa)DE-He213 Li, Shilin aut Xie, Junan aut Zhang, Zhuodong aut Zou, Jihua aut Yang, Chengduan aut He, Longlong aut Zeng, Qing aut Shu, Lin aut Huang, Guozhi aut Enthalten in Journal of neuroEngineering and rehabilitation BioMed Central, 2004 21(2024), 1 vom: 03. Apr. (DE-627)461907933 (DE-600)2164377-5 1743-0003 nnns volume:21 year:2024 number:1 day:03 month:04 https://dx.doi.org/10.1186/s12984-024-01337-6 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 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_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4338 GBV_ILN_4367 GBV_ILN_4700 44.09 VZ 44.52 VZ 44.90 VZ AR 21 2024 1 03 04
spelling	10.1186/s12984-024-01337-6 doi (DE-627)SPR055404456 (SPR)s12984-024-01337-6-e DE-627 ger DE-627 rakwb eng 610 VZ 44.09 bkl 44.52 bkl 44.90 bkl Li, Gege verfasserin aut Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. Methods This study included 92 participants with variable degrees of KOA. A modified Kellgren–Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). Results Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. Conclusion Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans. Knee osteoarthritis (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Wearable device (dpeaa)DE-He213 Gait analysis (dpeaa)DE-He213 Plantar pressure (dpeaa)DE-He213 Li, Shilin aut Xie, Junan aut Zhang, Zhuodong aut Zou, Jihua aut Yang, Chengduan aut He, Longlong aut Zeng, Qing aut Shu, Lin aut Huang, Guozhi aut Enthalten in Journal of neuroEngineering and rehabilitation BioMed Central, 2004 21(2024), 1 vom: 03. Apr. (DE-627)461907933 (DE-600)2164377-5 1743-0003 nnns volume:21 year:2024 number:1 day:03 month:04 https://dx.doi.org/10.1186/s12984-024-01337-6 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 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_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4338 GBV_ILN_4367 GBV_ILN_4700 44.09 VZ 44.52 VZ 44.90 VZ AR 21 2024 1 03 04
allfields_unstemmed	10.1186/s12984-024-01337-6 doi (DE-627)SPR055404456 (SPR)s12984-024-01337-6-e DE-627 ger DE-627 rakwb eng 610 VZ 44.09 bkl 44.52 bkl 44.90 bkl Li, Gege verfasserin aut Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. Methods This study included 92 participants with variable degrees of KOA. A modified Kellgren–Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). Results Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. Conclusion Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans. Knee osteoarthritis (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Wearable device (dpeaa)DE-He213 Gait analysis (dpeaa)DE-He213 Plantar pressure (dpeaa)DE-He213 Li, Shilin aut Xie, Junan aut Zhang, Zhuodong aut Zou, Jihua aut Yang, Chengduan aut He, Longlong aut Zeng, Qing aut Shu, Lin aut Huang, Guozhi aut Enthalten in Journal of neuroEngineering and rehabilitation BioMed Central, 2004 21(2024), 1 vom: 03. Apr. (DE-627)461907933 (DE-600)2164377-5 1743-0003 nnns volume:21 year:2024 number:1 day:03 month:04 https://dx.doi.org/10.1186/s12984-024-01337-6 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 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_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4338 GBV_ILN_4367 GBV_ILN_4700 44.09 VZ 44.52 VZ 44.90 VZ AR 21 2024 1 03 04
allfieldsGer	10.1186/s12984-024-01337-6 doi (DE-627)SPR055404456 (SPR)s12984-024-01337-6-e DE-627 ger DE-627 rakwb eng 610 VZ 44.09 bkl 44.52 bkl 44.90 bkl Li, Gege verfasserin aut Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. Methods This study included 92 participants with variable degrees of KOA. A modified Kellgren–Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). Results Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. Conclusion Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans. Knee osteoarthritis (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Wearable device (dpeaa)DE-He213 Gait analysis (dpeaa)DE-He213 Plantar pressure (dpeaa)DE-He213 Li, Shilin aut Xie, Junan aut Zhang, Zhuodong aut Zou, Jihua aut Yang, Chengduan aut He, Longlong aut Zeng, Qing aut Shu, Lin aut Huang, Guozhi aut Enthalten in Journal of neuroEngineering and rehabilitation BioMed Central, 2004 21(2024), 1 vom: 03. Apr. (DE-627)461907933 (DE-600)2164377-5 1743-0003 nnns volume:21 year:2024 number:1 day:03 month:04 https://dx.doi.org/10.1186/s12984-024-01337-6 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 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_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4338 GBV_ILN_4367 GBV_ILN_4700 44.09 VZ 44.52 VZ 44.90 VZ AR 21 2024 1 03 04
allfieldsSound	10.1186/s12984-024-01337-6 doi (DE-627)SPR055404456 (SPR)s12984-024-01337-6-e DE-627 ger DE-627 rakwb eng 610 VZ 44.09 bkl 44.52 bkl 44.90 bkl Li, Gege verfasserin aut Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. Methods This study included 92 participants with variable degrees of KOA. A modified Kellgren–Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). Results Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. Conclusion Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans. Knee osteoarthritis (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Wearable device (dpeaa)DE-He213 Gait analysis (dpeaa)DE-He213 Plantar pressure (dpeaa)DE-He213 Li, Shilin aut Xie, Junan aut Zhang, Zhuodong aut Zou, Jihua aut Yang, Chengduan aut He, Longlong aut Zeng, Qing aut Shu, Lin aut Huang, Guozhi aut Enthalten in Journal of neuroEngineering and rehabilitation BioMed Central, 2004 21(2024), 1 vom: 03. Apr. (DE-627)461907933 (DE-600)2164377-5 1743-0003 nnns volume:21 year:2024 number:1 day:03 month:04 https://dx.doi.org/10.1186/s12984-024-01337-6 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 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_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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_4338 GBV_ILN_4367 GBV_ILN_4700 44.09 VZ 44.52 VZ 44.90 VZ AR 21 2024 1 03 04
language	English
source	Enthalten in Journal of neuroEngineering and rehabilitation 21(2024), 1 vom: 03. Apr. volume:21 year:2024 number:1 day:03 month:04
sourceStr	Enthalten in Journal of neuroEngineering and rehabilitation 21(2024), 1 vom: 03. Apr. volume:21 year:2024 number:1 day:03 month:04
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Knee osteoarthritis Machine learning Wearable device Gait analysis Plantar pressure
dewey-raw	610
isfreeaccess_bool	true
container_title	Journal of neuroEngineering and rehabilitation
authorswithroles_txt_mv	Li, Gege @@aut@@ Li, Shilin @@aut@@ Xie, Junan @@aut@@ Zhang, Zhuodong @@aut@@ Zou, Jihua @@aut@@ Yang, Chengduan @@aut@@ He, Longlong @@aut@@ Zeng, Qing @@aut@@ Shu, Lin @@aut@@ Huang, Guozhi @@aut@@
publishDateDaySort_date	2024-04-03T00:00:00Z
hierarchy_top_id	461907933
dewey-sort	3610
id	SPR055404456
language_de	englisch
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Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. Methods This study included 92 participants with variable degrees of KOA. A modified Kellgren–Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). Results Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. Conclusion Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. 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author	Li, Gege
spellingShingle	Li, Gege ddc 610 bkl 44.09 bkl 44.52 bkl 44.90 misc Knee osteoarthritis misc Machine learning misc Wearable device misc Gait analysis misc Plantar pressure Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices
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topic_title	610 VZ 44.09 bkl 44.52 bkl 44.90 bkl Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices Knee osteoarthritis (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Wearable device (dpeaa)DE-He213 Gait analysis (dpeaa)DE-He213 Plantar pressure (dpeaa)DE-He213
topic	ddc 610 bkl 44.09 bkl 44.52 bkl 44.90 misc Knee osteoarthritis misc Machine learning misc Wearable device misc Gait analysis misc Plantar pressure
topic_unstemmed	ddc 610 bkl 44.09 bkl 44.52 bkl 44.90 misc Knee osteoarthritis misc Machine learning misc Wearable device misc Gait analysis misc Plantar pressure
topic_browse	ddc 610 bkl 44.09 bkl 44.52 bkl 44.90 misc Knee osteoarthritis misc Machine learning misc Wearable device misc Gait analysis misc Plantar pressure
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title	Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices
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title_full	Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices
author_sort	Li, Gege
journal	Journal of neuroEngineering and rehabilitation
journalStr	Journal of neuroEngineering and rehabilitation
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author_browse	Li, Gege Li, Shilin Xie, Junan Zhang, Zhuodong Zou, Jihua Yang, Chengduan He, Longlong Zeng, Qing Shu, Lin Huang, Guozhi
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title_sort	identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices
title_auth	Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices
abstract	Background Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. Methods This study included 92 participants with variable degrees of KOA. A modified Kellgren–Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). Results Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. Conclusion Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans. © The Author(s) 2024
abstractGer	Background Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. Methods This study included 92 participants with variable degrees of KOA. A modified Kellgren–Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). Results Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. Conclusion Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans. © The Author(s) 2024
abstract_unstemmed	Background Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. Methods This study included 92 participants with variable degrees of KOA. A modified Kellgren–Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). Results Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. Conclusion Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans. © The Author(s) 2024
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title_short	Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices
url	https://dx.doi.org/10.1186/s12984-024-01337-6
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author2	Li, Shilin Xie, Junan Zhang, Zhuodong Zou, Jihua Yang, Chengduan He, Longlong Zeng, Qing Shu, Lin Huang, Guozhi
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up_date	2024-07-03T15:25:40.918Z
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Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices

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