Can Pressure Data from Wearable Insole Devices Be Utilized to Estimate Low Back Moments for Exoskeleton Control System?

This study presents a machine learning model for predicting lumbar spine moments using data from low-cost sensors, with the ultimate aim of developing a control strategy for waist-active exoskeleton devices. The limitation of sparse features in low-cost insoles was addressed by leveraging a source m...
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Autor*in:	Seungheon Chae [verfasserIn] Ahnryul Choi [verfasserIn] Jeehae Kang [verfasserIn] Joung Hwan Mun [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	human motion analysis lifting task machine learning L5/S1 joint torque work-related musculoskeletal disorders low-cost insole system

Übergeordnetes Werk:	In: Actuators - MDPI AG, 2013, 13(2024), 3, p 92
Übergeordnetes Werk:	volume:13 ; year:2024 ; number:3, p 92

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/act13030092

Katalog-ID:	DOAJ100892116

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allfields	10.3390/act13030092 doi (DE-627)DOAJ100892116 (DE-599)DOAJ97dd9a4112264c698fef4a97686f8eec DE-627 ger DE-627 rakwb eng TA401-492 TK1001-1841 Seungheon Chae verfasserin aut Can Pressure Data from Wearable Insole Devices Be Utilized to Estimate Low Back Moments for Exoskeleton Control System? 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study presents a machine learning model for predicting lumbar spine moments using data from low-cost sensors, with the ultimate aim of developing a control strategy for waist-active exoskeleton devices. The limitation of sparse features in low-cost insoles was addressed by leveraging a source model constructed based on data acquired from the high-precision Pedar-X device, employing a transfer learning technique. The model’s performance saw significant improvement through a training approach that incorporated high-precision commercial insole data and fine-tuning with low-cost insole data. In comparison to the conventional model, this method resulted in a noteworthy 7% enhancement in performance, achieving an rRMSE of approximately 12% and a correlation coefficient of 0.9 in lumbar joint moment prediction. If the model can demonstrate real-time efficacy and effectiveness across various operations in future applications, it holds substantial potential for deployment as an active exoskeleton device for the waist. human motion analysis lifting task machine learning L5/S1 joint torque work-related musculoskeletal disorders low-cost insole system Materials of engineering and construction. Mechanics of materials Production of electric energy or power. Powerplants. Central stations Ahnryul Choi verfasserin aut Jeehae Kang verfasserin aut Joung Hwan Mun verfasserin aut In Actuators MDPI AG, 2013 13(2024), 3, p 92 (DE-627)726491802 (DE-600)2682469-3 20760825 nnns volume:13 year:2024 number:3, p 92 https://doi.org/10.3390/act13030092 kostenfrei https://doaj.org/article/97dd9a4112264c698fef4a97686f8eec kostenfrei https://www.mdpi.com/2076-0825/13/3/92 kostenfrei https://doaj.org/toc/2076-0825 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 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 13 2024 3, p 92
spelling	10.3390/act13030092 doi (DE-627)DOAJ100892116 (DE-599)DOAJ97dd9a4112264c698fef4a97686f8eec DE-627 ger DE-627 rakwb eng TA401-492 TK1001-1841 Seungheon Chae verfasserin aut Can Pressure Data from Wearable Insole Devices Be Utilized to Estimate Low Back Moments for Exoskeleton Control System? 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study presents a machine learning model for predicting lumbar spine moments using data from low-cost sensors, with the ultimate aim of developing a control strategy for waist-active exoskeleton devices. The limitation of sparse features in low-cost insoles was addressed by leveraging a source model constructed based on data acquired from the high-precision Pedar-X device, employing a transfer learning technique. The model’s performance saw significant improvement through a training approach that incorporated high-precision commercial insole data and fine-tuning with low-cost insole data. In comparison to the conventional model, this method resulted in a noteworthy 7% enhancement in performance, achieving an rRMSE of approximately 12% and a correlation coefficient of 0.9 in lumbar joint moment prediction. If the model can demonstrate real-time efficacy and effectiveness across various operations in future applications, it holds substantial potential for deployment as an active exoskeleton device for the waist. human motion analysis lifting task machine learning L5/S1 joint torque work-related musculoskeletal disorders low-cost insole system Materials of engineering and construction. Mechanics of materials Production of electric energy or power. Powerplants. Central stations Ahnryul Choi verfasserin aut Jeehae Kang verfasserin aut Joung Hwan Mun verfasserin aut In Actuators MDPI AG, 2013 13(2024), 3, p 92 (DE-627)726491802 (DE-600)2682469-3 20760825 nnns volume:13 year:2024 number:3, p 92 https://doi.org/10.3390/act13030092 kostenfrei https://doaj.org/article/97dd9a4112264c698fef4a97686f8eec kostenfrei https://www.mdpi.com/2076-0825/13/3/92 kostenfrei https://doaj.org/toc/2076-0825 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 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 13 2024 3, p 92
allfields_unstemmed	10.3390/act13030092 doi (DE-627)DOAJ100892116 (DE-599)DOAJ97dd9a4112264c698fef4a97686f8eec DE-627 ger DE-627 rakwb eng TA401-492 TK1001-1841 Seungheon Chae verfasserin aut Can Pressure Data from Wearable Insole Devices Be Utilized to Estimate Low Back Moments for Exoskeleton Control System? 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study presents a machine learning model for predicting lumbar spine moments using data from low-cost sensors, with the ultimate aim of developing a control strategy for waist-active exoskeleton devices. The limitation of sparse features in low-cost insoles was addressed by leveraging a source model constructed based on data acquired from the high-precision Pedar-X device, employing a transfer learning technique. The model’s performance saw significant improvement through a training approach that incorporated high-precision commercial insole data and fine-tuning with low-cost insole data. In comparison to the conventional model, this method resulted in a noteworthy 7% enhancement in performance, achieving an rRMSE of approximately 12% and a correlation coefficient of 0.9 in lumbar joint moment prediction. If the model can demonstrate real-time efficacy and effectiveness across various operations in future applications, it holds substantial potential for deployment as an active exoskeleton device for the waist. human motion analysis lifting task machine learning L5/S1 joint torque work-related musculoskeletal disorders low-cost insole system Materials of engineering and construction. Mechanics of materials Production of electric energy or power. Powerplants. Central stations Ahnryul Choi verfasserin aut Jeehae Kang verfasserin aut Joung Hwan Mun verfasserin aut In Actuators MDPI AG, 2013 13(2024), 3, p 92 (DE-627)726491802 (DE-600)2682469-3 20760825 nnns volume:13 year:2024 number:3, p 92 https://doi.org/10.3390/act13030092 kostenfrei https://doaj.org/article/97dd9a4112264c698fef4a97686f8eec kostenfrei https://www.mdpi.com/2076-0825/13/3/92 kostenfrei https://doaj.org/toc/2076-0825 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 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 13 2024 3, p 92
allfieldsGer	10.3390/act13030092 doi (DE-627)DOAJ100892116 (DE-599)DOAJ97dd9a4112264c698fef4a97686f8eec DE-627 ger DE-627 rakwb eng TA401-492 TK1001-1841 Seungheon Chae verfasserin aut Can Pressure Data from Wearable Insole Devices Be Utilized to Estimate Low Back Moments for Exoskeleton Control System? 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study presents a machine learning model for predicting lumbar spine moments using data from low-cost sensors, with the ultimate aim of developing a control strategy for waist-active exoskeleton devices. The limitation of sparse features in low-cost insoles was addressed by leveraging a source model constructed based on data acquired from the high-precision Pedar-X device, employing a transfer learning technique. The model’s performance saw significant improvement through a training approach that incorporated high-precision commercial insole data and fine-tuning with low-cost insole data. In comparison to the conventional model, this method resulted in a noteworthy 7% enhancement in performance, achieving an rRMSE of approximately 12% and a correlation coefficient of 0.9 in lumbar joint moment prediction. If the model can demonstrate real-time efficacy and effectiveness across various operations in future applications, it holds substantial potential for deployment as an active exoskeleton device for the waist. human motion analysis lifting task machine learning L5/S1 joint torque work-related musculoskeletal disorders low-cost insole system Materials of engineering and construction. Mechanics of materials Production of electric energy or power. Powerplants. Central stations Ahnryul Choi verfasserin aut Jeehae Kang verfasserin aut Joung Hwan Mun verfasserin aut In Actuators MDPI AG, 2013 13(2024), 3, p 92 (DE-627)726491802 (DE-600)2682469-3 20760825 nnns volume:13 year:2024 number:3, p 92 https://doi.org/10.3390/act13030092 kostenfrei https://doaj.org/article/97dd9a4112264c698fef4a97686f8eec kostenfrei https://www.mdpi.com/2076-0825/13/3/92 kostenfrei https://doaj.org/toc/2076-0825 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 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 13 2024 3, p 92
allfieldsSound	10.3390/act13030092 doi (DE-627)DOAJ100892116 (DE-599)DOAJ97dd9a4112264c698fef4a97686f8eec DE-627 ger DE-627 rakwb eng TA401-492 TK1001-1841 Seungheon Chae verfasserin aut Can Pressure Data from Wearable Insole Devices Be Utilized to Estimate Low Back Moments for Exoskeleton Control System? 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study presents a machine learning model for predicting lumbar spine moments using data from low-cost sensors, with the ultimate aim of developing a control strategy for waist-active exoskeleton devices. The limitation of sparse features in low-cost insoles was addressed by leveraging a source model constructed based on data acquired from the high-precision Pedar-X device, employing a transfer learning technique. The model’s performance saw significant improvement through a training approach that incorporated high-precision commercial insole data and fine-tuning with low-cost insole data. In comparison to the conventional model, this method resulted in a noteworthy 7% enhancement in performance, achieving an rRMSE of approximately 12% and a correlation coefficient of 0.9 in lumbar joint moment prediction. If the model can demonstrate real-time efficacy and effectiveness across various operations in future applications, it holds substantial potential for deployment as an active exoskeleton device for the waist. human motion analysis lifting task machine learning L5/S1 joint torque work-related musculoskeletal disorders low-cost insole system Materials of engineering and construction. Mechanics of materials Production of electric energy or power. Powerplants. Central stations Ahnryul Choi verfasserin aut Jeehae Kang verfasserin aut Joung Hwan Mun verfasserin aut In Actuators MDPI AG, 2013 13(2024), 3, p 92 (DE-627)726491802 (DE-600)2682469-3 20760825 nnns volume:13 year:2024 number:3, p 92 https://doi.org/10.3390/act13030092 kostenfrei https://doaj.org/article/97dd9a4112264c698fef4a97686f8eec kostenfrei https://www.mdpi.com/2076-0825/13/3/92 kostenfrei https://doaj.org/toc/2076-0825 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2027 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 13 2024 3, p 92
language	English
source	In Actuators 13(2024), 3, p 92 volume:13 year:2024 number:3, p 92
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format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	human motion analysis lifting task machine learning L5/S1 joint torque work-related musculoskeletal disorders low-cost insole system Materials of engineering and construction. Mechanics of materials Production of electric energy or power. Powerplants. Central stations
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container_title	Actuators
authorswithroles_txt_mv	Seungheon Chae @@aut@@ Ahnryul Choi @@aut@@ Jeehae Kang @@aut@@ Joung Hwan Mun @@aut@@
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callnumber-first	T - Technology
author	Seungheon Chae
spellingShingle	Seungheon Chae misc TA401-492 misc TK1001-1841 misc human motion analysis misc lifting task misc machine learning misc L5/S1 joint torque misc work-related musculoskeletal disorders misc low-cost insole system misc Materials of engineering and construction. Mechanics of materials misc Production of electric energy or power. Powerplants. Central stations Can Pressure Data from Wearable Insole Devices Be Utilized to Estimate Low Back Moments for Exoskeleton Control System?
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topic_title	TA401-492 TK1001-1841 Can Pressure Data from Wearable Insole Devices Be Utilized to Estimate Low Back Moments for Exoskeleton Control System? human motion analysis lifting task machine learning L5/S1 joint torque work-related musculoskeletal disorders low-cost insole system
topic	misc TA401-492 misc TK1001-1841 misc human motion analysis misc lifting task misc machine learning misc L5/S1 joint torque misc work-related musculoskeletal disorders misc low-cost insole system misc Materials of engineering and construction. Mechanics of materials misc Production of electric energy or power. Powerplants. Central stations
topic_unstemmed	misc TA401-492 misc TK1001-1841 misc human motion analysis misc lifting task misc machine learning misc L5/S1 joint torque misc work-related musculoskeletal disorders misc low-cost insole system misc Materials of engineering and construction. Mechanics of materials misc Production of electric energy or power. Powerplants. Central stations
topic_browse	misc TA401-492 misc TK1001-1841 misc human motion analysis misc lifting task misc machine learning misc L5/S1 joint torque misc work-related musculoskeletal disorders misc low-cost insole system misc Materials of engineering and construction. Mechanics of materials misc Production of electric energy or power. Powerplants. Central stations
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title	Can Pressure Data from Wearable Insole Devices Be Utilized to Estimate Low Back Moments for Exoskeleton Control System?
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title_full	Can Pressure Data from Wearable Insole Devices Be Utilized to Estimate Low Back Moments for Exoskeleton Control System?
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title_sort	can pressure data from wearable insole devices be utilized to estimate low back moments for exoskeleton control system?
callnumber	TA401-492
title_auth	Can Pressure Data from Wearable Insole Devices Be Utilized to Estimate Low Back Moments for Exoskeleton Control System?
abstract	This study presents a machine learning model for predicting lumbar spine moments using data from low-cost sensors, with the ultimate aim of developing a control strategy for waist-active exoskeleton devices. The limitation of sparse features in low-cost insoles was addressed by leveraging a source model constructed based on data acquired from the high-precision Pedar-X device, employing a transfer learning technique. The model’s performance saw significant improvement through a training approach that incorporated high-precision commercial insole data and fine-tuning with low-cost insole data. In comparison to the conventional model, this method resulted in a noteworthy 7% enhancement in performance, achieving an rRMSE of approximately 12% and a correlation coefficient of 0.9 in lumbar joint moment prediction. If the model can demonstrate real-time efficacy and effectiveness across various operations in future applications, it holds substantial potential for deployment as an active exoskeleton device for the waist.
abstractGer	This study presents a machine learning model for predicting lumbar spine moments using data from low-cost sensors, with the ultimate aim of developing a control strategy for waist-active exoskeleton devices. The limitation of sparse features in low-cost insoles was addressed by leveraging a source model constructed based on data acquired from the high-precision Pedar-X device, employing a transfer learning technique. The model’s performance saw significant improvement through a training approach that incorporated high-precision commercial insole data and fine-tuning with low-cost insole data. In comparison to the conventional model, this method resulted in a noteworthy 7% enhancement in performance, achieving an rRMSE of approximately 12% and a correlation coefficient of 0.9 in lumbar joint moment prediction. If the model can demonstrate real-time efficacy and effectiveness across various operations in future applications, it holds substantial potential for deployment as an active exoskeleton device for the waist.
abstract_unstemmed	This study presents a machine learning model for predicting lumbar spine moments using data from low-cost sensors, with the ultimate aim of developing a control strategy for waist-active exoskeleton devices. The limitation of sparse features in low-cost insoles was addressed by leveraging a source model constructed based on data acquired from the high-precision Pedar-X device, employing a transfer learning technique. The model’s performance saw significant improvement through a training approach that incorporated high-precision commercial insole data and fine-tuning with low-cost insole data. In comparison to the conventional model, this method resulted in a noteworthy 7% enhancement in performance, achieving an rRMSE of approximately 12% and a correlation coefficient of 0.9 in lumbar joint moment prediction. If the model can demonstrate real-time efficacy and effectiveness across various operations in future applications, it holds substantial potential for deployment as an active exoskeleton device for the waist.
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container_issue	3, p 92
title_short	Can Pressure Data from Wearable Insole Devices Be Utilized to Estimate Low Back Moments for Exoskeleton Control System?
url	https://doi.org/10.3390/act13030092 https://doaj.org/article/97dd9a4112264c698fef4a97686f8eec https://www.mdpi.com/2076-0825/13/3/92 https://doaj.org/toc/2076-0825
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