Comparison of plugin and redundant marker sets to analyze gait kinematics between different populations

Background Gait model consists of a marker set and a segment pose estimation algorithm. Plugin marker set and inverse kinematic algorithm (IK.) are prevalent in gait analysis, especially musculoskeletal motion analysis. Adding extra markers for the plugin marker set could increase the robustness to...
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Autor*in:	Ji, Run [verfasserIn] Lee, Wayne Yuk-wai Guan, Xinyu Yan, Bin Yang, Lei Yang, Jiemeng Wang, Ling Tao, Chunjing Kuai, Shengzheng Fan, Yubo

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Inverse kinematic algorithm Kinematics Marker set Gait model

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Biomedical engineering online - London : BioMed Central, 2002, 22(2023), 1 vom: 12. Dez.
Übergeordnetes Werk:	volume:22 ; year:2023 ; number:1 ; day:12 ; month:12

Links:	Volltext

DOI / URN:	10.1186/s12938-023-01177-w

Katalog-ID:	SPR054060613

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520			\|a Background Gait model consists of a marker set and a segment pose estimation algorithm. Plugin marker set and inverse kinematic algorithm (IK.) are prevalent in gait analysis, especially musculoskeletal motion analysis. Adding extra markers for the plugin marker set could increase the robustness to marker misplacement, motion artifacts, and even markers occlusion. However, how the different marker sets affect the gait analysis's kinematic output is unclear. Therefore, this study aims to investigate the effect of marker sets on the kinematic output during level walking in different populations. Results In all three planes, there are significant differences (P < 0.05) between marker sets in some kinematic variables at the hip, knee, and ankle. In different populations, the kinematic variables that show significant differences varied. When comparing the kinematic differences between populations using the two marker sets separately, the range of motion (ROM) of hip flexion was only found to be a significant difference using the redundant marker set, while the peak internal rotation at the knee was only found a significant difference using plugin marker set. In addition, the redundant marker set shows less intra-subject variation than the plugin marker set. Conclusion The findings in this study demonstrate the importance of marker set selection since it could change the result when comparing the kinematic differences between populations. Therefore, it is essential to increase the caution in explaining the result when using different marker sets. It is crucial to use the same marker set, and the redundant marker set might be a better choice for gait analysis.
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allfields	10.1186/s12938-023-01177-w doi (DE-627)SPR054060613 (SPR)s12938-023-01177-w-e DE-627 ger DE-627 rakwb eng Ji, Run verfasserin aut Comparison of plugin and redundant marker sets to analyze gait kinematics between different populations 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Gait model consists of a marker set and a segment pose estimation algorithm. Plugin marker set and inverse kinematic algorithm (IK.) are prevalent in gait analysis, especially musculoskeletal motion analysis. Adding extra markers for the plugin marker set could increase the robustness to marker misplacement, motion artifacts, and even markers occlusion. However, how the different marker sets affect the gait analysis's kinematic output is unclear. Therefore, this study aims to investigate the effect of marker sets on the kinematic output during level walking in different populations. Results In all three planes, there are significant differences (P < 0.05) between marker sets in some kinematic variables at the hip, knee, and ankle. In different populations, the kinematic variables that show significant differences varied. When comparing the kinematic differences between populations using the two marker sets separately, the range of motion (ROM) of hip flexion was only found to be a significant difference using the redundant marker set, while the peak internal rotation at the knee was only found a significant difference using plugin marker set. In addition, the redundant marker set shows less intra-subject variation than the plugin marker set. Conclusion The findings in this study demonstrate the importance of marker set selection since it could change the result when comparing the kinematic differences between populations. Therefore, it is essential to increase the caution in explaining the result when using different marker sets. It is crucial to use the same marker set, and the redundant marker set might be a better choice for gait analysis. Inverse kinematic algorithm (dpeaa)DE-He213 Kinematics (dpeaa)DE-He213 Marker set (dpeaa)DE-He213 Gait model (dpeaa)DE-He213 Lee, Wayne Yuk-wai aut Guan, Xinyu aut Yan, Bin aut Yang, Lei aut Yang, Jiemeng aut Wang, Ling aut Tao, Chunjing aut Kuai, Shengzheng aut Fan, Yubo aut Enthalten in Biomedical engineering online London : BioMed Central, 2002 22(2023), 1 vom: 12. Dez. (DE-627)35210547X (DE-600)2084374-4 1475-925X nnns volume:22 year:2023 number:1 day:12 month:12 https://dx.doi.org/10.1186/s12938-023-01177-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 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 2023 1 12 12
spelling	10.1186/s12938-023-01177-w doi (DE-627)SPR054060613 (SPR)s12938-023-01177-w-e DE-627 ger DE-627 rakwb eng Ji, Run verfasserin aut Comparison of plugin and redundant marker sets to analyze gait kinematics between different populations 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Gait model consists of a marker set and a segment pose estimation algorithm. Plugin marker set and inverse kinematic algorithm (IK.) are prevalent in gait analysis, especially musculoskeletal motion analysis. Adding extra markers for the plugin marker set could increase the robustness to marker misplacement, motion artifacts, and even markers occlusion. However, how the different marker sets affect the gait analysis's kinematic output is unclear. Therefore, this study aims to investigate the effect of marker sets on the kinematic output during level walking in different populations. Results In all three planes, there are significant differences (P < 0.05) between marker sets in some kinematic variables at the hip, knee, and ankle. In different populations, the kinematic variables that show significant differences varied. When comparing the kinematic differences between populations using the two marker sets separately, the range of motion (ROM) of hip flexion was only found to be a significant difference using the redundant marker set, while the peak internal rotation at the knee was only found a significant difference using plugin marker set. In addition, the redundant marker set shows less intra-subject variation than the plugin marker set. Conclusion The findings in this study demonstrate the importance of marker set selection since it could change the result when comparing the kinematic differences between populations. Therefore, it is essential to increase the caution in explaining the result when using different marker sets. It is crucial to use the same marker set, and the redundant marker set might be a better choice for gait analysis. Inverse kinematic algorithm (dpeaa)DE-He213 Kinematics (dpeaa)DE-He213 Marker set (dpeaa)DE-He213 Gait model (dpeaa)DE-He213 Lee, Wayne Yuk-wai aut Guan, Xinyu aut Yan, Bin aut Yang, Lei aut Yang, Jiemeng aut Wang, Ling aut Tao, Chunjing aut Kuai, Shengzheng aut Fan, Yubo aut Enthalten in Biomedical engineering online London : BioMed Central, 2002 22(2023), 1 vom: 12. Dez. (DE-627)35210547X (DE-600)2084374-4 1475-925X nnns volume:22 year:2023 number:1 day:12 month:12 https://dx.doi.org/10.1186/s12938-023-01177-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 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 2023 1 12 12
allfields_unstemmed	10.1186/s12938-023-01177-w doi (DE-627)SPR054060613 (SPR)s12938-023-01177-w-e DE-627 ger DE-627 rakwb eng Ji, Run verfasserin aut Comparison of plugin and redundant marker sets to analyze gait kinematics between different populations 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Gait model consists of a marker set and a segment pose estimation algorithm. Plugin marker set and inverse kinematic algorithm (IK.) are prevalent in gait analysis, especially musculoskeletal motion analysis. Adding extra markers for the plugin marker set could increase the robustness to marker misplacement, motion artifacts, and even markers occlusion. However, how the different marker sets affect the gait analysis's kinematic output is unclear. Therefore, this study aims to investigate the effect of marker sets on the kinematic output during level walking in different populations. Results In all three planes, there are significant differences (P < 0.05) between marker sets in some kinematic variables at the hip, knee, and ankle. In different populations, the kinematic variables that show significant differences varied. When comparing the kinematic differences between populations using the two marker sets separately, the range of motion (ROM) of hip flexion was only found to be a significant difference using the redundant marker set, while the peak internal rotation at the knee was only found a significant difference using plugin marker set. In addition, the redundant marker set shows less intra-subject variation than the plugin marker set. Conclusion The findings in this study demonstrate the importance of marker set selection since it could change the result when comparing the kinematic differences between populations. Therefore, it is essential to increase the caution in explaining the result when using different marker sets. It is crucial to use the same marker set, and the redundant marker set might be a better choice for gait analysis. Inverse kinematic algorithm (dpeaa)DE-He213 Kinematics (dpeaa)DE-He213 Marker set (dpeaa)DE-He213 Gait model (dpeaa)DE-He213 Lee, Wayne Yuk-wai aut Guan, Xinyu aut Yan, Bin aut Yang, Lei aut Yang, Jiemeng aut Wang, Ling aut Tao, Chunjing aut Kuai, Shengzheng aut Fan, Yubo aut Enthalten in Biomedical engineering online London : BioMed Central, 2002 22(2023), 1 vom: 12. Dez. (DE-627)35210547X (DE-600)2084374-4 1475-925X nnns volume:22 year:2023 number:1 day:12 month:12 https://dx.doi.org/10.1186/s12938-023-01177-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 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 2023 1 12 12
allfieldsGer	10.1186/s12938-023-01177-w doi (DE-627)SPR054060613 (SPR)s12938-023-01177-w-e DE-627 ger DE-627 rakwb eng Ji, Run verfasserin aut Comparison of plugin and redundant marker sets to analyze gait kinematics between different populations 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Gait model consists of a marker set and a segment pose estimation algorithm. Plugin marker set and inverse kinematic algorithm (IK.) are prevalent in gait analysis, especially musculoskeletal motion analysis. Adding extra markers for the plugin marker set could increase the robustness to marker misplacement, motion artifacts, and even markers occlusion. However, how the different marker sets affect the gait analysis's kinematic output is unclear. Therefore, this study aims to investigate the effect of marker sets on the kinematic output during level walking in different populations. Results In all three planes, there are significant differences (P < 0.05) between marker sets in some kinematic variables at the hip, knee, and ankle. In different populations, the kinematic variables that show significant differences varied. When comparing the kinematic differences between populations using the two marker sets separately, the range of motion (ROM) of hip flexion was only found to be a significant difference using the redundant marker set, while the peak internal rotation at the knee was only found a significant difference using plugin marker set. In addition, the redundant marker set shows less intra-subject variation than the plugin marker set. Conclusion The findings in this study demonstrate the importance of marker set selection since it could change the result when comparing the kinematic differences between populations. Therefore, it is essential to increase the caution in explaining the result when using different marker sets. It is crucial to use the same marker set, and the redundant marker set might be a better choice for gait analysis. Inverse kinematic algorithm (dpeaa)DE-He213 Kinematics (dpeaa)DE-He213 Marker set (dpeaa)DE-He213 Gait model (dpeaa)DE-He213 Lee, Wayne Yuk-wai aut Guan, Xinyu aut Yan, Bin aut Yang, Lei aut Yang, Jiemeng aut Wang, Ling aut Tao, Chunjing aut Kuai, Shengzheng aut Fan, Yubo aut Enthalten in Biomedical engineering online London : BioMed Central, 2002 22(2023), 1 vom: 12. Dez. (DE-627)35210547X (DE-600)2084374-4 1475-925X nnns volume:22 year:2023 number:1 day:12 month:12 https://dx.doi.org/10.1186/s12938-023-01177-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 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 2023 1 12 12
allfieldsSound	10.1186/s12938-023-01177-w doi (DE-627)SPR054060613 (SPR)s12938-023-01177-w-e DE-627 ger DE-627 rakwb eng Ji, Run verfasserin aut Comparison of plugin and redundant marker sets to analyze gait kinematics between different populations 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Background Gait model consists of a marker set and a segment pose estimation algorithm. Plugin marker set and inverse kinematic algorithm (IK.) are prevalent in gait analysis, especially musculoskeletal motion analysis. Adding extra markers for the plugin marker set could increase the robustness to marker misplacement, motion artifacts, and even markers occlusion. However, how the different marker sets affect the gait analysis's kinematic output is unclear. Therefore, this study aims to investigate the effect of marker sets on the kinematic output during level walking in different populations. Results In all three planes, there are significant differences (P < 0.05) between marker sets in some kinematic variables at the hip, knee, and ankle. In different populations, the kinematic variables that show significant differences varied. When comparing the kinematic differences between populations using the two marker sets separately, the range of motion (ROM) of hip flexion was only found to be a significant difference using the redundant marker set, while the peak internal rotation at the knee was only found a significant difference using plugin marker set. In addition, the redundant marker set shows less intra-subject variation than the plugin marker set. Conclusion The findings in this study demonstrate the importance of marker set selection since it could change the result when comparing the kinematic differences between populations. Therefore, it is essential to increase the caution in explaining the result when using different marker sets. It is crucial to use the same marker set, and the redundant marker set might be a better choice for gait analysis. Inverse kinematic algorithm (dpeaa)DE-He213 Kinematics (dpeaa)DE-He213 Marker set (dpeaa)DE-He213 Gait model (dpeaa)DE-He213 Lee, Wayne Yuk-wai aut Guan, Xinyu aut Yan, Bin aut Yang, Lei aut Yang, Jiemeng aut Wang, Ling aut Tao, Chunjing aut Kuai, Shengzheng aut Fan, Yubo aut Enthalten in Biomedical engineering online London : BioMed Central, 2002 22(2023), 1 vom: 12. Dez. (DE-627)35210547X (DE-600)2084374-4 1475-925X nnns volume:22 year:2023 number:1 day:12 month:12 https://dx.doi.org/10.1186/s12938-023-01177-w kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 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_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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 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 2023 1 12 12
language	English
source	Enthalten in Biomedical engineering online 22(2023), 1 vom: 12. Dez. volume:22 year:2023 number:1 day:12 month:12
sourceStr	Enthalten in Biomedical engineering online 22(2023), 1 vom: 12. Dez. volume:22 year:2023 number:1 day:12 month:12
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Inverse kinematic algorithm Kinematics Marker set Gait model
isfreeaccess_bool	true
container_title	Biomedical engineering online
authorswithroles_txt_mv	Ji, Run @@aut@@ Lee, Wayne Yuk-wai @@aut@@ Guan, Xinyu @@aut@@ Yan, Bin @@aut@@ Yang, Lei @@aut@@ Yang, Jiemeng @@aut@@ Wang, Ling @@aut@@ Tao, Chunjing @@aut@@ Kuai, Shengzheng @@aut@@ Fan, Yubo @@aut@@
publishDateDaySort_date	2023-12-12T00:00:00Z
hierarchy_top_id	35210547X
id	SPR054060613
language_de	englisch
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When comparing the kinematic differences between populations using the two marker sets separately, the range of motion (ROM) of hip flexion was only found to be a significant difference using the redundant marker set, while the peak internal rotation at the knee was only found a significant difference using plugin marker set. In addition, the redundant marker set shows less intra-subject variation than the plugin marker set. Conclusion The findings in this study demonstrate the importance of marker set selection since it could change the result when comparing the kinematic differences between populations. Therefore, it is essential to increase the caution in explaining the result when using different marker sets. 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author	Ji, Run
spellingShingle	Ji, Run misc Inverse kinematic algorithm misc Kinematics misc Marker set misc Gait model Comparison of plugin and redundant marker sets to analyze gait kinematics between different populations
authorStr	Ji, Run
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topic_title	Comparison of plugin and redundant marker sets to analyze gait kinematics between different populations Inverse kinematic algorithm (dpeaa)DE-He213 Kinematics (dpeaa)DE-He213 Marker set (dpeaa)DE-He213 Gait model (dpeaa)DE-He213
topic	misc Inverse kinematic algorithm misc Kinematics misc Marker set misc Gait model
topic_unstemmed	misc Inverse kinematic algorithm misc Kinematics misc Marker set misc Gait model
topic_browse	misc Inverse kinematic algorithm misc Kinematics misc Marker set misc Gait model
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title	Comparison of plugin and redundant marker sets to analyze gait kinematics between different populations
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title_full	Comparison of plugin and redundant marker sets to analyze gait kinematics between different populations
author_sort	Ji, Run
journal	Biomedical engineering online
journalStr	Biomedical engineering online
lang_code	eng
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author_browse	Ji, Run Lee, Wayne Yuk-wai Guan, Xinyu Yan, Bin Yang, Lei Yang, Jiemeng Wang, Ling Tao, Chunjing Kuai, Shengzheng Fan, Yubo
container_volume	22
format_se	Elektronische Aufsätze
author-letter	Ji, Run
doi_str_mv	10.1186/s12938-023-01177-w
title_sort	comparison of plugin and redundant marker sets to analyze gait kinematics between different populations
title_auth	Comparison of plugin and redundant marker sets to analyze gait kinematics between different populations
abstract	Background Gait model consists of a marker set and a segment pose estimation algorithm. Plugin marker set and inverse kinematic algorithm (IK.) are prevalent in gait analysis, especially musculoskeletal motion analysis. Adding extra markers for the plugin marker set could increase the robustness to marker misplacement, motion artifacts, and even markers occlusion. However, how the different marker sets affect the gait analysis's kinematic output is unclear. Therefore, this study aims to investigate the effect of marker sets on the kinematic output during level walking in different populations. Results In all three planes, there are significant differences (P < 0.05) between marker sets in some kinematic variables at the hip, knee, and ankle. In different populations, the kinematic variables that show significant differences varied. When comparing the kinematic differences between populations using the two marker sets separately, the range of motion (ROM) of hip flexion was only found to be a significant difference using the redundant marker set, while the peak internal rotation at the knee was only found a significant difference using plugin marker set. In addition, the redundant marker set shows less intra-subject variation than the plugin marker set. Conclusion The findings in this study demonstrate the importance of marker set selection since it could change the result when comparing the kinematic differences between populations. Therefore, it is essential to increase the caution in explaining the result when using different marker sets. It is crucial to use the same marker set, and the redundant marker set might be a better choice for gait analysis. © The Author(s) 2023
abstractGer	Background Gait model consists of a marker set and a segment pose estimation algorithm. Plugin marker set and inverse kinematic algorithm (IK.) are prevalent in gait analysis, especially musculoskeletal motion analysis. Adding extra markers for the plugin marker set could increase the robustness to marker misplacement, motion artifacts, and even markers occlusion. However, how the different marker sets affect the gait analysis's kinematic output is unclear. Therefore, this study aims to investigate the effect of marker sets on the kinematic output during level walking in different populations. Results In all three planes, there are significant differences (P < 0.05) between marker sets in some kinematic variables at the hip, knee, and ankle. In different populations, the kinematic variables that show significant differences varied. When comparing the kinematic differences between populations using the two marker sets separately, the range of motion (ROM) of hip flexion was only found to be a significant difference using the redundant marker set, while the peak internal rotation at the knee was only found a significant difference using plugin marker set. In addition, the redundant marker set shows less intra-subject variation than the plugin marker set. Conclusion The findings in this study demonstrate the importance of marker set selection since it could change the result when comparing the kinematic differences between populations. Therefore, it is essential to increase the caution in explaining the result when using different marker sets. It is crucial to use the same marker set, and the redundant marker set might be a better choice for gait analysis. © The Author(s) 2023
abstract_unstemmed	Background Gait model consists of a marker set and a segment pose estimation algorithm. Plugin marker set and inverse kinematic algorithm (IK.) are prevalent in gait analysis, especially musculoskeletal motion analysis. Adding extra markers for the plugin marker set could increase the robustness to marker misplacement, motion artifacts, and even markers occlusion. However, how the different marker sets affect the gait analysis's kinematic output is unclear. Therefore, this study aims to investigate the effect of marker sets on the kinematic output during level walking in different populations. Results In all three planes, there are significant differences (P < 0.05) between marker sets in some kinematic variables at the hip, knee, and ankle. In different populations, the kinematic variables that show significant differences varied. When comparing the kinematic differences between populations using the two marker sets separately, the range of motion (ROM) of hip flexion was only found to be a significant difference using the redundant marker set, while the peak internal rotation at the knee was only found a significant difference using plugin marker set. In addition, the redundant marker set shows less intra-subject variation than the plugin marker set. Conclusion The findings in this study demonstrate the importance of marker set selection since it could change the result when comparing the kinematic differences between populations. Therefore, it is essential to increase the caution in explaining the result when using different marker sets. It is crucial to use the same marker set, and the redundant marker set might be a better choice for gait analysis. © The Author(s) 2023
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title_short	Comparison of plugin and redundant marker sets to analyze gait kinematics between different populations
url	https://dx.doi.org/10.1186/s12938-023-01177-w
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author2	Lee, Wayne Yuk-wai Guan, Xinyu Yan, Bin Yang, Lei Yang, Jiemeng Wang, Ling Tao, Chunjing Kuai, Shengzheng Fan, Yubo
author2Str	Lee, Wayne Yuk-wai Guan, Xinyu Yan, Bin Yang, Lei Yang, Jiemeng Wang, Ling Tao, Chunjing Kuai, Shengzheng Fan, Yubo
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doi_str	10.1186/s12938-023-01177-w
up_date	2024-07-03T23:43:57.645Z
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Comparison of plugin and redundant marker sets to analyze gait kinematics between different populations

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