Quantitative gait analysis of idiopathic normal pressure hydrocephalus using deep learning algorithms on monocular videos

Abstract A vision-based gait analysis method using monocular videos was proposed to estimate temporo-spatial gait parameters by leveraging deep learning algorithms. This study aimed to validate vision-based gait analysis using GAITRite as the reference system and analyze relationships between Fronta...

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Autores principales:	Sungmoon Jeong, Hosang Yu, Jaechan Park, Kyunghun Kang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/19d728098b0b4742bc4b1593a9301f02
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spelling	oai:doaj.org-article:19d728098b0b4742bc4b1593a9301f022021-12-02T14:58:20ZQuantitative gait analysis of idiopathic normal pressure hydrocephalus using deep learning algorithms on monocular videos10.1038/s41598-021-90524-92045-2322https://doaj.org/article/19d728098b0b4742bc4b1593a9301f022021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90524-9https://doaj.org/toc/2045-2322Abstract A vision-based gait analysis method using monocular videos was proposed to estimate temporo-spatial gait parameters by leveraging deep learning algorithms. This study aimed to validate vision-based gait analysis using GAITRite as the reference system and analyze relationships between Frontal Assessment Battery (FAB) scores and gait variability measured by vision-based gait analysis in idiopathic normal pressure hydrocephalus (INPH) patients. Gait data from 46 patients were simultaneously collected from the vision-based system utilizing deep learning algorithms and the GAITRite system. There was a strong correlation in 11 gait parameters between our vision-based gait analysis method and the GAITRite gait analysis system. Our results also demonstrated excellent agreement between the two measurement systems for all parameters except stride time variability after the cerebrospinal fluid tap test. Our data showed that stride time and stride length variability measured by the vision-based gait analysis system were correlated with FAB scores. Vision-based gait analysis utilizing deep learning algorithms can provide comparable data to GAITRite when assessing gait dysfunction in INPH. Frontal lobe functions may be associated with gait variability measurements using vision-based gait analysis for INPH patients.Sungmoon JeongHosang YuJaechan ParkKyunghun KangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sungmoon Jeong Hosang Yu Jaechan Park Kyunghun Kang Quantitative gait analysis of idiopathic normal pressure hydrocephalus using deep learning algorithms on monocular videos
description	Abstract A vision-based gait analysis method using monocular videos was proposed to estimate temporo-spatial gait parameters by leveraging deep learning algorithms. This study aimed to validate vision-based gait analysis using GAITRite as the reference system and analyze relationships between Frontal Assessment Battery (FAB) scores and gait variability measured by vision-based gait analysis in idiopathic normal pressure hydrocephalus (INPH) patients. Gait data from 46 patients were simultaneously collected from the vision-based system utilizing deep learning algorithms and the GAITRite system. There was a strong correlation in 11 gait parameters between our vision-based gait analysis method and the GAITRite gait analysis system. Our results also demonstrated excellent agreement between the two measurement systems for all parameters except stride time variability after the cerebrospinal fluid tap test. Our data showed that stride time and stride length variability measured by the vision-based gait analysis system were correlated with FAB scores. Vision-based gait analysis utilizing deep learning algorithms can provide comparable data to GAITRite when assessing gait dysfunction in INPH. Frontal lobe functions may be associated with gait variability measurements using vision-based gait analysis for INPH patients.
format	article
author	Sungmoon Jeong Hosang Yu Jaechan Park Kyunghun Kang
author_facet	Sungmoon Jeong Hosang Yu Jaechan Park Kyunghun Kang
author_sort	Sungmoon Jeong
title	Quantitative gait analysis of idiopathic normal pressure hydrocephalus using deep learning algorithms on monocular videos
title_short	Quantitative gait analysis of idiopathic normal pressure hydrocephalus using deep learning algorithms on monocular videos
title_full	Quantitative gait analysis of idiopathic normal pressure hydrocephalus using deep learning algorithms on monocular videos
title_fullStr	Quantitative gait analysis of idiopathic normal pressure hydrocephalus using deep learning algorithms on monocular videos
title_full_unstemmed	Quantitative gait analysis of idiopathic normal pressure hydrocephalus using deep learning algorithms on monocular videos
title_sort	quantitative gait analysis of idiopathic normal pressure hydrocephalus using deep learning algorithms on monocular videos
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/19d728098b0b4742bc4b1593a9301f02
work_keys_str_mv	AT sungmoonjeong quantitativegaitanalysisofidiopathicnormalpressurehydrocephalususingdeeplearningalgorithmsonmonocularvideos AT hosangyu quantitativegaitanalysisofidiopathicnormalpressurehydrocephalususingdeeplearningalgorithmsonmonocularvideos AT jaechanpark quantitativegaitanalysisofidiopathicnormalpressurehydrocephalususingdeeplearningalgorithmsonmonocularvideos AT kyunghunkang quantitativegaitanalysisofidiopathicnormalpressurehydrocephalususingdeeplearningalgorithmsonmonocularvideos
_version_	1718389248797704192

Quantitative gait analysis of idiopathic normal pressure hydrocephalus using deep learning algorithms on monocular videos

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