Algorithm based on one monocular video delivers highly valid and reliable gait parameters

Abstract Despite its paramount importance for manifold use cases (e.g., in the health care industry, sports, rehabilitation and fitness assessment), sufficiently valid and reliable gait parameter measurement is still limited to high-tech gait laboratories mostly. Here, we demonstrate the excellent v...

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Autores principales:	Arash Azhand, Sophie Rabe, Swantje Müller, Igor Sattler, Anika Heimann-Steinert
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6929d766ecb6485fa4c980c7114da863
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spelling	oai:doaj.org-article:6929d766ecb6485fa4c980c7114da8632021-12-02T15:39:59ZAlgorithm based on one monocular video delivers highly valid and reliable gait parameters10.1038/s41598-021-93530-z2045-2322https://doaj.org/article/6929d766ecb6485fa4c980c7114da8632021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93530-zhttps://doaj.org/toc/2045-2322Abstract Despite its paramount importance for manifold use cases (e.g., in the health care industry, sports, rehabilitation and fitness assessment), sufficiently valid and reliable gait parameter measurement is still limited to high-tech gait laboratories mostly. Here, we demonstrate the excellent validity and test–retest repeatability of a novel gait assessment system which is built upon modern convolutional neural networks to extract three-dimensional skeleton joints from monocular frontal-view videos of walking humans. The validity study is based on a comparison to the GAITRite pressure-sensitive walkway system. All measured gait parameters (gait speed, cadence, step length and step time) showed excellent concurrent validity for multiple walk trials at normal and fast gait speeds. The test–retest-repeatability is on the same level as the GAITRite system. In conclusion, we are convinced that our results can pave the way for cost, space and operationally effective gait analysis in broad mainstream applications. Most sensor-based systems are costly, must be operated by extensively trained personnel (e.g., motion capture systems) or—even if not quite as costly—still possess considerable complexity (e.g., wearable sensors). In contrast, a video sufficient for the assessment method presented here can be obtained by anyone, without much training, via a smartphone camera.Arash AzhandSophie RabeSwantje MüllerIgor SattlerAnika Heimann-SteinertNature 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 Arash Azhand Sophie Rabe Swantje Müller Igor Sattler Anika Heimann-Steinert Algorithm based on one monocular video delivers highly valid and reliable gait parameters
description	Abstract Despite its paramount importance for manifold use cases (e.g., in the health care industry, sports, rehabilitation and fitness assessment), sufficiently valid and reliable gait parameter measurement is still limited to high-tech gait laboratories mostly. Here, we demonstrate the excellent validity and test–retest repeatability of a novel gait assessment system which is built upon modern convolutional neural networks to extract three-dimensional skeleton joints from monocular frontal-view videos of walking humans. The validity study is based on a comparison to the GAITRite pressure-sensitive walkway system. All measured gait parameters (gait speed, cadence, step length and step time) showed excellent concurrent validity for multiple walk trials at normal and fast gait speeds. The test–retest-repeatability is on the same level as the GAITRite system. In conclusion, we are convinced that our results can pave the way for cost, space and operationally effective gait analysis in broad mainstream applications. Most sensor-based systems are costly, must be operated by extensively trained personnel (e.g., motion capture systems) or—even if not quite as costly—still possess considerable complexity (e.g., wearable sensors). In contrast, a video sufficient for the assessment method presented here can be obtained by anyone, without much training, via a smartphone camera.
format	article
author	Arash Azhand Sophie Rabe Swantje Müller Igor Sattler Anika Heimann-Steinert
author_facet	Arash Azhand Sophie Rabe Swantje Müller Igor Sattler Anika Heimann-Steinert
author_sort	Arash Azhand
title	Algorithm based on one monocular video delivers highly valid and reliable gait parameters
title_short	Algorithm based on one monocular video delivers highly valid and reliable gait parameters
title_full	Algorithm based on one monocular video delivers highly valid and reliable gait parameters
title_fullStr	Algorithm based on one monocular video delivers highly valid and reliable gait parameters
title_full_unstemmed	Algorithm based on one monocular video delivers highly valid and reliable gait parameters
title_sort	algorithm based on one monocular video delivers highly valid and reliable gait parameters
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6929d766ecb6485fa4c980c7114da863
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Algorithm based on one monocular video delivers highly valid and reliable gait parameters

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