Deep neural networks enable quantitative movement analysis using single-camera videos

In the context of diseases impairing movement, quantitative assessment of motion is critical to medical decision-making but is currently possible only with expensive motion capture systems and trained personnel. Here, the authors present a method for predicting clinically relevant motion parameters...

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Autores principales:	Łukasz Kidziński, Bryan Yang, Jennifer L. Hicks, Apoorva Rajagopal, Scott L. Delp, Michael H. Schwartz
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/16ae278becf743e49bd54eccf9afc9a2
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id	oai:doaj.org-article:16ae278becf743e49bd54eccf9afc9a2
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spelling	oai:doaj.org-article:16ae278becf743e49bd54eccf9afc9a22021-12-02T15:08:24ZDeep neural networks enable quantitative movement analysis using single-camera videos10.1038/s41467-020-17807-z2041-1723https://doaj.org/article/16ae278becf743e49bd54eccf9afc9a22020-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17807-zhttps://doaj.org/toc/2041-1723In the context of diseases impairing movement, quantitative assessment of motion is critical to medical decision-making but is currently possible only with expensive motion capture systems and trained personnel. Here, the authors present a method for predicting clinically relevant motion parameters from an ordinary video of a patient.Łukasz KidzińskiBryan YangJennifer L. HicksApoorva RajagopalScott L. DelpMichael H. SchwartzNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Łukasz Kidziński Bryan Yang Jennifer L. Hicks Apoorva Rajagopal Scott L. Delp Michael H. Schwartz Deep neural networks enable quantitative movement analysis using single-camera videos
description	In the context of diseases impairing movement, quantitative assessment of motion is critical to medical decision-making but is currently possible only with expensive motion capture systems and trained personnel. Here, the authors present a method for predicting clinically relevant motion parameters from an ordinary video of a patient.
format	article
author	Łukasz Kidziński Bryan Yang Jennifer L. Hicks Apoorva Rajagopal Scott L. Delp Michael H. Schwartz
author_facet	Łukasz Kidziński Bryan Yang Jennifer L. Hicks Apoorva Rajagopal Scott L. Delp Michael H. Schwartz
author_sort	Łukasz Kidziński
title	Deep neural networks enable quantitative movement analysis using single-camera videos
title_short	Deep neural networks enable quantitative movement analysis using single-camera videos
title_full	Deep neural networks enable quantitative movement analysis using single-camera videos
title_fullStr	Deep neural networks enable quantitative movement analysis using single-camera videos
title_full_unstemmed	Deep neural networks enable quantitative movement analysis using single-camera videos
title_sort	deep neural networks enable quantitative movement analysis using single-camera videos
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/16ae278becf743e49bd54eccf9afc9a2
work_keys_str_mv	AT łukaszkidzinski deepneuralnetworksenablequantitativemovementanalysisusingsinglecameravideos AT bryanyang deepneuralnetworksenablequantitativemovementanalysisusingsinglecameravideos AT jenniferlhicks deepneuralnetworksenablequantitativemovementanalysisusingsinglecameravideos AT apoorvarajagopal deepneuralnetworksenablequantitativemovementanalysisusingsinglecameravideos AT scottldelp deepneuralnetworksenablequantitativemovementanalysisusingsinglecameravideos AT michaelhschwartz deepneuralnetworksenablequantitativemovementanalysisusingsinglecameravideos
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Deep neural networks enable quantitative movement analysis using single-camera videos

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