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Towards Single Camera Human 3D-Kinematics
Markerless estimation of 3D Kinematics has the great potential to clinically diagnose and monitor movement disorders without referrals to expensive motion capture labs; however, current approaches are limited by performing multiple de-coupled steps to estimate the kinematics of a person from videos....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823525/ https://www.ncbi.nlm.nih.gov/pubmed/36616937 http://dx.doi.org/10.3390/s23010341 |
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author | Bittner, Marian Yang, Wei-Tse Zhang, Xucong Seth, Ajay van Gemert, Jan van der Helm, Frans C. T. |
author_facet | Bittner, Marian Yang, Wei-Tse Zhang, Xucong Seth, Ajay van Gemert, Jan van der Helm, Frans C. T. |
author_sort | Bittner, Marian |
collection | PubMed |
description | Markerless estimation of 3D Kinematics has the great potential to clinically diagnose and monitor movement disorders without referrals to expensive motion capture labs; however, current approaches are limited by performing multiple de-coupled steps to estimate the kinematics of a person from videos. Most current techniques work in a multi-step approach by first detecting the pose of the body and then fitting a musculoskeletal model to the data for accurate kinematic estimation. Errors in training data of the pose detection algorithms, model scaling, as well the requirement of multiple cameras limit the use of these techniques in a clinical setting. Our goal is to pave the way toward fast, easily applicable and accurate 3D kinematic estimation. To this end, we propose a novel approach for direct 3D human kinematic estimation D3KE from videos using deep neural networks. Our experiments demonstrate that the proposed end-to-end training is robust and outperforms 2D and 3D markerless motion capture based kinematic estimation pipelines in terms of joint angles error by a large margin (35% from 5.44 to 3.54 degrees). We show that D3KE is superior to the multi-step approach and can run at video framerate speeds. This technology shows the potential for clinical analysis from mobile devices in the future. |
format | Online Article Text |
id | pubmed-9823525 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98235252023-01-08 Towards Single Camera Human 3D-Kinematics Bittner, Marian Yang, Wei-Tse Zhang, Xucong Seth, Ajay van Gemert, Jan van der Helm, Frans C. T. Sensors (Basel) Article Markerless estimation of 3D Kinematics has the great potential to clinically diagnose and monitor movement disorders without referrals to expensive motion capture labs; however, current approaches are limited by performing multiple de-coupled steps to estimate the kinematics of a person from videos. Most current techniques work in a multi-step approach by first detecting the pose of the body and then fitting a musculoskeletal model to the data for accurate kinematic estimation. Errors in training data of the pose detection algorithms, model scaling, as well the requirement of multiple cameras limit the use of these techniques in a clinical setting. Our goal is to pave the way toward fast, easily applicable and accurate 3D kinematic estimation. To this end, we propose a novel approach for direct 3D human kinematic estimation D3KE from videos using deep neural networks. Our experiments demonstrate that the proposed end-to-end training is robust and outperforms 2D and 3D markerless motion capture based kinematic estimation pipelines in terms of joint angles error by a large margin (35% from 5.44 to 3.54 degrees). We show that D3KE is superior to the multi-step approach and can run at video framerate speeds. This technology shows the potential for clinical analysis from mobile devices in the future. MDPI 2022-12-28 /pmc/articles/PMC9823525/ /pubmed/36616937 http://dx.doi.org/10.3390/s23010341 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bittner, Marian Yang, Wei-Tse Zhang, Xucong Seth, Ajay van Gemert, Jan van der Helm, Frans C. T. Towards Single Camera Human 3D-Kinematics |
title | Towards Single Camera Human 3D-Kinematics |
title_full | Towards Single Camera Human 3D-Kinematics |
title_fullStr | Towards Single Camera Human 3D-Kinematics |
title_full_unstemmed | Towards Single Camera Human 3D-Kinematics |
title_short | Towards Single Camera Human 3D-Kinematics |
title_sort | towards single camera human 3d-kinematics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823525/ https://www.ncbi.nlm.nih.gov/pubmed/36616937 http://dx.doi.org/10.3390/s23010341 |
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