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Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning

The study of human posture analysis and gait event detection from various types of inputs is a key contribution to the human life log. With the help of this research and technologies humans can save costs in terms of time and utility resources. In this paper we present a robust approach to human pos...

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Autores principales: Ghadi, Yazeed, Akhter, Israr, Alarfaj, Mohammed, Jalal, Ahmad, Kim, Kibum
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627229/
https://www.ncbi.nlm.nih.gov/pubmed/34901426
http://dx.doi.org/10.7717/peerj-cs.764
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author Ghadi, Yazeed
Akhter, Israr
Alarfaj, Mohammed
Jalal, Ahmad
Kim, Kibum
author_facet Ghadi, Yazeed
Akhter, Israr
Alarfaj, Mohammed
Jalal, Ahmad
Kim, Kibum
author_sort Ghadi, Yazeed
collection PubMed
description The study of human posture analysis and gait event detection from various types of inputs is a key contribution to the human life log. With the help of this research and technologies humans can save costs in terms of time and utility resources. In this paper we present a robust approach to human posture analysis and gait event detection from complex video-based data. For this, initially posture information, landmark information are extracted, and human 2D skeleton mesh are extracted, using this information set we reconstruct the human 2D to 3D model. Contextual features, namely, degrees of freedom over detected body parts, joint angle information, periodic and non-periodic motion, and human motion direction flow, are extracted. For features mining, we applied the rule-based features mining technique and, for gait event detection and classification, the deep learning-based CNN technique is applied over the mpii-video pose, the COCO, and the pose track datasets. For the mpii-video pose dataset, we achieved a human landmark detection mean accuracy of 87.09% and a gait event recognition mean accuracy of 90.90%. For the COCO dataset, we achieved a human landmark detection mean accuracy of 87.36% and a gait event recognition mean accuracy of 89.09%. For the pose track dataset, we achieved a human landmark detection mean accuracy of 87.72% and a gait event recognition mean accuracy of 88.18%. The proposed system performance shows a significant improvement compared to existing state-of-the-art frameworks.
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spelling pubmed-86272292021-12-10 Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning Ghadi, Yazeed Akhter, Israr Alarfaj, Mohammed Jalal, Ahmad Kim, Kibum PeerJ Comput Sci Human–Computer Interaction The study of human posture analysis and gait event detection from various types of inputs is a key contribution to the human life log. With the help of this research and technologies humans can save costs in terms of time and utility resources. In this paper we present a robust approach to human posture analysis and gait event detection from complex video-based data. For this, initially posture information, landmark information are extracted, and human 2D skeleton mesh are extracted, using this information set we reconstruct the human 2D to 3D model. Contextual features, namely, degrees of freedom over detected body parts, joint angle information, periodic and non-periodic motion, and human motion direction flow, are extracted. For features mining, we applied the rule-based features mining technique and, for gait event detection and classification, the deep learning-based CNN technique is applied over the mpii-video pose, the COCO, and the pose track datasets. For the mpii-video pose dataset, we achieved a human landmark detection mean accuracy of 87.09% and a gait event recognition mean accuracy of 90.90%. For the COCO dataset, we achieved a human landmark detection mean accuracy of 87.36% and a gait event recognition mean accuracy of 89.09%. For the pose track dataset, we achieved a human landmark detection mean accuracy of 87.72% and a gait event recognition mean accuracy of 88.18%. The proposed system performance shows a significant improvement compared to existing state-of-the-art frameworks. PeerJ Inc. 2021-11-19 /pmc/articles/PMC8627229/ /pubmed/34901426 http://dx.doi.org/10.7717/peerj-cs.764 Text en ©2021 Ghadi et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Human–Computer Interaction
Ghadi, Yazeed
Akhter, Israr
Alarfaj, Mohammed
Jalal, Ahmad
Kim, Kibum
Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning
title Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning
title_full Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning
title_fullStr Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning
title_full_unstemmed Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning
title_short Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning
title_sort syntactic model-based human body 3d reconstruction and event classification via association based features mining and deep learning
topic Human–Computer Interaction
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627229/
https://www.ncbi.nlm.nih.gov/pubmed/34901426
http://dx.doi.org/10.7717/peerj-cs.764
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