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Anthropometric Ratios for Lower-Body Detection Based on Deep Learning and Traditional Methods

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dc.contributor.authorJaruenpunyasak, Jermphiphut
dc.contributor.authorGarcía Seco de Herrera, Alba
dc.contributor.authorDuangsoithong, Rakkrit
dc.date.accessioned2025-03-27T09:28:44Z
dc.date.available2025-03-27T09:28:44Z
dc.date.issued2022-03-04
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en Applied Sciences (Switzerland) 12, n.º 5 (2022), está disponible en línea en el sitio web del editor: MDPI, https://doi.org/10.3390/APP12052678. The registered version of this article, first published in Applied Sciences (Switzerland) 12, no. 5 (2022), is available online on the publisher's website: MDPI, https://doi.org/10.3390/APP12052678.
dc.description.abstractLower-body detection can be useful in many applications, such as the detection of falling and injuries during exercises. However, it can be challenging to detect the lower-body, especially under various lighting and occlusion conditions. This paper presents a novel lower-body detection framework using proposed anthropometric ratios and compares the performance of deep learning (convolutional neural networks and OpenPose) and traditional detection methods. According to the results, the proposed framework helps to successfully detect the accurate boundaries of the lower-body under various illumination and occlusion conditions for lower-limb monitoring. The proposed framework of anthropometric ratios combined with convolutional neural networks (A-CNNs) also achieves high accuracy (90.14%), while the combination of anthropometric ratios and traditional techniques (A-Traditional) for lower-body detection shows satisfactory performance with an averaged accuracy (74.81%). Although the accuracy of OpenPose (95.82%) is higher than the A-CNNs for lower-body detection, the A-CNNs provides lower complexity than the OpenPose, which is advantageous for lower-body detection and implementation on monitoring systems.en
dc.description.versionversión publicada
dc.identifier.citationJermphiphut Jaruenpunyasak, Alba García Seco de Herrera, y Rakkrit Duangsoithong. «Anthropometric Ratios for Lower-Body Detection Based on Deep Learning and Traditional Methods». Applied Sciences (Switzerland) 12, n.º 5 (2022). https://doi.org/10.3390/APP12052678
dc.identifier.doihttps://doi.org/10.3390/APP12052678
dc.identifier.issn2076-3417
dc.identifier.urihttps://hdl.handle.net/20.500.14468/26374
dc.journal.issue5
dc.journal.titleApplied Sciences
dc.journal.volume12
dc.language.isoen
dc.publisherMDPI
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.subject.keywordsAnthropometric ratioen
dc.subject.keywordslower-body detectionen
dc.subject.keywordsdeep learningen
dc.subject.keywordsOpenPoseen
dc.titleAnthropometric Ratios for Lower-Body Detection Based on Deep Learning and Traditional Methodsen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication33e1cf81-6a46-4cc6-828f-1c0f2a7e7497
relation.isAuthorOfPublication.latestForDiscovery33e1cf81-6a46-4cc6-828f-1c0f2a7e7497
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