Comparing Posture Classification: A Human Lying Posture Pressure-Map Dataset

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dc.contributor.authorHusák, Michalcs
dc.contributor.authorMihálik, Ondrejcs
dc.contributor.authorArm, Jakubcs
dc.contributor.authorMesárošová, Michaelacs
dc.contributor.authorKaczmarczyk, Václavcs
dc.contributor.authorBradáč, Zdeněkcs
dc.coverage.issue1cs
dc.coverage.volume13cs
dc.date.accessioned2025-05-27T09:57:05Z
dc.date.available2025-05-27T09:57:05Z
dc.date.issued2025-04-11cs
dc.description.abstractWe discuss methods and algorithms for classifying the posture of a patient in their bed. The actual classification tasks are performed with a measurement chain including an in-house designed pressure mattress, a matrix of 30×11 sensing spots, a data concentrator, and a cloud-based service. Utilizing a survey of open-source datasets that facilitate such classifying operations, we designed a relevant experiment. A Human Lying Posture Pressure-Map dataset (HLPPDat) formed during the research is publicly available. Involving 20 subjects in 64 defined postures, the classification output was separated into four basic groups included prone posture. The data enabled us to analyze multiple classification methods developed with the state-of-the-art concepts of Machine Learning (ML), sparse representation, and artificial intelligence represented by Transfer Learning (TL). The analysis included both data measured and data experimentally corrupted in a manner that would most probably occur due to a partial measurement error. Regarding the techniques and options tested, feature extraction via the Histogram of Oriented Gradient (HOG) and the K-Nearest Neighbors (KNN) tools appeared to be the most beneficial, yielding an accuracy of over 99.5% in the leave-one-subject-out crossvalidation. The research confirmed that an accurate classification is feasible at a matrix sensor resolution markedly lower than the limits regularly presented in the literature. The system allows monitoring how long a bedridden person has remained in the same posture, and thus it has a potential to help prevent decubitus in both hospitals and the home care.en
dc.formattextcs
dc.format.extent65420-65437cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIEEE Access. 2025, vol. 13, issue 1, p. 65420-65437.en
dc.identifier.doi10.1109/ACCESS.2025.3559764cs
dc.identifier.issn2169-3536cs
dc.identifier.orcid0000-0002-8743-6776cs
dc.identifier.orcid0000-0001-7433-9275cs
dc.identifier.orcid0000-0001-8290-8255cs
dc.identifier.orcid0009-0005-1276-8388cs
dc.identifier.orcid0000-0001-5055-5619cs
dc.identifier.orcid0000-0002-7698-6959cs
dc.identifier.other197734cs
dc.identifier.researcheridD-8201-2012cs
dc.identifier.scopus57191708470cs
dc.identifier.scopus55907362000cs
dc.identifier.urihttps://hdl.handle.net/11012/251057
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofIEEE Accesscs
dc.relation.urihttps://ieeexplore.ieee.org/document/10963691cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2169-3536/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectClassificationen
dc.subjectmachine learningen
dc.subjectmedical monitoring systemen
dc.subjectpressure sensoren
dc.titleComparing Posture Classification: A Human Lying Posture Pressure-Map Dataseten
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-197734en
sync.item.dbtypeVAVen
sync.item.insts2025.05.27 11:57:05en
sync.item.modts2025.05.27 11:33:40en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav automatizace a měřicí technikycs
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