Sparse Representation for Classification of Posture in Bed

Simple item page
but.event.date25.04.2023cs
but.event.titleSTUDENT EEICT 2023cs
dc.contributor.authorMesárošová, Michaela
dc.contributor.authorMihálik, Ondrej
dc.date.accessioned2023-07-17T05:57:33Z
dc.date.available2023-07-17T05:57:33Z
dc.date.issued2023cs
dc.description.abstractRedundant dictionaries, also known as frames, offera non–orthogonal representation of signals, which leads to sparsityin their representative coefficients. As this approach providesmany advantageous properties it has been used in various applicationssuch as denoising, robust transmissions, segmentation,quantum theory and others. This paper investigates the possibilityof using sparse representation in classification, comparing theachieved results to other commonly used classifiers. The differentmethods were evaluated in a real-world classification task inwhich the position of a lying patient has to be deduced basedon the data provided by a pressure mattress of 30×11 sensors.The investigated method outperformed most of the commonlyused classifiers with accuracy exceeding 92%, while being lessdemanding on design and implementation complexity.en
dc.formattextcs
dc.format.extent101-104cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 29st Conference STUDENT EEICT 2023: Selected papers. s. 101-104. ISBN 978-80-214-6154-3cs
dc.identifier.doi10.13164/eeict.2023.101
dc.identifier.isbn978-80-214-6154-3
dc.identifier.issn2788-1334
dc.identifier.urihttp://hdl.handle.net/11012/210665
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 29st Conference STUDENT EEICT 2023: Selected papersen
dc.relation.urihttps://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdfcs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectsparse representationen
dc.subjectlinear regressionen
dc.subjectLASSO,redundant basisen
dc.subjectSRCen
dc.subjectclassificationen
dc.titleSparse Representation for Classification of Posture in Beden
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
101_EEICT_selected.pdf
Size:
774.62 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Student EEICT 2023: Selected Papers