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MIHÁLIK, O. SÝKORA, T. HUSÁK, M. FIEDLER, P.
Originální název
In-Bed Posture Classification Based on Sparse Representation in Redundant Dictionaries
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
Non-orthogonal signal representation using redundant dictionaries gradually gained popularity over the last decades. Sparse methods find major application in signal denoising, audio declipping, time-frequency analysis, and classification, to name a few. This paper is inspired by the exceptional results of sparse representation classification originally suggested for face recognition. We compare the method to other common classifiers using simulated as well as real datasets. In the latter the proposed method is tested with real pressure data from a bed equipped with a matrix of 30×11 pressure sensors. Here the method outperforms standard classification methods (surpassing 91 % accuracy) without need of parameter selection or special user’s skills. Furthermore it offers a means of dealing with occlusions, whose results are presented as well.
Klíčová slova
sparse representation, redundant dictionary, SRC, posture classification, denoising
Autoři
MIHÁLIK, O.; SÝKORA, T.; HUSÁK, M.; FIEDLER, P.
Vydáno
20. 5. 2022
Nakladatel
Elsevier
ISSN
2405-8963
Periodikum
IFAC-PapersOnLine (ELSEVIER)
Ročník
55
Číslo
4
Stát
Nizozemsko
Strany od
374
Strany do
379
Strany počet
6
URL
https://doi.org/10.1016/j.ifacol.2022.06.062
Plný text v Digitální knihovně
http://hdl.handle.net/11012/208166
BibTex
@inproceedings{BUT177806, author="Ondrej {Mihálik} and Tomáš {Sýkora} and Michal {Husák} and Petr {Fiedler}", title="In-Bed Posture Classification Based on Sparse Representation in Redundant Dictionaries", booktitle="17th IFAC Conference on Programmable Devices and Embedded Systems – PDeS 2022.", year="2022", journal="IFAC-PapersOnLine (ELSEVIER)", volume="55", number="4", pages="374--379", publisher="Elsevier", doi="10.1016/j.ifacol.2022.06.062", issn="2405-8963", url="https://doi.org/10.1016/j.ifacol.2022.06.062" }
Dokumenty
Mihalik_PDeS_2022.pdf