Detail publikačního výsledku

In-Bed Posture Classification Based on Sparse Representation in Redundant Dictionaries

MIHÁLIK, O.; SÝKORA, T.; HUSÁK, M.; FIEDLER, P.

Originální název

In-Bed Posture Classification Based on Sparse Representation in Redundant Dictionaries

Anglický název

In-Bed Posture Classification Based on Sparse Representation in Redundant Dictionaries

Druh

Stať ve sborníku v databázi WoS či Scopus

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.

Anglický 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

Klíčová slova v angličtině

sparse representation, redundant dictionary, SRC, posture classification, denoising

Autoři

MIHÁLIK, O.; SÝKORA, T.; HUSÁK, M.; FIEDLER, P.

Rok RIV

2023

Vydáno

20.05.2022

Nakladatel

Elsevier

Kniha

17th IFAC Conference on Programmable Devices and Embedded Systems – PDeS 2022.

ISSN

2405-8963

Periodikum

IFAC-PapersOnLine

Svazek

55

Číslo

4

Stát

Spojené království Velké Británie a Severního Irska

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",
  volume="55",
  number="4",
  pages="374--379",
  publisher="Elsevier",
  doi="10.1016/j.ifacol.2022.06.062",
  issn="2405-8971",
  url="https://doi.org/10.1016/j.ifacol.2022.06.062"
}

Dokumenty

Mihalik_PDeS_2022
Mihalik_PDeS_2022_accepted