Detail publikace

Sparse Representation for Classification of Posture in Bed

MESÁROŠOVÁ, M. MIHÁLIK, O.

Originální název

Sparse Representation for Classification of Posture in Bed

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

Redundant dictionaries, also known as frames, offer a non–orthogonal representation of signals, which leads to sparsity in their representative coefficients. As this approach provides many advantageous properties it has been used in various applications such as denoising, robust transmissions, segmentation, quantum theory and others. This paper investigates the possibility of using sparse representation in classification, comparing the achieved results to other commonly used classifiers. The different methods were evaluated in a real-world classification task in which the position of a lying patient has to be deduced based on the data provided by a pressure mattress of 30×11 sensors. The investigated method outperformed most of the commonly used classifiers with accuracy exceeding 92%, while being less demanding on design and implementation complexity.

Klíčová slova

sparse representation, linear regression, LASSO, redundant basis, SRC, classification

Autoři

MESÁROŠOVÁ, M.; MIHÁLIK, O.

Vydáno

25. 4. 2023

Nakladatel

Brno University of Technology, Faculty of Electrical Engineering and Communication

Místo

Brno

ISBN

978-80-214-6154-3

Kniha

Proceedings II of the 29 th Conference STUDENT EEICT 2023 Selected papers

Edice

1

ISSN

2788-1334

Periodikum

Proceedings II of the Conference STUDENT EEICT

Stát

Česká republika

Strany od

101

Strany do

104

Strany počet

4

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf

BibTex

@inproceedings{BUT184282,
  author="Michaela {Mesárošová} and Ondrej {Mihálik}",
  title="Sparse Representation for Classification of Posture in Bed",
  booktitle="Proceedings II of the 29 th Conference STUDENT EEICT 2023 Selected papers",
  year="2023",
  series="1",
  journal="Proceedings II of the Conference STUDENT EEICT",
  pages="101--104",
  publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication",
  address="Brno",
  isbn="978-80-214-6154-3",
  issn="2788-1334",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2023_sbornik_2_v2.pdf"
}