Publication detail

Sparse Representation for Classification of Posture in Bed

MESÁROŠOVÁ, M. MIHÁLIK, O.

Original Title

Sparse Representation for Classification of Posture in Bed

Type

conference paper

Language

English

Original Abstract

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.

Keywords

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

Authors

MESÁROŠOVÁ, M.; MIHÁLIK, O.

Released

25. 4. 2023

Publisher

Brno University of Technology, Faculty of Electrical Engineering and Communication

Location

Brno

ISBN

978-80-214-6154-3

Book

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

Edition

1

ISBN

2788-1334

Periodical

Proceedings II of the Conference STUDENT EEICT

State

Czech Republic

Pages from

101

Pages to

104

Pages count

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"
}