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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
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" }