In-Bed Posture Classification Based on Sparse Representation in Redundant Dictionaries
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Date
2022-05-20
Authors
Mihálik, Ondrej
Sýkora, Tomáš
Husák, Michal
Fiedler, Petr
0000-0001-7433-9275Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
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Abstract
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.
Description
Citation
IFAC-PapersOnLine (ELSEVIER). 2022, vol. 55, issue 4, p. 374-379.
https://doi.org/10.1016/j.ifacol.2022.06.062
https://doi.org/10.1016/j.ifacol.2022.06.062
Document type
Peer-reviewed
Document version
Accepted version
Date of access to the full text
Language of document
en
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Date of acceptance
Defence
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Document licence
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
http://creativecommons.org/licenses/by-nc-nd/4.0/