CNN Architecture for Posture Classification on Small Data

Mesárošová, Michaela; Mihálik, Ondrej; Jirgl, Miroslav

CNN Architecture for Posture Classification on Small Data

Files

CNN Architecture for Posture Classification on Small Data.pdf(1.04 MB)

Date

2024-08-14

Authors

Mesárošová, Michaela

Mihálik, Ondrej

Jirgl, Miroslav

ORCID

0009-0005-1276-8388

0000-0001-7433-9275

0000-0002-1037-0641

Publisher

Elsevier

Altmetrics

Abstract

A convolutional neural network is often mentioned as one of the deep learning methods that requires a large amount of training data. Questioning this belief, this paper explores the applicability of classification based on a shallow net structure trained on a small data set in the~context of patient posture classification based on data from a pressure mattress. Designing a CNN often presents a complex problem, especially without a universally applicable approach, allowing many diverse structural possibilities and training settings. We tested various training options and layer configurations to provide an overview of influential parameters for posture classification. Experiments show encouraging results with the leave-one-out cross-validation accuracy of 93.1% of one of the evaluated CNN structures and its hyperparameter settings.

Keywords

CNN, fine tuning, network structure, optimization, posture classification

Citation

IFAC-PapersOnLine (ELSEVIER). 2024, vol. 58, issue 9, p. 299-304.
https://doi.org/10.1016/j.ifacol.2024.07.413

Document type

Peer-reviewed

Document version

Published version

Language of document

en

Document licence

Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/