Publication detail

CNN Architecture for Posture Classification on Small Data

MESÁROŠOVÁ, M. MIHÁLIK, O. JIRGL, M.

Original Title

CNN Architecture for Posture Classification on Small Data

Type

conference paper

Language

English

Original 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

Authors

MESÁROŠOVÁ, M.; MIHÁLIK, O.; JIRGL, M.

Released

14. 8. 2024

Publisher

Elsevier

Location

Brno, Czechia

ISBN

2405-8963

Periodical

IFAC-PapersOnLine (ELSEVIER)

Year of study

58

Number

9

State

Kingdom of the Netherlands

Pages from

299

Pages to

304

Pages count

6

URL

https://doi.org/10.1016/j.ifacol.2024.07.413

Full text in the Digital Library

http://hdl.handle.net/11012/249472

BibTex

@inproceedings{BUT189141,
  author="Michaela {Mesárošová} and Ondrej {Mihálik} and Miroslav {Jirgl}",
  title="CNN Architecture for Posture Classification on Small Data",
  booktitle="18th IFAC Conference on Programmable Devices and Embedded Systems – PDeS 2024.",
  year="2024",
  journal="IFAC-PapersOnLine (ELSEVIER)",
  volume="58",
  number="9",
  pages="299--304",
  publisher="Elsevier",
  address="Brno, Czechia",
  doi="10.1016/j.ifacol.2024.07.413",
  issn="2405-8963",
  url="https://doi.org/10.1016/j.ifacol.2024.07.413"
}