CNN Architecture for Posture Classification on Small Data

CNN Architecture for Posture Classification on Small Data

Paper in proceedings (conference paper)

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.

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.

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

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

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

2025

14.08.2024

Elsevier

Brno, Czechia

18th IFAC Conference on Programmable Devices and Embedded Systems – PDeS 2024.

2405-8963

IFAC-PapersOnLine

58

9

United Kingdom of Great Britain and Northern Ireland

299

304

6

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

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

CNN Architecture for Posture Classification on Small Data