Automated Infant Monitoring based on R-CNN and HMM

Cheng Li, A. Pourtaherian, L. van Onzenoort, P. H. N. de With

2021

Abstract

Manual monitoring of young infants suffering from reflux is a significant effort, since infants can hardly articulate their feelings. This work proposes a near real-time video-based infant monitoring system for the analysis of infant expressions. The discomfort moments can be correlated with a reflux measurement for gastroesophageal reflux disease diagnose. The system consists of two components: expression classification and expression state stabilization. The expression classification is realized by Faster R-CNN and the state stabilization is implemented with a Hidden Markov Model. The experimental results show a mean average precision of 82.3% and 83.4% for 7 different expression classifications, and up to 90% for discomfort detection, evaluated with both clinical and daily datasets. Moreover, when adopting temporal analysis, the false expression changes between frames can be reduced up to 65%, which significantly enhances the consistency of the system output.

Paper Citation

in Harvard Style

Li C., Pourtaherian A., van Onzenoort L. and N. de With P. (2021). Automated Infant Monitoring based on R-CNN and HMM. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP; ISBN 978-989-758-488-6, SciTePress, pages 553-560. DOI: 10.5220/0010299605530560

in Bibtex Style

@conference{visapp21,
author={Cheng Li and A. Pourtaherian and L. van Onzenoort and P. H. N. de With},
title={Automated Infant Monitoring based on R-CNN and HMM},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP},
year={2021},
pages={553-560},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010299605530560},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 5: VISAPP
TI - Automated Infant Monitoring based on R-CNN and HMM
SN - 978-989-758-488-6
AU - Li C.
AU - Pourtaherian A.
AU - van Onzenoort L.
AU - N. de With P.
PY - 2021
SP - 553
EP - 560
DO - 10.5220/0010299605530560
PB - SciTePress