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Authors: Cheng Li 1 ; A. Pourtaherian 1 ; L. van Onzenoort 2 and P. N. de With 1

Affiliations: 1 Eindhoven University of Technology, Eindhoven, The Netherlands ; 2 Maxima Medical Center, Veldhoven, The Netherlands

ISBN: 978-989-758-488-6

ISSN: 2184-4321

Keyword(s): Automated Infant Monitoring, R-CNN, HMM, GERD Diagnosis.

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
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 - Volume 5: VISAPP, ISBN 978-989-758-488-6 ISSN 2184-4321, pages 553-560. DOI: 10.5220/0010299605530560

@conference{visapp21,
author={Cheng Li. and A. Pourtaherian. and L. {van Onzenoort}. and P. {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 - Volume 5: VISAPP,},
year={2021},
pages={553-560},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010299605530560},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Automated Infant Monitoring based on R-CNN and HMM
SN - 978-989-758-488-6
IS - 2184-4321
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

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