Smart Mat for Respiratory Activity Detection: Study in a Clinical Setting [r-libre/1757]

Otis, Samuel; Bessam, Abdulrazak; Ben Jebara, Sofia; Tournoux, François et Mezghani, Neila (2019). Smart Mat for Respiratory Activity Detection: Study in a Clinical Setting. Dans Pagán, J.; Mokhtari, M.; Aloulou, H.; Abdulrazak, B. et Cabrera, M. (dir.), How AI Impacts Urban Living and Public Health. ICOST 2019 (p. 61-72). Cham : Springer, coll. « Lecture Notes in Computer Science », vol. 11862. https://doi.org/10.1007/978-3-030-32785-9_6

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Catégorie de document :	Chapitres de livres
Évaluation par un comité de lecture :	Oui
Étape de publication :	Publié
Résumé :	We discuss in this paper a study of a smart and unobtrusive mattress in a clinical setting on a population with cardiorespiratory problems. Up to recently, the vast majority of studies with unobtrusive sensors are done with healthy populations. The unobtrusive monitoring of the Respiratory Rate (RR) is essential for proposing better diagnoses. Thus, new industrial and research activity on smart mattresses is targeting respiratory rate in an Internet-of-Things (IoT) context. In our work, we are interested in the performances of a microbend fiber optic sensor (FOS) mattress on 81 subjects admitted in the Cardiac Intensive Care Unit (CICU) by estimating the RR from their ballistocardiograms (BCG). Our study proposes a new RR estimator, based on harmonic plus noise models (HNM) and compares it with known estimators such as MODWT and CLIE. The goal is to examine, using a more representative and bigger dataset, the performances of these methods and of the smart mattress in general. Results of applying these three estimators on the BCG show that MODWT is more accurate with an average mean absolute error (MAE) of 1.97 ± 2.12 BPM. However, the HNM estimator has space for improvements with estimation errors of 2.91 ± 4.07 BPM. The smart mattress works well within a standard RR range of 10–20 breaths-per-minute (BPM) but gets less accurate with a bigger range of estimation. These results highlight the need to test these sensors in much more realistic contexts.
Déposant:	Mezghani, Neila
Responsable :	Neila Mezghani
Dépôt :	10 oct. 2019 18:16
Dernière modification :	10 oct. 2019 18:16

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