Blood oxygen concentration and physiological data changes during motion while wearing face masks

Charvátová, Hana; Procházka, Aleš; Fričl, Matěj; Vyšata, Oldřich

dc.title	Blood oxygen concentration and physiological data changes during motion while wearing face masks	en
dc.contributor.author	Charvátová, Hana
dc.contributor.author	Procházka, Aleš
dc.contributor.author	Fričl, Matěj
dc.contributor.author	Vyšata, Oldřich
dc.relation.ispartof	IEEE Access
dc.identifier.issn	2169-3536 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued	2022
dc.citation.spage	91763
dc.citation.epage	91770
dc.type	article
dc.language.iso	en
dc.publisher	Institute of Electrical and Electronics Engineers Inc.
dc.identifier.doi	10.1109/ACCESS.2022.3202931
dc.relation.uri	https://ieeexplore.ieee.org/document/9869822/
dc.relation.uri	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9869822
dc.subject	blood	en
dc.subject	motion control	en
dc.subject	heart rate	en
dc.subject	biomedical monitoring	en
dc.subject	absorption	en
dc.subject	temperature sensors	en
dc.subject	cameras	en
dc.subject	computational intelligence	en
dc.subject	machine learning	en
dc.subject	motion monitoring	en
dc.subject	wearable sensors	en
dc.subject	blood oxygen concentration	en
dc.subject	breathing analysis	en
dc.subject	computational intelligence	en
dc.subject	machine learning	en
dc.subject	classification	en
dc.description.abstract	The study of physiological changes recorded by wearable devices during physical exercises belongs to very important research topics in neurology for the detection of motion disorders or monitoring of the fitness level during sports activities. This paper contributes to this area with studies of the effect of face masks and respirators on blood oxygen concentration, breathing frequency, and the heart rate changes. Experimental data sets include 296 segments of their total length of 60 hours, recorded on a home exercise bike under different motion conditions. Wearable instruments with oximetric, heart rate, accelerometric, and thermal camera sensors were used to fill the own database of signals recorded with selected sampling frequencies. The proposed methodology includes fundamental signal and image processing methods for signal analysis and machine learning tools for labeling image components and detecting facial temperature changes. Results show the minimal effect of mask wearing on blood oxygen concentration but its substantial influence on the breathing frequency and the heart rate. The use of a respirator substantially increased the respiratory rate for the given set of experiments under the load. This indicates how wearable sensors, computational intelligence, and machine learning can be used for motion monitoring and data analysis of signals recorded in different conditions.	en
utb.faculty	Faculty of Applied Informatics
dc.identifier.uri	http://hdl.handle.net/10563/1011136
utb.identifier.obdid	43884056
utb.identifier.scopus	2-s2.0-85137583288
utb.identifier.wok	000852480700001
utb.source	j-scopus
dc.date.accessioned	2022-09-20T08:07:44Z
dc.date.available	2022-09-20T08:07:44Z
dc.rights	Attribution 4.0 International
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.rights.access	openAccess
utb.ou	Department of Computing and Control Engineering
utb.contributor.internalauthor	Charvátová, Hana
utb.fulltext.affiliation	HANA CHARVÁTOVÁ https://orcid.org/0000-0001-7363-976X 1, ALEŠ PROCHÁZKA https://orcid.org/0000-0002-0270-1738 2,3,4, (Life Senior Member, IEEE),MATĚJ FRIČL2, AND OLDŘICH VYŠATA4, (Member, IEEE) 1 Faculty of Applied Informatics, Tomas Bata University in Zlín, 760 01 Zlín, Czech Republic 2 Department of Computing and Control Engineering, University of Chemistry and Technology at Prague, 160 00 Prague, Czech Republic 3 Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University of Prague, 160 00 Prague, Czech Republic 4 Department of Neurology, Faculty of Medicine, Charles University at Hradec Králové, 500 05 Hradec Králové, Czech Republic Corresponding author: Hana Charvátová ([email protected])
utb.fulltext.dates	Received 15 August 2022 accepted 27 August 2022 date of publication 29 August 2022 date of current version 6 September 2022
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utb.fulltext.sponsorship	This work was supported in part by the Development of Advanced Computational Algorithms for Evaluating Post-Surgery Rehabilitation under Grant LTAIN19007; and in part by the National Sustainability Program Project of the Ministry of Education, Youth and Sports of the Czech Republic under Grant LO1303 (MSMT-7778/2014).
utb.wos.affiliation	[Charvatova, Hana] Tomas Bata Univ Zlin, Fac Appl Informat, Zlin 76001, Czech Republic; [Prochazka, Ales; Fricl, Matej] Univ Chem & Technol Prague, Dept Comp & Control Engn, Prague, Czech Republic; [Prochazka, Ales] Czech Tech Univ, Czech Inst Informat Robot & Cybernet, Prague 16000, Czech Republic; [Prochazka, Ales] Charles Univ Hradec Kralove, Dept Neurol, Fac Med, Hradec Kralove, Czech Republic
utb.scopus.affiliation	Faculty of Applied Informatics, Tomas Bata University in Zlin, Zlín, Czech Republic; Department of Computing and Control Engineering, University of Chemistry and Technology at Prague, Prague, Czech Republic; Department of Neurology, Faculty of Medicine, Charles University at Hradec Králové, Hradec Králové, Czech Republic
utb.fulltext.projects	LTAIN19007
utb.fulltext.projects	LO1303 (MSMT-7778/2014)
utb.fulltext.faculty	Faculty of Applied Informatics
utb.fulltext.ou	-
utb.identifier.jel	-