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

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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 (hcharvatova@email.cz)
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
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