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The effect of face masks on physiological data and the classification of rehabilitation walking

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dc.title The effect of face masks on physiological data and the classification of rehabilitation walking en
dc.contributor.author Procházka, Aleš
dc.contributor.author Vyšata, Oldřich
dc.contributor.author Charvátová, Hana
dc.relation.ispartof IEEE Transactions on Neural Systems and Rehabilitation Engineering
dc.identifier.issn 1534-4320 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.issn 1558-0210 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2022
utb.relation.volume 30
dc.citation.spage 2467
dc.citation.epage 2473
dc.type article
dc.language.iso en
dc.publisher IEEE
dc.identifier.doi 10.1109/TNSRE.2022.3201487
dc.relation.uri https://ieeexplore.ieee.org/document/9866077
dc.relation.uri https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9866077
dc.subject classification en
dc.subject computational intelligence en
dc.subject face masks en
dc.subject gait analysis en
dc.subject machine learning en
dc.subject motion monitoring en
dc.subject physiological data acquisition en
dc.description.abstract Gait analysis and the assessment of rehabilitation exercises are important processes that occur during fitness level monitoring and the treatment of neurological disorders. This paper presents the possibility of using oximetric, heart rate (HR), accelerometric, and global navigation satellite systems (GNSSs) to analyse signals recorded during uphill and downhill walking without and with a face mask to find its influence on physiological functions during selected walking patterns. The experimental dataset includes 86 signal segments acquired under different conditions. The proposed methodology is based on signal analysis in both the time and frequency domains. The results indicate that face mask use has a minimal effect on blood oxygen concentration and heart rate, with the average mean changes of these parameters being less than 2%. The support vector machine, a Bayesian method, the k-nearest neighbour method, and a two-layer neural network showed very good separation abilities and successfully classified different walking patterns only in the case when the effect of face mask wearing was not included in the classification process. Our methodology suggests that artificial intelligence and machine learning tools are efficient methods for the assessment of motion patterns in different motion conditions and that face masks have a negligible effect for short-duration experiments. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1011124
utb.identifier.obdid 43884054
utb.identifier.scopus 2-s2.0-85137136123
utb.identifier.wok 000849260100011
utb.identifier.pubmed 36001515
utb.source J-wok
dc.date.accessioned 2022-09-12T10:36:14Z
dc.date.available 2022-09-12T10:36:14Z
dc.description.sponsorship [LTAIN19007]
dc.rights Attribution 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.rights.access openAccess
utb.contributor.internalauthor Charvátová, Hana
utb.fulltext.affiliation Aleš Procházka https://orcid.org/0000-0002-0270-1738 , Life Senior Member, IEEE, Hana Charvátová https://orcid.org/0000-0001-7363-976X , and Oldřich Vyšata, Member, IEEE Aleš Procházka is with the Department of Computing and Control Engineering, University of Chemistry and Technology, Prague, 166 28 Prague, Czech Republic, and also with the Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic (e-mail: a.prochazka@ieee.org). Hana Charvátová is with the Faculty of Applied Informatics, Tomas Bata University in Zlín, 760 01 Zlín, Czech Republic. Oldřich Vyšata is with the Department of Neurology, Faculty of Medicine, Charles University in Hradec Králové, 500 03 Hradec Králové, Czech Republic. (Corresponding author: Aleš Procházka.)
utb.fulltext.dates Manuscript received 24 March 2022 revised 11 June 2022 and 18 July 2022 accepted 13 August 2022 Date of publication 24 August 2022 date of current version 1 September 2022
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Control Eng., Univ. Chem. Technol., Technicka, Prague, Czech Republic, Jun. 2022. [Online]. Available: https://repozitar.vscht.cz/api/presentation/1.0/download/ae50e15c-0154-46f6-82aa-36b35e4387cc/
utb.fulltext.sponsorship This work was supported by the Research Development of Advanced Computational Algorithms for Evaluating Post-Surgery Rehabilitation under Grant LTAIN19007.
utb.wos.affiliation [Prochazka, Ales] Univ Chem & Technol, Dept Comp & Control Engn, Prague 16628, Czech Republic; [Prochazka, Ales] Czech Tech Univ, Czech Inst Informat Robot & Cybernet, Prague 16636, Czech Republic; [Charvatova, Hana] Tomas Bata Univ Zlin, Fac Appl Informat, Zlin 76001, Czech Republic; [Vysata, Oldrich] Charles Univ Hradec Kralove, Dept Neurol, Fac Med, Hradec Kralove 50003, Czech Republic
utb.scopus.affiliation Department of Computing and Control Engineering, University of Chemistry and Technology, Prague & Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Czech Republic; Faculty of Applied Informatics, Tomas Bata University in Zlín, Czech Republic; Department of Neurology, Faculty of Medicine, Charles University in Hradec Králové, Czech Republic
utb.fulltext.projects LTAIN19007
utb.fulltext.faculty Faculty of Applied Informatics
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