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

Procházka, Aleš; Vyšata, Oldřich; Charvátová, Hana

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
utb.fulltext.ou	-
utb.identifier.jel	-