Motion analysis using global navigation satellite system and physiological data

Procházka, Aleš; Molčanová, Alexandra; Charvátová, Hana; Geman, Oana; Vyšata, Oldřich

dc.title	Motion analysis using global navigation satellite system and physiological data	en
dc.contributor.author	Procházka, Aleš
dc.contributor.author	Molčanová, Alexandra
dc.contributor.author	Charvátová, Hana
dc.contributor.author	Geman, Oana
dc.contributor.author	Vyšata, Oldřich
dc.relation.ispartof	IEEE Access
dc.identifier.issn	2169-3536 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued	2023
utb.relation.volume	11
dc.citation.spage	42096
dc.citation.epage	42103
dc.type	article
dc.language.iso	en
dc.publisher	Institute of Electrical and Electronics Engineers Inc.
dc.identifier.doi	10.1109/ACCESS.2023.3270102
dc.relation.uri	https://ieeexplore.ieee.org/document/10107613
dc.relation.uri	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10107613
dc.subject	multichannel signal processing	en
dc.subject	global navigation satellite systems	en
dc.subject	feature extraction	en
dc.subject	machine learning	en
dc.subject	computational intelligence	en
dc.subject	classification	en
dc.subject	physical activity monitoring	en
dc.subject	cardiology	en
dc.subject	global navigation satellite system	en
dc.subject	sensors	en
dc.subject	monitoring	en
dc.subject	biomedical monitoring	en
dc.subject	satellites	en
dc.subject	heart rate	en
dc.subject	smart phones	en
dc.description.abstract	Motion analysis using wearable sensors is an essential research topic with broad mathematical foundations and applications in various areas, including engineering, robotics, and neurology. This paper presents the use of the global navigation satellite system (GNSS) for detecting and recording the position of a moving body, along with signals from additional sensors, for monitoring of physical activity and analyzing heart rate dynamics during running on route segments of different slopes and speeds. This method provides an alternative to the heart monitoring on the treadmill ergometer in the cardiology laboratory. The proposed computational methodology involves digital data preprocessing, time synchronization, and data resampling to enable their correlation, feature extraction both in time and frequency domains, and classification. The datasets include signals acquired during ten experimental runs in the selected area. The motion patterns detection involves segmenting the signals by analysing the GNSS data, evaluating the patterns, and classifying the motion signals under different terrain conditions. This classification method compares neural networks, support vector machine, Bayesian, and k-nearest neighbour methods. The highest accuracy of 93.3 % was achieved by using combined features and a two-layer neural network for classification into three classes with different slopes. The proposed method and graphical user interface demonstrate the efficiency of multi-channel and multi-dimensional signal processing with applications in rehabilitation, fitness movement monitoring, neurology, cardiology, engineering, and robotic systems.	en
utb.faculty	Faculty of Applied Informatics
dc.identifier.uri	http://hdl.handle.net/10563/1011548
utb.identifier.obdid	43884902
utb.identifier.scopus	2-s2.0-85159687720
utb.identifier.wok	000981907000001
utb.source	j-scopus
dc.date.accessioned	2023-06-12T08:13:24Z
dc.date.available	2023-06-12T08:13:24Z
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.access	openAccess
utb.contributor.internalauthor	Charvátová, Hana
utb.fulltext.affiliation	ALEŠ PROCHÁZKA 1,2, (Life Senior Member, IEEE), ALEXANDRA MOLČANOVÁ1, HANA CHARVÁTOVÁ 3, OANA GEMAN4, (Senior Member, IEEE), AND OLDŘICH VYŠATA5, (Member, IEEE) 1 Department of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology Prague, 160 00 Prague, Czech Republic 2 Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 160 00 Prague, Czech Republic 3 Faculty of Applied Informatics, Tomas Bata University in Zlín, 760 01 Zlín, Czech Republic 4 Department of Health and Human Development, Stefan cel Mare University of Suceava, 720229 Suceava, Romania 5 Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 05 Hradec Králové, Czech Republic Corresponding author: Aleš Procházka (A.Prochazka@ieee.org)
utb.fulltext.dates	Received 3 April 2023 accepted 20 April 2023 date of publication 24 April 2023 date of current version 3 May 2023
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utb.fulltext.sponsorship	This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic—Program NPU I (LO1504).
utb.wos.affiliation	[Prochazka, Ales; Molcanova, Alexandra] Univ Chem & Technol Prague, Dept Math Informat & Cybernet, Prague 16000, Czech Republic; [Prochazka, Ales] Czech Tech Univ, Czech Inst Informat Robot & Cybernet, Prague 16000, Czech Republic; [Charvatova, Hana] Tomas Bata Univ Zlin, Fac Appl Informat, Zlin 76001, Czech Republic; [Geman, Oana] Stefan cel Mare Univ Suceava, Dept Hlth & Human Dev, Suceava 720229, Romania; [Vysata, Oldrich] Charles Univ Prague, Fac Med Hradec Kralove, Dept Neurol, Hradec Kralove 50005, Czech Republic
utb.scopus.affiliation	Department of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology in Prague, Prague, Czech Republic; Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic; Department of Health and Human Development, Stefan cel Mare University of Suceava, Suceava, Romania; Department of Neurology, Faculty of Medicine at Hradec Králové, Charles University in Prague, Hradec Králové, Czech Republic
utb.fulltext.projects	LO1504
utb.fulltext.faculty	Faculty of Applied Informatics
utb.fulltext.ou	-