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Motion assessment for accelerometric and heart rate cycling data analysis

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dc.title Motion assessment for accelerometric and heart rate cycling data analysis en
dc.contributor.author Charvátová, Hana
dc.contributor.author Procházka, Aleš
dc.contributor.author Vyšata, Oldřich
dc.relation.ispartof Sensors
dc.identifier.issn 1424-8220 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2020
utb.relation.volume 20
utb.relation.issue 5
dc.type article
dc.language.iso en
dc.publisher MDPI AG
dc.identifier.doi 10.3390/s20051523
dc.relation.uri https://www.mdpi.com/1424-8220/20/5/1523
dc.subject multimodal signal analysis en
dc.subject computational intelligence en
dc.subject machine learning en
dc.subject motion monitoring en
dc.subject accelerometers en
dc.subject classification en
dc.description.abstract Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands 〈3, 8〉 and 〈8, 15〉 Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1009634
utb.identifier.obdid 43881287
utb.identifier.scopus 2-s2.0-85081627422
utb.identifier.wok 000525271500285
utb.identifier.pubmed 32164235
utb.source j-scopus
dc.date.accessioned 2020-04-03T15:08:55Z
dc.date.available 2020-04-03T15:08:55Z
dc.description.sponsorship Ministry of Health of the Czech RepublicMinistry of Health, Czech Republic [FN HK 00179906]; Charles University at Prague, Czech Republic [PROGRES Q40]; project PERSONMED-Centre for the Development of Personalized Medicine in Age-Related Diseases [CZ.02.1.010.00.017_0480007441]; European Regional Development Fund (ERDF)European Union (EU); governmental budget of the Czech Republic
dc.rights Attribution 4.0 International
dc.rights.uri http://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 1*, Aleš Procházka 2,3,4, Oldřich Vyšata 4 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 in Prague, 166 28 Prague 6, Czech Republic 3 Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 160 00 Prague 6, Czech Republic 4 Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 05 Hradec Králové, Czech Republic * Author to whom correspondence should be addressed.
utb.fulltext.dates Received: 12 February 2020 Revised: 4 March 2020 Accepted: 5 March 2020 Published: 10 March 2020
utb.fulltext.sponsorship This research was supported by grant projects of the Ministry of Health of the Czech Republic (FN HK 00179906) and of the Charles University at Prague, Czech Republic (PROGRES Q40), as well as by the project PERSONMED—Centre for the Development of Personalized Medicine in Age-Related Diseases, Reg. No. CZ.02.1.010.00.017_0480007441, co-financed by the European Regional Development Fund (ERDF) and the governmental budget of the Czech Republic. No ethical approval was required for this study.
utb.wos.affiliation [Charvatova, Hana] Tomas Bata Univ Zlin, Fac Appl Informat, Zlin 76001, Czech Republic; [Prochazka, Ales] Univ Chem & Technol Prague, Dept Comp & Control Engn, Prague 16628 6, Czech Republic; [Prochazka, Ales] Czech Tech Univ, Czech Inst Informat Robot & Cybernet, Prague 16000 6, Czech Republic; [Prochazka, Ales; Vygata, Oldrich] Charles Univ Prague, Fac Med Hradec Kralove, Dept Neurol, Hradec Kralove 50005, Czech Republic
utb.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, 760 01, Czech Republic; Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, Prague 6, 166 28, Czech Republic; Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague 6, 160 00, Czech Republic; Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, Hradec Králové, 500 05, Czech Republic
utb.fulltext.projects FN HK 00179906
utb.fulltext.projects PROGRES Q40
utb.fulltext.projects CZ.02.1.010.00.017_0480007441
utb.fulltext.faculty Faculty of Applied Informatics
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