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Cycling segments multimodal analysis and classification using neural networks

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dc.title Cycling segments multimodal analysis and classification using neural networks en
dc.contributor.author Procházka, Aleš
dc.contributor.author Vaseghi, Saeed
dc.contributor.author Charvátová, Hana
dc.contributor.author Ťupa, Ondřej
dc.contributor.author Vyšata, Oldřich
dc.relation.ispartof Applied Sciences (Switzerland)
dc.identifier.issn 2076-3417 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2017
utb.relation.volume 7
utb.relation.issue 6
dc.type article
dc.language.iso en
dc.publisher Molecular Diversity Preservation International (MDPI)
dc.identifier.doi 10.3390/app7060581
dc.relation.uri http://www.mdpi.com/2076-3417/7/6/581/htm
dc.subject GPS data acquisition en
dc.subject heart rate analysis en
dc.subject neural computing en
dc.subject visualization en
dc.subject computational intelligence en
dc.subject classification en
dc.subject human-machine interaction en
dc.description.abstract This paper presents methodology for the processing of GPS and heart rate signals acquired during long-term physical activities. The data analysed include geo-positioning and heart rate multichannel signals recorded for 272.2 h of cycling across the Andes mountains over a 5694-km long expedition. The proposed computational methods include multimodal data de-noising, visualization, and analysis in order to determine specific biomedical features. The results include the correspondence between the heart rate and slope for downhill and uphill cycling and the mean heart rate evolution on flat segments: a regression coefficient of -0.014 bpm/h related to time and 6.3 bpm/km related to altitude. The classification accuracy of selected cycling features by neural networks, support vector machine, and k-nearest neighbours methods is between 91.3% and 98.6%. The proposed methods allow the analysis of data during physical activities, enabling an efficient human-machine interaction. © 2017 by the authors. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007421
utb.identifier.obdid 43876825
utb.identifier.scopus 2-s2.0-85020253439
utb.identifier.wok 000404449800057
utb.source j-scopus
dc.date.accessioned 2017-09-08T12:14:54Z
dc.date.available 2017-09-08T12:14:54Z
dc.description.sponsorship Cancer Research UK
dc.rights Attribution 4.0 International
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.rights.access openAccess
utb.contributor.internalauthor Charvátová, Hana
utb.fulltext.affiliation Aleš Procházka 1,2,*, Saeed Vaseghi 1, Hana Charvátová 3, Ondřej Ťupa 1 and Oldřich Vyšata 1,2,4 1 Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague, Czech Republic; saeedvaseghi@aol.com (S.V.); tupao@vscht.cz (O.T.); Vysatao@gmail.com (O.V.) 2 Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic 3 Faculty of Applied Informatics, Tomas Bata University in Zlín, 760 05 Zlín, Czech Republic; hcharvatova@email.cz 4 Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 05 Prague, Czech Republic * Correspondence: A.Prochazka@ieee.org; Tel.: +420-220-444-198 Academic Editor: Christos Bouras
utb.fulltext.dates Received: 21 April 2017; Accepted: 31 May 2017; Published: 4 June 2017
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utb.fulltext.sponsorship All real data were kindly provided by Professor Saeed Vaseghi from his cycling across the Andes in the autumn of 2016. The physiological monitoring was done at the Department of Neurology of the Faculty hospital of the Charles University in Hradec Kralove. The whole project was in support of Cancer Research UK (www.justgiving.comfundraisingsaeedvaseghi).
utb.scopus.affiliation Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, Prague, Czech Republic; Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic; Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic; Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, Prague, Czech Republic
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