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GPS-based analysis of physical activities using positioning and heart rate cycling data

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dc.title GPS-based analysis of physical activities using positioning and heart rate cycling data en
dc.contributor.author Charvátová, Hana
dc.contributor.author Procházka, Aleš
dc.contributor.author Vaseghi, Saeed V.
dc.contributor.author Vyšata, Oldřich
dc.contributor.author Vališ, Martin
dc.relation.ispartof Signal, Image and Video Processing
dc.identifier.issn 1863-1703 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2017
utb.relation.volume 11
utb.relation.issue 2
dc.citation.spage 251
dc.citation.epage 258
dc.type article
dc.language.iso en
dc.publisher Springer London
dc.identifier.doi 10.1007/s11760-016-0928-z
dc.relation.uri https://link.springer.com/article/10.1007/s11760-016-0928-z
dc.subject Bayesian classification en
dc.subject Biomedical signal analysis en
dc.subject Cycling data processing en
dc.subject Data fusion en
dc.subject Feature extraction en
dc.subject GPS data acquisition en
dc.description.abstract This paper addresses the use of multichannel signal processing methods in analysis of heart rate changes during cycling using the global positioning system (GPS) to record the route conditions. The main objectives of this work are in monitoring of physiological activities, cycling features extraction, their classification and visualization. Real data were acquired from 41 cycling rides of the same 11.48-km long route divided into 2460 segments of approximately 60 s. The data were recorded with a varying sampling period within the range of 1–22 s depending on the route profile. The pre-processing stage included preparatory analysis, filtering and resampling of the data to a constant sampling rate. The proposed algorithm includes the evaluation of the cross-correlation between the heart rate and the altitude gradient as recorded by a GPS satellite system. A Bayesian approach was then applied to classify the cycling segment features into two classes (specifying cycling up and down) with the classification accuracy better than 93 %. A comparison with other classification methods is presented in the paper as well. The results include the following relationships: (1) the heart rate and altitude gradient, which shared a positive correlation coefficient of 0.62; (2) the heart rate and speed, which shared a negative correlation coefficient of −0.72 over all of the analysed segments; and (3) the mean heart rate change delay (6.8–11.5 s) in relation to the changes in the altitude gradients associated with cycling up and down. The paper forms a contribution to the use of computational intelligence and visualization for data processing both in cycling and fitness physical activities as well. © 2016, Springer-Verlag London. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1006909
utb.identifier.obdid 43876810
utb.identifier.scopus 2-s2.0-84976271568
utb.identifier.wok 000393116900008
utb.source j-scopus
dc.date.accessioned 2017-06-27T08:13:12Z
dc.date.available 2017-06-27T08:13:12Z
utb.contributor.internalauthor Charvátová, Hana
utb.fulltext.affiliation Hana Charvátová 1 · Aleš Procházka 2,3 · Saeed Vaseghi 2,3 · Oldřich Vyšata 4 · Martin Vališ 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, Czech Republic 3 Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic 4 Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University in Prague, 500 05 Hradec Králové, Czech Republic
utb.fulltext.dates Received: 5 March 2016 Revised: 5 June 2016 Accepted: 18 June 2016 Published online: 27 June 2016
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