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Appl. Sci. 2017, 7(6), 581; doi:10.3390/app7060581

Cycling Segments Multimodal Analysis and Classification Using Neural Networks

1
Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague, Czech Republic
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
4
Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 05 Prague, Czech Republic
*
Author to whom correspondence should be addressed.
Academic Editor: Bouras Christos
Received: 21 April 2017 / Revised: 14 May 2017 / Accepted: 31 May 2017 / Published: 4 June 2017
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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. View Full-Text
Keywords: GPS data acquisition; heart rate analysis; neural computing; visualization; computational intelligence; classification; human–machine interaction GPS data acquisition; heart rate analysis; neural computing; visualization; computational intelligence; classification; human–machine interaction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Procházka, A.; Vaseghi, S.; Charvátová, H.; Ťupa, O.; Vyšata, O. Cycling Segments Multimodal Analysis and Classification Using Neural Networks. Appl. Sci. 2017, 7, 581.

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