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Multimodal breathing analysis in the evaluation of physical load

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dc.title Multimodal breathing analysis in the evaluation of physical load en
dc.contributor.author Procházka, Aleš
dc.contributor.author Charvátová, Hana
dc.contributor.author Vyšata, Oldřich
dc.contributor.author Cejnar, Pavel
dc.contributor.author Mařík, Vladimír
dc.relation.ispartof International Conference on Digital Signal Processing, DSP
dc.identifier.isbn 9781538618950
dc.date.issued 2017
utb.relation.volume 2017-August
dc.event.title 2017 22nd International Conference on Digital Signal Processing, DSP 2017
dc.event.location London
utb.event.state-en United Kingdom
utb.event.state-cs Spojené království
dc.event.sdate 2017-08-23
dc.event.edate 2017-08-25
dc.type conferenceObject
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers Inc.
dc.identifier.doi 10.1109/ICDSP.2017.8096050
dc.relation.uri https://ieeexplore.ieee.org/abstract/document/8096050/
dc.description.abstract The paper presents specific methods of processing of multimodal data recorded during physical activities by depth MS Kinect cameras, thermal imaging cameras and heart rate sensors. All video data and heart rate signals used in the present study were recorded in the home environment. The proposed methodology includes the detection of the chest breathing area for breathing motion analysis used by the MS Kinect. For the thermal image processing the static and dynamic selection of regions of interests was performed in associated sets of images to find time evolution of respiratory signals and their temperature changes. Signal de-noising by finite impulse filters is applied both for breathing and heart rate data. Correlation analysis is used in the data processing stage to find the time relation between individual physiological variables. Results include relations between signals acquired during physical activities and they show how simple sensors can be used to increase the accuracy of standard diagnostical tools in biomedicine as well. © 2017 IEEE. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007745
utb.identifier.obdid 43876829
utb.identifier.scopus 2-s2.0-85040373181
utb.identifier.wok 000426874700016
utb.source d-scopus
dc.date.accessioned 2018-02-26T10:20:05Z
dc.date.available 2018-02-26T10:20:05Z
utb.contributor.internalauthor Charvátová, Hana
utb.scopus.affiliation University of Chemistry and Technology, Dept of Computing and Control Engineering, Czech Republic; Czech Technical University, Czech Institute of Informatics, Robotics and Cybernetics, Czech Republic; Charles University, Department of Neurology, Czech Republic; Tomas Bata University in Zlín, Czech Republic
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