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Video processing and 3D modelling of chest movement using MS Kinect depth sensor

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dc.title Video processing and 3D modelling of chest movement using MS Kinect depth sensor en
dc.contributor.author Procházka, Aleš
dc.contributor.author Vyšata, Oldřich
dc.contributor.author Schätz, Martin
dc.contributor.author Charvátová, Hana
dc.contributor.author Araujo, Carmen Paz Suárez
dc.contributor.author Geman, Oana
dc.contributor.author Mařík, Vladimír
dc.relation.ispartof 2016 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2016
dc.identifier.isbn 9781509055241
dc.date.issued 2016
dc.event.title 2016 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2016
dc.event.location Reggio Calabria
utb.event.state-en Italy
utb.event.state-cs Itálie
dc.event.sdate 2016-10-27
dc.event.edate 2016-10-28
dc.type conferenceObject
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.identifier.doi 10.1109/IWCIM.2016.7801175
dc.relation.uri http://ieeexplore.ieee.org/document/7801175/
dc.subject breathing analysis en
dc.subject computational intelligence en
dc.subject depth sensors en
dc.subject three dimensional modelling en
dc.subject Video data processing en
dc.description.abstract General methods of video processing and three dimensional modelling have a wide range of applications in engineering, archaeology and spacial objects study. The paper is devoted to applications of these methods in biomedicine and neurology using MS Kinect depth sensor for non-contact monitoring of breathing. A special attention is paid to visualization of results and motion mapping over the selected chest area. The proposed methodology applies digital signal processing methods and functional transforms for acquired data de-noising, spectral analysis, and feature selection. Suggested method uses further the local polynomial approxima-tion to detect extremal values of spectral components. The results verify the correspondence between the evaluations of the breathing frequency obtained from the thorax movement recorded by the depth sensor. The study proves that simple depth sensors can be used for non-contact detection of breathing frequency and for the three dimensional modelling of the chest movement. The proposed non-contact method enables to analyse breathing for diagnostic purposes and monitoring in the home environment as a component of assisted living technologies. General methodology studied form a contribu-tion to the use of video sequences or sets of images for spacial objects modelling, their recognition, possible three dimen-sional printing or analysis of time evolution of their features. © 2016 IEEE. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1006955
utb.identifier.scopus 2-s2.0-85011003355
utb.identifier.wok 000392205700002
utb.source d-wok
dc.date.accessioned 2017-07-13T14:50:25Z
dc.date.available 2017-07-13T14:50:25Z
utb.contributor.internalauthor Charvátová, Hana
utb.fulltext.affiliation Aleš Prochazka 1,2, Oldřich Vyšata 2,3, Martin Schatz 1, Hana Charvatova 4, Carmen Paz Suarez Araujo 5 , Oana Geman 6 , Vladimír Mařík 2 1 University of Chemistry and Technology in Prague, Czech Republic 2 Czech Technical University, Czech Institute of Informatics, Robotics and Cybernetics, Czech Republic 3 Charles University, Faculty of Medicine in Hradec Králové, Dept of Neurology, Czech Republic 4 Tomas Bata University in Zlín, Faculty of Applied Informatics, Czech Republic 5 Universidad de Las Palmas de Gran Canaria, Spain 6 Stefan cel Mare University of Suceava, Romania
utb.fulltext.dates -
utb.fulltext.sponsorship Real data were kindly provided by the Department of Neurology of the Faculty hospital of the Charles University in Hradec Kralove. The project was approved by the Local Ethics Committee as stipulated by the Helsinki Declaration.
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