|Title:||Video processing and 3D modelling of chest movement using MS Kinect depth sensor||Authors:||Procházka, A.
Suarez Araujo, Carmen Paz
|UNESCO Clasification:||120325 Diseño de sistemas sensores
120304 Inteligencia artificial
220990 Tratamiento digital. Imágenes
32 Ciencias médicas
Three dimensional modelling
Video data processing
|Issue Date:||2016||Conference:||2016 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2016||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.||URI:||http://hdl.handle.net/10553/41363||ISBN:||978-1-5090-5525-8||DOI:||10.1109/IWCIM.2016.7801175||Source:||2016 International Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2016|
|Appears in Collections:||Actas de congresos|
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