Multimodal breathing analysis in the evaluation of physical load

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F17%3A63516824" target="_blank" >RIV/70883521:28140/17:63516824 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/abstract/document/8096050/" target="_blank" >http://ieeexplore.ieee.org/abstract/document/8096050/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICDSP.2017.8096050" target="_blank" >10.1109/ICDSP.2017.8096050</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multimodal breathing analysis in the evaluation of physical load

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Multimodal breathing analysis in the evaluation of physical load

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Digital Signal Processing (DSP), 2017 22nd International Conference on Digital Signal Processing

ISBN

978-1-5386-1896-7

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

4

Strana od-do

1-4

Název nakladatele

IEEE

Místo vydání

London

Místo konání akce

Londýn

Datum konání akce

23. 8. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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