Co může otec udělat pro to, aby jeho dva synové vykročili do života tou správnou nohou? Inženýr Spáčil má jasno. Nachystá pro ně náročnou zkoušku odolnosti v odlehlé horské chalupě. Ovšem navzdory jeho smělému plánu není nouze o komické situace a při čtení se proto skvěle pobavíte!

The paper is devoted to the study of facial region temperature changes using a simple thermal imaging camera and to the comparison of their time evolution with the pectoral area motion recorded by the MS Kinect depth sensor. The goal of this research is to propose the use of video records as alternative diagnostics of breathing disorders allowing their analysis in the home environment as well. The methods proposed include (i) specific image processing algorithms for detecting facial parts with periodic temperature changes; (ii) computational intelligence tools for analysing the associated videosequences; and (iii) digital filters and spectral estimation tools for processing the depth matrices. Machine learning applied to thermal imaging camera calibration allowed the recognition of its digital information with an accuracy close to 100% for the classification of individual temperature values. The proposed detection of breathing features was used for monitoring of physical activities by the home exercise bike. The results include a decrease of breathing temperature and its frequency after a load, with mean values -0.16 °C/min and -0.72 bpm respectively, for the given set of experiments. The proposed methods verify that thermal and depth cameras can be used as additional tools for multimodal detection of breathing patterns.

• MeSH
• algoritmy MeSH
• dýchání * MeSH
• počítačové zpracování obrazu MeSH
• pohyb těles MeSH
• umělá inteligence MeSH
• Publikační typ
• časopisecké články MeSH

This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect image, depth and infrared sensors to enable their time analysis in selected regions of interest. The proposed methodology includes the use of computational methods and functional transforms for data selection, as well as their denoising, spectral analysis and visualization, in order to determine specific biomedical features. The results that were obtained verify the correspondence between the evaluation of the breathing frequency that was obtained from the image and infrared data of the mouth area and from the thorax movement that was recorded by the depth sensor. Spectral analysis of the time evolution of the mouth area video frames was also used for heart rate estimation. Results estimated from the image and infrared data of the mouth area were compared with those obtained by contact measurements by Garmin sensors (www.garmin.com). The study proves that simple image and depth sensors can be used to efficiently record biomedical multidimensional data with sufficient accuracy to detect selected biomedical features using specific methods of computational intelligence. The achieved accuracy for non-contact detection of breathing rate was 0.26% and the accuracy of heart rate estimation was 1.47% for the infrared sensor. The following results show how video frames with depth data can be used to differentiate different kinds of breathing. The proposed method enables us to obtain and analyse data for diagnostic purposes in the home environment or during physical activities, enabling efficient human-machine interaction.

• MeSH
• audiovizuální záznam MeSH
• časové faktory MeSH
• dýchání * MeSH
• lidé MeSH
• monitorování fyziologických funkcí přístrojové vybavení   MeSH
• pohyb MeSH
• srdeční frekvence fyziologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

The monitoring of data from global positioning system (GPS) receivers and remote sensors of physiological and environmental data allow forming an information database for observed data processing. In this paper, we propose the use of such a database for the analysis of physical activities during cycling. The main idea of the proposed algorithm is to use cross-correlations between the heart rate and the altitude gradient to evaluate the delay between these variables and to study its time evolution. The data acquired during 22 identical cycling routes, each about 130 km long, include more than 6,700 segments of length 60 s recorded with varying sampling periods. General statistical and digital signal processing methods used include mathematical tools to reject gross errors, resampling using selected interpolation methods, digital filtering of noise signal components, and estimating cross-correlations between the position data and the physiological signals. The results of a regression between GPS and physiological data include the estimate of the time delay between the heart rate change and gradient altitude of about 7.5 s and its decrease during each training route.

• MeSH
• algoritmy MeSH
• cyklistika fyziologie   MeSH
• geografické informační systémy * MeSH
• lidé MeSH
• počítačové zpracování signálu * MeSH
• regresní analýza MeSH
• srdeční frekvence fyziologie   MeSH
• telemetrie metody   MeSH
• zeměpis MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

• MeSH
• diagnostické zobrazování využití   MeSH
• financování organizované MeSH
• Fourierova analýza MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   využití   MeSH
• počítačová rentgenová tomografie metody   využití   MeSH
• počítačové zpracování obrazu metody   využití   MeSH
• statistika jako téma metody   MeSH
• ultrasonografie metody   MeSH
• vylepšení obrazu metody   MeSH
• Check Tag
• lidé MeSH