Diverse studies have demonstrated the importance of monitoring breathing rate (BR). Commonly, changes in BR are one of the earliest and major markers of serious complications/illness. However, it is frequently neglected due to limitations of clinically established measurement techniques, which require attachment of sensors. The employment of adhesive pads or thoracic belts in preterm infants as well as in traumatized or burned patients is an additional paramount issue. The present paper proposes a new robust approach, based on data fusion, to remotely monitor BR using infrared thermography (IRT). The algorithm considers not only temperature modulation around mouth and nostrils but also the movements of both shoulders. The data of these four sensors/regions of interest need to be further fused to reach improved accuracy. To investigate the performance of our approach, two different experiments (phase A: normal breathing, phase B: simulation of breathing disorders) on twelve healthy volunteers were performed. Thoracic effort (piezoplethysmography) was simultaneously acquired to validate our results. Excellent agreements between BR estimated with IRT and gold standard were achieved. While in phase A a mean correlation of 0.98 and a root-mean-square error (RMSE) of 0.28 bpm was reached, in phase B the mean correlation and the RMSE hovered around 0.95 and 3.45 bpm, respectively. The higher RMSE in phase B results predominantly from delays between IRT and gold standard in BR transitions: eupnea/apnea, apnea/tachypnea etc. Moreover, this study also demonstrates the capability of IRT to capture varied breathing disorders, and consecutively, to assess respiratory function. In summary, IRT might be a promising monitoring alternative to the conventional contact-based techniques regarding its performance and remarkable capabilities.

• Klíčová slova
• Breathing disorders, Data fusion, Physiological monitoring, Respiratory rate, Thermal imaging,
• MeSH
• algoritmy MeSH
• audiovizuální záznam MeSH
• Bayesova věta MeSH
• biologické modely MeSH
• dechová frekvence * MeSH
• dýchání * MeSH
• lidé MeSH
• monitorování fyziologických funkcí metody   MeSH
• pilotní projekty MeSH
• počítačové zpracování signálu * MeSH
• pohyb MeSH
• statistické modely MeSH
• zdraví dobrovolníci pro lékařské studie MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví 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.

• Klíčová slova
• MS Kinect data acquisition, big data processing, breathing analysis, computational intelligence, human–machine interaction, image and depth sensors, neurological disorders, visualization,
• 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