title: Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis.
abstract: 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.
authors.affiliation: Department of Computing and Control Engineering, University of Chemistry and Technology, 166 28 Prague 6, Czech Republic. A.Prochazka@ieee.org.
authors.forename: Aleš
authors.initials: A
authors.lastname: Procházka
authors.affiliation: Department of Computing and Control Engineering, University of Chemistry and Technology, 166 28 Prague 6, Czech Republic. Martin.Schatz@vscht.cz.
authors.forename: Martin
authors.initials: M
authors.lastname: Schätz
authors.affiliation: Department of Computing and Control Engineering, University of Chemistry and Technology, 166 28 Prague 6, Czech Republic. Vysatao@gmail.com.|Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University, 166 36 Prague 6, Czech Republic. Vysatao@gmail.com.|Department of Neurology, Faculty of Medicine in Hradec Kr&#225;lov&#233;, Charles University, 500 05 Hradec Kr&#225;lov&#233;, Czech Republic. Vysatao@gmail.com.
authors.forename: Oldřich
authors.initials: O
authors.lastname: Vyšata
authors.affiliation: Department of Neurology, Faculty of Medicine in Hradec Kr&#225;lov&#233;, Charles University, 500 05 Hradec Kr&#225;lov&#233;, Czech Republic. Valismar@seznam.cz.
authors.forename: Martin
authors.initials: M
authors.lastname: Vališ
mesh_terms: D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D009068:Movement; D012119:Respiration; D013997:Time Factors; D014741:Video Recording
keywords: MS Kinect data acquisition; big data processing; breathing analysis; computational intelligence; human–machine interaction; image and depth sensors; neurological disorders; visualization