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Sensors 2016, 16(7), 996; doi:10.3390/s16070996

Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis

1
Department of Computing and Control Engineering, University of Chemistry and Technology, 166 28 Prague 6, Czech Republic
2
Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University, 166 36 Prague 6, Czech Republic
3
Department of Neurology, Faculty of Medicine in Hradec Králové, Charles University, 500 05 Hradec Králové, Czech Republic
*
Author to whom correspondence should be addressed.
Academic Editors: Changzhi Li, Roberto Gómez-García and José-María Muñoz-Ferreras
Received: 18 April 2016 / Revised: 18 June 2016 / Accepted: 22 June 2016 / Published: 28 June 2016
(This article belongs to the Special Issue Non-Contact Sensing)
View Full-Text   |   Download PDF [1645 KB, uploaded 28 June 2016]   |  

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. View Full-Text
Keywords: MS Kinect data acquisition; image and depth sensors; computational intelligence; human–machine interaction; breathing analysis; neurological disorders; visualization; big data processing MS Kinect data acquisition; image and depth sensors; computational intelligence; human–machine interaction; breathing analysis; neurological disorders; visualization; big data processing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Procházka, A.; Schätz, M.; Vyšata, O.; Vališ, M. Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis. Sensors 2016, 16, 996.

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