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

作     者：Prochazka, Ales Schatz, Martin Vysata, Oldrich Valis, Martin 

作者机构：Univ Chem & Technol Dept Comp & Control Engn Prague 16628 6 Czech Republic Czech Tech Univ Czech Inst Informat Robot & Cybernet Prague 16636 6 Czech Republic Charles Univ Prague Fac Med Hradec Kralove Dept Neurol Hradec Kralove 50005 Czech Republic 

出 版 物：《SENSORS》 (传感器)

年 卷 期：2016年第16卷第7期

页      面：1-11页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

主　　题：MS Kinect data acquisition image and depth sensors computational intelligence human-machine interaction breathing analysis neurological disorders visualization big data processing 

摘      要：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 (***). 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.