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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F16%3A43901602" target="_blank" >RIV/60461373:22340/16:43901602 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/16:00306267 RIV/00216208:11150/16:10332751

Result on the web

<a href="http://www.mdpi.com/1424-8220/16/7/996/pdf" target="_blank" >http://www.mdpi.com/1424-8220/16/7/996/pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s16070996" target="_blank" >10.3390/s16070996</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

—

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Sensors

ISSN

1424-8220

e-ISSN

—

Volume of the periodical

16

Issue of the periodical within the volume

7

Country of publishing house

CH - SWITZERLAND

Number of pages

11

Pages from-to

"996/1"-"996/11"

UT code for WoS article

000380967000054

EID of the result in the Scopus database

2-s2.0-84976416943