Radar-Based, Simultaneous Human Presence Detection and Breathing Rate Estimation

Human presence detection is an application that has a growing need in many industries. Hotel room occupancy is critical for electricity and energy conservation. Industrial factories and plants have the same need to know the occupancy status to regulate electricity, lighting, and energy expenditures....

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Main Authors: Nir Regev, Dov Wulich
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/21/10/3529
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spelling doaj-0287cd403dc44607a96633b0e0b9f21f2021-06-01T00:27:15ZengMDPI AGSensors1424-82202021-05-01213529352910.3390/s21103529Radar-Based, Simultaneous Human Presence Detection and Breathing Rate EstimationNir Regev0Dov Wulich1School of Electrical and Computer Engineering, Ben-Gurion University of The Negev, Beer-Sheva 8410501, IsraelSchool of Electrical and Computer Engineering, Ben-Gurion University of The Negev, Beer-Sheva 8410501, IsraelHuman presence detection is an application that has a growing need in many industries. Hotel room occupancy is critical for electricity and energy conservation. Industrial factories and plants have the same need to know the occupancy status to regulate electricity, lighting, and energy expenditures. In home security there is an obvious necessity to detect human presence inside the residence. For elderly care and healthcare, the system would like to know if the person is sleeping in the room, sitting on a sofa or conversely, is not present. This paper focuses on the problem of detecting presence using only the minute movements of breathing while at the same time estimating the breathing rate, which is the secondary aim of the paper. We extract the suspected breathing signal, and construct its Fourier series (FS) equivalent. Then we employ a generalized likelihood ratio test (GLRT) on the FS signal to determine if it is a breathing pattern or noise. We will show that calculating the GLRT also yields the maximum likelihood (ML) estimator for the breathing rate. We tested this algorithm on sleeping babies as well as conducted experiments on humans aged 12 to 44 sitting on a chair in front of the radar. The results are reported in the sequel.https://www.mdpi.com/1424-8220/21/10/3529micro-Doppleroccupancy detectionpresence detectionvital signsrespirationspectral-estimation
collection DOAJ
language English
format Article
sources DOAJ
author Nir Regev
Dov Wulich
spellingShingle Nir Regev
Dov Wulich
Radar-Based, Simultaneous Human Presence Detection and Breathing Rate Estimation
Sensors
micro-Doppler
occupancy detection
presence detection
vital signs
respiration
spectral-estimation
author_facet Nir Regev
Dov Wulich
author_sort Nir Regev
title Radar-Based, Simultaneous Human Presence Detection and Breathing Rate Estimation
title_short Radar-Based, Simultaneous Human Presence Detection and Breathing Rate Estimation
title_full Radar-Based, Simultaneous Human Presence Detection and Breathing Rate Estimation
title_fullStr Radar-Based, Simultaneous Human Presence Detection and Breathing Rate Estimation
title_full_unstemmed Radar-Based, Simultaneous Human Presence Detection and Breathing Rate Estimation
title_sort radar-based, simultaneous human presence detection and breathing rate estimation
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2021-05-01
description Human presence detection is an application that has a growing need in many industries. Hotel room occupancy is critical for electricity and energy conservation. Industrial factories and plants have the same need to know the occupancy status to regulate electricity, lighting, and energy expenditures. In home security there is an obvious necessity to detect human presence inside the residence. For elderly care and healthcare, the system would like to know if the person is sleeping in the room, sitting on a sofa or conversely, is not present. This paper focuses on the problem of detecting presence using only the minute movements of breathing while at the same time estimating the breathing rate, which is the secondary aim of the paper. We extract the suspected breathing signal, and construct its Fourier series (FS) equivalent. Then we employ a generalized likelihood ratio test (GLRT) on the FS signal to determine if it is a breathing pattern or noise. We will show that calculating the GLRT also yields the maximum likelihood (ML) estimator for the breathing rate. We tested this algorithm on sleeping babies as well as conducted experiments on humans aged 12 to 44 sitting on a chair in front of the radar. The results are reported in the sequel.
topic micro-Doppler
occupancy detection
presence detection
vital signs
respiration
spectral-estimation
url https://www.mdpi.com/1424-8220/21/10/3529
work_keys_str_mv AT nirregev radarbasedsimultaneoushumanpresencedetectionandbreathingrateestimation
AT dovwulich radarbasedsimultaneoushumanpresencedetectionandbreathingrateestimation
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