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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Online Access: | https://www.mdpi.com/1424-8220/21/10/3529 |
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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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