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Human Occupancy Detection via Passive Cognitive Radio

Human occupancy detection (HOD) in an enclosed space, such as indoors or inside of a vehicle, via passive cognitive radio (CR) is a new and challenging research area. Part of the difficulty arises from the fact that a human subject cannot easily be detected due to spectrum variation. In this paper,...

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Autores principales: Liu, Jenny, Mu, Huaizheng, Vakil, Asad, Ewing, Robert, Shen, Xiaoping, Blasch, Erik, Li, Jia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7436269/
https://www.ncbi.nlm.nih.gov/pubmed/32751618
http://dx.doi.org/10.3390/s20154248
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author Liu, Jenny
Mu, Huaizheng
Vakil, Asad
Ewing, Robert
Shen, Xiaoping
Blasch, Erik
Li, Jia
author_facet Liu, Jenny
Mu, Huaizheng
Vakil, Asad
Ewing, Robert
Shen, Xiaoping
Blasch, Erik
Li, Jia
author_sort Liu, Jenny
collection PubMed
description Human occupancy detection (HOD) in an enclosed space, such as indoors or inside of a vehicle, via passive cognitive radio (CR) is a new and challenging research area. Part of the difficulty arises from the fact that a human subject cannot easily be detected due to spectrum variation. In this paper, we present an advanced HOD system that dynamically reconfigures a CR to collect passive radio frequency (RF) signals at different places of interest. Principal component analysis (PCA) and recursive feature elimination with logistic regression (RFE-LR) algorithms are applied to find the frequency bands sensitive to human occupancy when the baseline spectrum changes with locations. With the dynamically collected passive RF signals, four machine learning (ML) classifiers are applied to detect human occupancy, including support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), and linear SVM with stochastic gradient descent (SGD) training. The experimental results show that the proposed system can accurately detect human subjects—not only in residential rooms—but also in commercial vehicles, demonstrating that passive CR is a viable technique for HOD. More specifically, the RFE-LR with SGD achieves the best results with a limited number of frequency bands. The proposed adaptive spectrum sensing method has not only enabled robust detection performance in various environments, but also improved the efficiency of the CR system in terms of speed and power consumption.
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spelling pubmed-74362692020-08-24 Human Occupancy Detection via Passive Cognitive Radio Liu, Jenny Mu, Huaizheng Vakil, Asad Ewing, Robert Shen, Xiaoping Blasch, Erik Li, Jia Sensors (Basel) Article Human occupancy detection (HOD) in an enclosed space, such as indoors or inside of a vehicle, via passive cognitive radio (CR) is a new and challenging research area. Part of the difficulty arises from the fact that a human subject cannot easily be detected due to spectrum variation. In this paper, we present an advanced HOD system that dynamically reconfigures a CR to collect passive radio frequency (RF) signals at different places of interest. Principal component analysis (PCA) and recursive feature elimination with logistic regression (RFE-LR) algorithms are applied to find the frequency bands sensitive to human occupancy when the baseline spectrum changes with locations. With the dynamically collected passive RF signals, four machine learning (ML) classifiers are applied to detect human occupancy, including support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), and linear SVM with stochastic gradient descent (SGD) training. The experimental results show that the proposed system can accurately detect human subjects—not only in residential rooms—but also in commercial vehicles, demonstrating that passive CR is a viable technique for HOD. More specifically, the RFE-LR with SGD achieves the best results with a limited number of frequency bands. The proposed adaptive spectrum sensing method has not only enabled robust detection performance in various environments, but also improved the efficiency of the CR system in terms of speed and power consumption. MDPI 2020-07-30 /pmc/articles/PMC7436269/ /pubmed/32751618 http://dx.doi.org/10.3390/s20154248 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Jenny
Mu, Huaizheng
Vakil, Asad
Ewing, Robert
Shen, Xiaoping
Blasch, Erik
Li, Jia
Human Occupancy Detection via Passive Cognitive Radio
title Human Occupancy Detection via Passive Cognitive Radio
title_full Human Occupancy Detection via Passive Cognitive Radio
title_fullStr Human Occupancy Detection via Passive Cognitive Radio
title_full_unstemmed Human Occupancy Detection via Passive Cognitive Radio
title_short Human Occupancy Detection via Passive Cognitive Radio
title_sort human occupancy detection via passive cognitive radio
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7436269/
https://www.ncbi.nlm.nih.gov/pubmed/32751618
http://dx.doi.org/10.3390/s20154248
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