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Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare

Yordanka Karayaneva Orcid Logo, Sara Sharifzadeh Orcid Logo, Wenda Li Orcid Logo, Yanguo Jing, Bo Tan Orcid Logo

IEEE Access, Volume: 9, Pages: 62984 - 63001

Swansea University Author: Sara Sharifzadeh Orcid Logo

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DOI (Published version): 10.1109/access.2021.3074088

Abstract

Passive radio frequency (RF) sensing and monitoring of human daily activities in elderly care homes is an emerging topic. Micro-Doppler radars are an appealing solution considering their non-intrusiveness, deep penetration, and high-distance range. Unsupervised activity recognition using Doppler rad...

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Published in: IEEE Access
ISSN: 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa65599
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spelling v2 65599 2024-02-09 Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare a4e15f304398ecee3f28c7faec69c1b0 0000-0003-4621-2917 Sara Sharifzadeh Sara Sharifzadeh true false 2024-02-09 MACS Passive radio frequency (RF) sensing and monitoring of human daily activities in elderly care homes is an emerging topic. Micro-Doppler radars are an appealing solution considering their non-intrusiveness, deep penetration, and high-distance range. Unsupervised activity recognition using Doppler radar data has not received attention, in spite of its importance in case of unlabelled or poorly labelled activities in real scenarios. This study proposes two unsupervised feature extraction methods for the purpose of human activity monitoring using Doppler-streams. These include a local Discrete Cosine Transform (DCT)-based feature extraction method and a local entropy-based feature extraction method. In addition, a novel application of Convolutional Variational Autoencoder (CVAE) feature extraction is employed for the first time for Doppler radar data. The three feature extraction architectures are compared with the previously used Convolutional Autoencoder (CAE) and linear feature extraction based on Principal Component Analysis (PCA) and 2DPCA. Unsupervised clustering is performed using K-Means and K-Medoids. The results show the superiority of DCT-based method, entropy-based method, and CVAE features compared to CAE, PCA, and 2DPCA, with more than 5%-20% average accuracy. In regards to computation time, the two proposed methods are noticeably much faster than the existing CVAE. Furthermore, for high-dimensional data visualisation, three manifold learning techniques are considered. The methods are compared for the projection of raw data as well as the encoded CVAE features. All three methods show an improved visualisation ability when applied to the encoded CVAE features. Journal Article IEEE Access 9 62984 63001 Institute of Electrical and Electronics Engineers (IEEE) 2169-3536 30 4 2021 2021-04-30 10.1109/access.2021.3074088 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee DataDriven Research Innovation DDRI Coventry University 2024-07-12T11:24:14.9081590 2024-02-09T00:53:55.0218934 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Yordanka Karayaneva 0000-0002-5859-6746 1 Sara Sharifzadeh 0000-0003-4621-2917 2 Wenda Li 0000-0001-6617-9136 3 Yanguo Jing 4 Bo Tan 0000-0002-6855-6270 5 65599__29916__05f8e23c3f88478d988087643eaf95d0.pdf 65599.VOR.pdf 2024-04-04T13:13:42.8174979 Output 2078603 application/pdf Version of Record true This work is licensed under a Creative Commons Attribution 4.0 License. true eng https://creativecommons.org/licenses/by/4.0/
title Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare
spellingShingle Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare
Sara Sharifzadeh
title_short Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare
title_full Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare
title_fullStr Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare
title_full_unstemmed Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare
title_sort Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare
author_id_str_mv a4e15f304398ecee3f28c7faec69c1b0
author_id_fullname_str_mv a4e15f304398ecee3f28c7faec69c1b0_***_Sara Sharifzadeh
author Sara Sharifzadeh
author2 Yordanka Karayaneva
Sara Sharifzadeh
Wenda Li
Yanguo Jing
Bo Tan
format Journal article
container_title IEEE Access
container_volume 9
container_start_page 62984
publishDate 2021
institution Swansea University
issn 2169-3536
doi_str_mv 10.1109/access.2021.3074088
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description Passive radio frequency (RF) sensing and monitoring of human daily activities in elderly care homes is an emerging topic. Micro-Doppler radars are an appealing solution considering their non-intrusiveness, deep penetration, and high-distance range. Unsupervised activity recognition using Doppler radar data has not received attention, in spite of its importance in case of unlabelled or poorly labelled activities in real scenarios. This study proposes two unsupervised feature extraction methods for the purpose of human activity monitoring using Doppler-streams. These include a local Discrete Cosine Transform (DCT)-based feature extraction method and a local entropy-based feature extraction method. In addition, a novel application of Convolutional Variational Autoencoder (CVAE) feature extraction is employed for the first time for Doppler radar data. The three feature extraction architectures are compared with the previously used Convolutional Autoencoder (CAE) and linear feature extraction based on Principal Component Analysis (PCA) and 2DPCA. Unsupervised clustering is performed using K-Means and K-Medoids. The results show the superiority of DCT-based method, entropy-based method, and CVAE features compared to CAE, PCA, and 2DPCA, with more than 5%-20% average accuracy. In regards to computation time, the two proposed methods are noticeably much faster than the existing CVAE. Furthermore, for high-dimensional data visualisation, three manifold learning techniques are considered. The methods are compared for the projection of raw data as well as the encoded CVAE features. All three methods show an improved visualisation ability when applied to the encoded CVAE features.
published_date 2021-04-30T11:24:14Z
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