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Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare

Karayaneva, Y; Sharifzadeh, S; Li, W; Jing, Y; Tan, B; (2021) Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare. IEEE Access , 9 pp. 62984-63001. 10.1109/ACCESS.2021.3074088. Green open access

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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 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.

Type: Article
Title: Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ACCESS.2021.3074088
Publisher version: https://doi.org/10.1109/ACCESS.2021.3074088
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Keywords: Science & Technology, Technology, Computer Science, Information Systems, Engineering, Electrical & Electronic, Telecommunications, Computer Science, Engineering, Feature extraction, Doppler radar, Principal component analysis, Data visualization, Activity recognition, Discrete cosine transforms, Unsupervised learning, data visualization, health and safety, DCT analysis, unsupervised learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
URI: https://discovery.ucl.ac.uk/id/eprint/10128076
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