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Log-Likelihood Clustering-Enabled Passive RF Sensing for Residential Activity Recognition

Li, W; Tan, B; Xu, Y; Piechocki, RJ; (2018) Log-Likelihood Clustering-Enabled Passive RF Sensing for Residential Activity Recognition. IEEE Sensors Journal , 18 (13) pp. 5413-5421. 10.1109/JSEN.2018.2834739. Green open access

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Abstract

Physical activity recognition is an important research area in pervasive computing because of its importance for e-healthcare, security, and human-machine interaction. Among various approaches, passive radio frequency sensing is a well-tried radar principle that has potential to provide the unique solution for non-invasive activity detection and recognition. However, this technology is still far from mature. This paper presents a novel hidden Markov model-based log-likelihood matrix for characterizing the Doppler shifts to break the fixed sliding window limitation in traditional feature extraction approaches. We prove the effectiveness of the proposed feature extraction method by K-means K-medoids clustering algorithms with experimental Doppler data gathered from a passive radar system. The results show that the time adaptive log-likelihood matrix outperforms the traditional singular value decomposition, principal component analysis, and physical feature-based approaches, and reaches 80% in recognizing rate.

Type: Article
Title: Log-Likelihood Clustering-Enabled Passive RF Sensing for Residential Activity Recognition
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/JSEN.2018.2834739
Publisher version: https://doi.org/10.1109/JSEN.2018.2834739
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Human activity recognition , log-likelihood matrix , Doppler radar , passive sensing, Doppler effect , Radio frequency , Hidden Markov models , Passive radar , Activity recognition , Surveillance
UCL classification: UCL > Provost and Vice Provost Offices
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 Electronic and Electrical Eng
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/10067520
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