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.
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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 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/10067520 |
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