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

Bo Tan, Wenda Li, Robert Piechocki

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
58 Downloads (Pure)


Physical activity recognition is an important research area in pervasive computing because its importance for in e-healthcare, security and human machine interaction. Among various approaches, passive RF sensing on the basis of well-tried radar principle has potential to provides unique non-invasive human activity detection and recognition solution, and draws more attention. However, this thechnology is far from mature. This paper presents a novel HMM-log-likelihood matrix based feacture characterizing to breack the fixed sliding window limitation in traditional feature extraction approaches. We prove the effectiveness of proposed feature extraction method in K-means&K-medoids clustering algorithms with experimental Doppler data gathered collected from a passive radar system. The time adaptive log-likelihood matrix based approach is proven outperforming the traditional SVD, PCA and physical feature based approaches by 80% regarding activity recognizing rate.
Original languageEnglish
Pages (from-to)5413 - 5421
JournalIEEE Sensors Journal
Issue number13
Early online date9 May 2018
Publication statusPublished - 1 Jul 2018


  • Human Activity Recognition
  • Passive Sensing
  • Doppler Radar
  • Log-likelihood matrix


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