Log-likelihood clustering-enabled passive rf sensing for residential activity recognition

Wenda Li*, Bo Tan, Yangdi Xu, Robert J. Piechocki

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

12 Citations (Scopus)
292 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)5413-5421
Number of pages9
JournalIEEE Sensors Journal
Issue number13
Early online date9 May 2018
Publication statusPublished - 1 Jul 2018

Structured keywords

  • Digital Health


  • Doppler radar
  • Human activity recognition
  • log-likelihood matrix
  • passive sensing


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