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

• Published in: IEEE sensors journal Vol. 18; no. 13; pp. 5413 - 5421
• Main Authors: Wenda Li, Bo Tan, Yangdi Xu, Piechocki, Robert J.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-07-2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activity recognition; Adaptive systems; Clustering; Cybersecurity; Doppler effect; Doppler radar; Feature extraction; Hidden Markov models; Human activity recognition; log-likelihood matrix; Markov chains; Passive radar; passive sensing; Principal components analysis; Radio frequency; Residential areas; Singular value decomposition; Surveillance; Ubiquitous computing
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2018.2834739

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.