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.

Preview

Text Wenda_Li_Log_likelihood_Clustering_Enabled_Passive_RF_Sensing_for_Residential_Activity_Recognition.pdf - Accepted Version Download (966kB) | Preview

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.

Download activity - last month

Export as XMLCSVJSON

Download activity - last 12 months

Export as JSONCSVXML

Downloads by country - last 12 months

Export as CSVXMLJSON

Archive Staff Only

View Item