@ARTICLE{7938705, 
author={L. Yao and Q. Z. Sheng and X. Li and T. Gu and M. Tan and X. Wang and S. Wang and W. Ruan}, 
journal={IEEE Transactions on Mobile Computing}, 
title={Compressive Representation for Device-Free Activity Recognition with Passive RFID Signal Strength}, 
year={2018}, 
volume={17}, 
number={2}, 
pages={293-306}, 
keywords={biomedical telemetry;body sensor networks;compressed sensing;computer vision;feature extraction;geriatrics;learning (artificial intelligence);medical computing;patient monitoring;radiofrequency identification;robust system;older people;machine learning algorithms;compressive sensing;dictionary-based approach;unsupervised subspace decomposition;embodied discriminative information;robust activity recognition;device-free activity recognition;passive RFID signal strength;fall detection;remote health monitoring;human activity recognition;computer vision;wearable sensor technologies;video camera;remote health intervention;real-life residential environment;signal fluctuations decipher;radio-frequency identification technology;feature selection;Activity recognition;Radiofrequency identification;Monitoring;Legged locomotion;Senior citizens;Robustness;Mobile computing;Activity recognition;RFID;compressive sensing;subspace decomposition;feature selection}, 
doi={10.1109/TMC.2017.2706282}, 
ISSN={1536-1233}, 
month={Feb},}