Abstract
With the growth of smart healthcare in the Internet of Things (IoT), breathing monitoring and apnea detection are of increasing importance. In this paper, we propose AutoTag, a recurrent variational autoencoder model for breathing and apnea detection with commodity RFID Tags. The AutoTag system consists of signal extraction, calibration, and respiration monitoring modules. We propose a novel method to mitigate the frequency hopping offset with realtime calibration for FCC complaint RFID systems, and a new recurrent variational autoencoder method for apnea and breathing detection. Experimental results demonstrate the effectiveness of the proposed AutoTag system in two different environments.