AutoTag: Recurrent Variational Autoencoder for Unsupervised Apnea Detection with RFID Tags

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.