RF-AcSense: Device-Free Activity Localization and Recognition via Passive RFID Tag Array

Position and behavior are indispensable as indicators of a human’s state, which in turn is the basis of various intelligent scenarios, e.g., smart home, smart city. Most of the pioneer systems focus unilaterally on the meticulous level of localization or the accuracy of activity recognition. In this paper, we build a real-time framework RF-AcSense that can identify both location and activity of the human in a passive and flexible way, which completes the perception by the changes of surrounding signals induced by the human. Specifically, through the Radio-Frequency IDentification (RFID) based context-aware smart space, two radio images can be acquired separately for the ground plane and the tag array plane, which are subsequently used for upper-lower layer combined localization and upper-lower limb separated activity recognition. In particular, a 3D-convolution based deep learning network is designed to capture the comprehensive deep features from time-sequence radio images, and pruning operation is supplemented to further improve the classification accuracy and the processing speed of the system. We implement RF-AcSense with COTS RFID devices, and the experimental results demonstrate the superiority of RF-AcSense on both localization and activity recognition.