While a large range of sensing applications such as activity sensing and vital sign monitoring have been realized with WiFi sensing, using commercial WiFi devices to obtain fine-grained size information of objects remains challenging due to the narrow bandwidth of WiFi. Very recent studies attempted to measure object sizes using WiFi signals. However, these systems are still far from practical with a lot of limitations including requiring multiple transceiver pairs and can only measure one-dimensional size, hindering their real-life adoption. Also, these systems rely on Channel State Information (CSI) to work, which is only available on few commercial WiFi cards. In this work, we propose to employ a new channel data, i.e., Beamforming Feedback Information (BFI), widely available on almost all new generation WiFi cards for fine-grained size measurement. Through thoroughly analyzing the mathematical relationship between BFI and CSI, we show how to use BFI to achieve fine-grained size measurement. We propose a novel method to accurately measure the two-dimensional size of an object using a single transceiver pair by identifying the positions of singularities when the object passes through the diffraction zone of the transceiver pair. Experiment results show that Wi2DMeasure can accurately measure the two-dimensional size of objects under various conditions, achieving a small median error of only 3.7 mm.

With a big success in data communication, wireless signals are now exploited for fine-grained contactless activity sensing including human respiration monitoring, finger gesture recognition, subtle chin movement tracking when speaking, etc. Different from coarsegrained body and limb movements, these fine-grained movements are in the scale of millimetres and are thus difficult to be sensed. While good sensing performance can be achieved at one location, the performance degrades dramatically at a very nearby location. In this paper, by revealing the effect of static multipaths in sensing, we propose a novel method to add man-made "virtual" multipath to significantly improve the sensing performance. With carefully designed "virtual" multipath, we are able to boost the sensing performance at each location purely in software without any extra hardware.