Non-intrusive respiration sensing without any device attached to the target plays a particular important role in our everyday lives. However, existing solutions either require dedicated hardware or employ special-purpose signals which are not cost-effective, significantly limiting their real-life applications. Also very few work concerns about the theory behind and can explain the large performance variations in different scenarios. In this paper, we employ the cheap commodity Wi-Fi hardware already ubiquitously deployed around us for respiration sensing. For the first time, we utilize the Fresnel diffraction model to accurately quantify the relationship between the diffraction gain and human target's subtle chest displacement and thus successfully turn the previously considered "destructive" obstruction diffraction in the First Fresnel Zone (FFZ) into beneficial sensing capability. By not just considering the chest displacement at the frontside as the existing solutions, but also the subtle displacement at the backside, we achieve surprisingly matching results with respect to the theoretical plots and become the first to clearly explain the theory behind the performance distinction between lying and sitting for respiration sensing. With two cheap commodity Wi-Fi cards each equipped with just one antenna, we are able to achieve higher than 98% accuracy of respiration rate monitoring at more than 60% of the locations in the FFZ. Furthermore, we are able to present the detail heatmap of the sensing capability at each location inside the FFZ to guide the respiration sensing so users clearly know where are the good positions for respiration monitoring and if located at a bad position, how to move just slightly to reach a good position.

In recent years, wireless sensing has been exploited as a promising research direction for contactless human activity recognition. However, one major issue hindering the real deployment of these systems is that the signal variation patterns induced by the human activities with different devices and environmental settings are neither stable nor consistent, resulting in unstable system performance. The existing machine learning based methods usually take the "black box" approach and fails to achieve consistent performance. In this paper, we argue that a deep understanding of radio signal propagation in wireless sensing is needed, and it may be possible to develop a deterministic sensing model to make the signal variation patterns predictable. With this intuition, in this paper we investigate: 1) how wireless signals are affected by human activities taking transceiver location and environment settings into consideration; 2) a new deterministic sensing approach to model the received signal variation patterns for different human activities; 3) a proof-of-concept prototype to demonstrate our approach and a case study to detect diverse activities. In particular, we propose a diffraction-based sensing model to quantitatively determine the signal change with respect to a target's motions, which eventually links signal variation patterns with motions, and hence can be used to recognize human activities. Through our case study, we demonstrate that the diffraction-based sensing model is effective and robust in recognizing exercises and daily activities. In addition, we demonstrate that the proposed model improves the recognition accuracy of existing machine learning systems by above 10%.

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Wireless signals have been extensively utilized for contactless sensing in the past few years. Due to the intrinsic nature of employing the weak target-reflected signal for sensing, the sensing range is limited. For instance, WiFi and RFID can achieve 3-6 meter sensing range while acoustic-based sensing is limited to less than one meter. In this work, we identify exciting sensing opportunities with LoRa, which is the new long-range communication technology designed for IoT communication. We explore the sensing capability of LoRa, both theoretically and experimentally. We develop the sensing model to characterize the relationship between target movement and signal variation, and propose novel techniques to increase LoRa sensing range to over 25 meters for human respiration sensing. We further build a prototype system which is capable of sensing both coarse-grained and fine-grained human activities. Experimental results show that (1) human respiration can still be sensed when the target is 25 meters away from the LoRa devices, and 15 meters away with a wall in between; and (2) human walking (both displacement and direction) can be tracked accurately even when the target is 30 meters away from the LoRa transceiver pair.