Recent years have witnessed an increasing demand for human fall detection systems. Among all existing methods, Wi-Fi-based fall detection has become one of the most promising solutions due to its pervasiveness. However, when applied to a new domain, existing Wi-Fi-based solutions suffer from severe performance degradation caused by low generalizability. In this paper, we propose XFall, a domain-adaptive fall detection system based on Wi-Fi. XFall overcomes the generalization problem from three aspects. To advance cross-environment sensing, XFall exploits an environment-independent feature called speed distribution profile, which is irrelevant to indoor layout and device deployment. To ensure sensitivity across all fall types, an attention-based encoder is designed to extract the general fall representation by associating both the spatial and temporal dimensions of the input. To train a large model with limited amounts of Wi-Fi data, we design a cross-modal learning framework, adopting a pre-trained visual model for supervision during the training process. We implement and evaluate XFall on one of the latest commercial wireless products through a year-long deployment in real-world settings. The result shows XFall achieves an overall accuracy of 96.8%, with a miss alarm rate of 3.1% and a false alarm rate of 3.3%, outperforming the state-of-the-art solutions in both in-domain and cross-domain evaluation.

As the range of the non-line-of-sight (NLOS) ultraviolet (UV) communication increases, atmospheric turbulence will become one of the primary atmospheric processes that affect the propagation of optical waves besides atmospheric absorption and scattering. This paper analyzes scintillation attenuation (SA), indicating that it should not be ignored for long communication range and relatively strong turbulence. A turbulence model considering SA is proposed, and the marginal probability density function (PDF) of the received optical power is derived. Then, the signal-to-noise ratio (SNR) and the bit-error-rate (BER) of NLOS UV communication systems are analyzed, showing that atmospheric turbulence induces greater performance deteriorations due to SA.