In this paper, we propose BeamSense, a completely novel approach to implement standard-compliant Wi-Fi sensing applications. Wi-Fi sensing enables game-changing applications in remote healthcare, home entertainment, and home surveillance, among others. However, existing work leverages the manual extraction of channel state information (CSI) from Wi-Fi chips to classify activities, which is not supported by the Wi-Fi standard and hence requires the usage of specialized equipment. On the contrary, BeamSense leverages the standard-compliant beamforming feedback information (BFI) to characterize the propagation environment. Conversely from CSI, the BFI (i) can be easily recorded without any firmware modification, and (ii) captures the multiple channels between the access point and the stations, thus providing much better sensitivity. BeamSense includes a novel cross-domain few-shot learning (FSL) algorithm to handle unseen environments and subjects with few additional data points. We evaluate BeamSense through an extensive data collection campaign with three subjects performing twenty different activities in three different environments. We show that our BFI-based approach achieves about 10% more accuracy when compared to CSI-based prior work, while our FSL strategy improves accuracy by up to 30% and 80% when compared with state-of-the-art cross-domain algorithms.

翻译：本文提出BeamSense，一种实现符合标准的Wi-Fi感知应用的完全新型方法。Wi-Fi感知在远程医疗、家庭娱乐和家庭监控等领域能够催生颠覆性应用。然而，现有工作依赖从Wi-Fi芯片手动提取信道状态信息来分类活动，这并非Wi-Fi标准所支持，因此需要使用专用设备。相反，BeamSense利用符合标准的波束赋形反馈信息来表征传播环境。与CSI不同，BFI（i）无需任何固件修改即可轻松记录，且（ii）捕获接入点与站点之间的多个信道，从而提供更好的灵敏度。BeamSense包含一种新型跨域小样本学习算法，以借助少量额外数据点处理未见环境和被测试对象。我们通过一项大规模数据采集活动对BeamSense进行评估，该活动由三位被测试对象在三种不同环境中执行二十种不同活动。结果表明，与基于CSI的先前工作相比，我们的基于BFI的方法准确率提高约10%，而我们的FSL策略与最先进的跨域算法相比，准确率提升高达30%和80%。