Wi-Fi devices, akin to passive radars, can discern human activities within indoor settings due to the human body's interaction with electromagnetic signals. Current Wi-Fi sensing applications predominantly employ data-driven learning techniques to associate the fluctuations in the physical properties of the communication channel with the human activity causing them. However, these techniques often lack the desired flexibility and transparency. This paper introduces DeepProbHAR, a neuro-symbolic architecture for Wi-Fi sensing, providing initial evidence that Wi-Fi signals can differentiate between simple movements, such as leg or arm movements, which are integral to human activities like running or walking. The neuro-symbolic approach affords gathering such evidence without needing additional specialised data collection or labelling. The training of DeepProbHAR is facilitated by declarative domain knowledge obtained from a camera feed and by fusing signals from various antennas of the Wi-Fi receivers. DeepProbHAR achieves results comparable to the state-of-the-art in human activity recognition. Moreover, as a by-product of the learning process, DeepProbHAR generates specialised classifiers for simple movements that match the accuracy of models trained on finely labelled datasets, which would be particularly costly.

翻译：Wi-Fi设备类似于被动雷达，能够基于人体与电磁信号的相互作用来识别室内环境中的人类活动。当前的Wi-Fi感知应用主要采用数据驱动的学习技术，将通信信道物理特性的波动与引发这些波动的人类活动相关联。然而，这些技术通常缺乏所需的灵活性与可解释性。本文提出了一种用于Wi-Fi感知的神经符号架构DeepProbHAR，其初步证据表明Wi-Fi信号能够区分简单动作（如腿部或手臂运动），这些动作是跑步或行走等人类活动的基本组成部分。该神经符号方法无需额外专门的数据收集或标注即可获取此类证据。DeepProbHAR的训练通过从摄像头馈送中获取的声明性领域知识以及融合来自Wi-Fi接收器多个天线的信号来实现。DeepProbHAR在人类活动识别任务中取得了与最先进方法相当的结果。此外，作为学习过程的副产品，DeepProbHAR能够生成针对简单动作的专用分类器，其准确率与在精细标注数据集上训练的模型相当，而后者通常需要极高的标注成本。