用于无源雷达的Wi-Fi感知数据神经符号融合与跨模态知识迁移
Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge Transfer
AI总结：
本文提出神经符号架构DeepProbHAR，通过融合Wi-Fi多天线信号并利用摄像头领域知识，在不额外标注数据的情况下实现人类活动识别，并生成与精细标注模型精度相当的简单动作分类器。
摘要：
Wi-Fi设备类似于无源雷达，由于人体与电磁信号的相互作用，能够辨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，将通信信道物理属性的波动与引起这些波动的人类活动关联起来。然而，这些技术往往缺乏所期望的灵活性和透明性。本文介绍了DeepProbHAR，一种用于Wi-Fi感知的神经符号架构，提供了初步证据表明Wi-Fi信号能够区分简单动作，例如腿部或手臂动作，这些动作是跑步或行走等人类活动的组成部分。该神经符号方法能够在不进行额外专门数据收集或标注的情况下获取此类证据。DeepProbHAR的训练借助来自摄像头馈送的声明式领域知识，并通过融合Wi-Fi接收器多个天线的信号来促进。DeepProbHAR在人类活动识别方面取得了与最先进水平相当的结果。此外，作为学习过程的副产品，DeepProbHAR生成了针对简单动作的专用分类器，其准确度与在精细标注数据集上训练的模型相匹配，而这类数据集的获取成本尤其高昂。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
