用非侵入式Wi-Fi信号识别动作，结合神经符号模型提升隐私与可解释性。
Approaches to human activity recognition via passive radar
- 基于Wi-Fi信道状态信息，用脉冲神经网络捕捉人体动作信号变化
- 神经符号模型在实验中实现高精度动作识别，适用于多场景
- 低功耗设计适合隐私敏感应用，兼具可解释性与适应性
本论文研究利用被动雷达进行人体活动识别（HAR）的新方法，重点聚焦于非侵入式的Wi-Fi信道状态信息（CSI）数据。传统HAR方法常依赖摄像头或可穿戴传感器，引发隐私担忧。本研究利用CSI的非侵入特性，采用脉冲神经网络（SNN）解析由人体运动引起的信号变化，并结合深度概率逻辑（DeepProbLog）等符号推理框架，提升HAR系统的适应性与可解释性。SNN具有低功耗优势，适用于隐私敏感场景。实验结果表明，基于SNN的神经符号模型实现了高精度动作识别，展现出在多种应用场景中的广阔前景。
原文摘要 · Abstract (English)
The thesis explores novel methods for Human Activity Recognition (HAR) using passive radar with a focus on non-intrusive Wi-Fi Channel State Information (CSI) data. Traditional HAR approaches often use invasive sensors like cameras or wearables, raising privacy issues. This study leverages the non-intrusive nature of CSI, using Spiking Neural Networks (SNN) to interpret signal variations caused by human movements. These networks, integrated with symbolic reasoning frameworks such as DeepProbLog, enhance the adaptability and interpretability of HAR systems. SNNs offer reduced power consumption, ideal for privacy-sensitive applications. Experimental results demonstrate SNN-based neurosymbolic models achieve high accuracy making them a promising alternative for HAR across various domains.
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