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.

翻译：本论文探索了利用被动雷达进行人类活动识别（HAR）的新方法，重点关注非侵入式的Wi-Fi信道状态信息（CSI）数据。传统的HAR方法通常使用摄像头或可穿戴设备等侵入式传感器，引发了隐私问题。本研究利用CSI的非侵入特性，采用脉冲神经网络（SNN）来解读由人体运动引起的信号变化。这些网络与DeepProbLog等符号推理框架相结合，增强了HAR系统的适应性和可解释性。SNN具有较低的功耗，非常适合对隐私敏感的应用场景。实验结果表明，基于SNN的神经符号模型实现了高精度，使其成为跨多个领域的HAR的一种有前景的替代方案。