Recent advancements in Ultra-Wideband (UWB) radar technology have enabled contactless, non-line-of-sight vital sign monitoring, making it a valuable tool for healthcare. However, UWB radar's ability to capture sensitive physiological data, even through walls, raises significant privacy concerns, particularly in human-robot interactions and autonomous systems that rely on radar for sensing human presence and physiological functions. In this paper, we present Anti-Sensing, a novel defense mechanism designed to prevent unauthorized radar-based sensing. Our approach introduces physically realizable perturbations, such as oscillatory motion from wearable devices, to disrupt radar sensing by mimicking natural cardiac motion, thereby misleading heart rate (HR) estimations. We develop a gradient-based algorithm to optimize the frequency and spatial amplitude of these oscillations for maximal disruption while ensuring physiological plausibility. Through both simulations and real-world experiments with radar data and neural network-based HR sensing models, we demonstrate the effectiveness of Anti-Sensing in significantly degrading model accuracy, offering a practical solution for privacy preservation.

翻译：超宽带雷达技术的近期进展实现了非接触式、非视距的生命体征监测，使其成为医疗保健领域的宝贵工具。然而，超宽带雷达能够捕获敏感生理数据（甚至能穿透墙壁），这引发了严重的隐私担忧，尤其是在依赖雷达感知人体存在与生理功能的人机交互及自主系统中。本文提出"反感知"——一种旨在防止未经授权的雷达感知的新型防御机制。我们的方法通过引入物理可实现的扰动（例如来自穿戴式设备的振荡运动），通过模拟自然心脏运动来干扰雷达感知，从而误导心率估计。我们开发了一种基于梯度的算法，以优化这些振荡的频率与空间幅度，在确保生理合理性的同时实现最大程度的干扰。通过雷达数据与基于神经网络的心率感知模型的仿真及真实实验，我们证明了"反感知"能显著降低模型准确度，为隐私保护提供了一种实用解决方案。