用大模型直接分析无线信号，零样本识别人体动作。
Wi-Chat: Large Language Model Powered Wi-Fi Sensing
- 把无线传感原理嵌入提示词，让大模型理解原始信号
- 在真实数据集上实现零样本动作识别，无需传统处理流程
- 为无线感知提供新思路，适合想拓展大模型应用的开发者
大型语言模型（LLMs）在多种任务中展现出卓越能力，但其在融合物理模型知识以解释真实信号方面的潜力尚未被充分探索。本文提出Wi-Chat，首个基于大模型的Wi-Fi人体活动识别系统。我们证明，通过将Wi-Fi感知原理融入提示词，大模型可直接处理原始Wi-Fi信号并推断人体活动。该方法利用物理模型先验指导大模型解读信道状态信息（CSI）数据，无需传统信号处理步骤。在真实世界Wi-Fi数据集上的实验表明，大模型具备强大推理能力，实现了零样本活动识别。这些发现揭示了无线感知的新范式，拓展了大模型的应用边界，提升了无线感知技术在实际部署中的可及性。
原文摘要 · Abstract (English)
Recent advancements in Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks. However, their potential to integrate physical model knowledge for real-world signal interpretation remains largely unexplored. In this work, we introduce Wi-Chat, the first LLM-powered Wi-Fi-based human activity recognition system. We demonstrate that LLMs can process raw Wi-Fi signals and infer human activities by incorporating Wi-Fi sensing principles into prompts. Our approach leverages physical model insights to guide LLMs in interpreting Channel State Information (CSI) data without traditional signal processing techniques. Through experiments on real-world Wi-Fi datasets, we show that LLMs exhibit strong reasoning capabilities, achieving zero-shot activity recognition. These findings highlight a new paradigm for Wi-Fi sensing, expanding LLM applications beyond conventional language tasks and enhancing the accessibility of wireless sensing for real-world deployments.
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