用海量无线信号训练通用感知模型，让现有路由器也能智能识别人体状态。
AM-FM: A Foundation Model for Ambient Intelligence Through WiFi
- 基于对比学习和物理约束预训练，从920万条未标注信道状态信息中提取通用特征
- 在9个下游任务上实现跨任务性能提升，数据效率显著优于传统方法
- 适合想用现成WiFi设备做智能环境感知的研究者与开发者
环境智能旨在持续理解物理空间中的人体存在、活动及生理状态，是智慧环境、健康监测与人机交互的基础。WiFi基础设施为这一能力提供了无处不在、始终在线且隐私友好的基础支撑，覆盖数十亿物联网设备。然而，无线传感长期依赖特定任务的模型，需大量标注数据，限制了实际部署。本文提出AM-FM，首个通过WiFi实现环境智能的基座模型。AM-FM在439天内从20种商用设备类型收集的920万条未标注信道状态信息（CSI）上进行预训练，采用对比学习、掩码重建与物理信息目标联合优化，学习通用表征。在涵盖九项下游任务的公开基准上评估，显示其具备优异的跨任务表现与数据效率，证明基座模型可借助现有无线基础设施实现可扩展的环境智能。
原文摘要 · Abstract (English)
Ambient intelligence, continuously understanding human presence, activity, and physiology in physical spaces, is fundamental to smart environments, health monitoring, and human-computer interaction. WiFi infrastructure provides a ubiquitous, always-on, privacy-preserving substrate for this capability across billions of IoT devices. Yet this potential remains largely untapped, as wireless sensing has typically relied on task-specific models that require substantial labeled data and limit practical deployment. We present AM-FM, the first foundation model for ambient intelligence and sensing through WiFi. AM-FM is pre-trained on 9.2 million unlabeled Channel State Information (CSI) samples collected over 439 days from 20 commercial device types deployed worldwide, learning general-purpose representations via contrastive learning, masked reconstruction, and physics-informed objectives tailored to wireless signals. Evaluated on public benchmarks spanning nine downstream tasks, AM-FM shows strong cross-task performance with improved data efficiency, demonstrating that foundation models can enable scalable ambient intelligence using existing wireless infrastructure.
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