AM-FM: A Foundation Model for Ambient Intelligence Through WiFi

TL;DR

AM-FM is a pioneering foundation model that leverages WiFi signals to enable scalable ambient intelligence, demonstrating strong performance across multiple tasks with limited labeled data.

Contribution

This work introduces the first foundation model for WiFi-based ambient intelligence, trained on extensive unlabeled data, enabling versatile and efficient sensing in smart environments.

Findings

Strong cross-task performance on public benchmarks

Improved data efficiency over task-specific models

Demonstrates feasibility of scalable ambient intelligence using WiFi

Abstract

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.