RF-Net: a Unified Meta-learning Framework for RF-enabled One-shot Human Activity Recognition

TL;DR

RF-Net is a meta-learning framework that enables rapid one-shot human activity recognition using RF signals, significantly reducing environment adaptation effort and outperforming existing methods in real-world indoor settings.

Contribution

The paper introduces RF-Net, a novel meta-learning approach with a dual-path RF feature extractor and RF-specific metric module for fast environment adaptation in RF-based HAR.

Findings

RF-Net outperforms state-of-the-art baselines in multiple indoor environments.

The dual-path network effectively captures spatial and temporal RF features.

Meta-learning enhances rapid adaptation with minimal labeled data.

Abstract

Radio-Frequency (RF) based device-free Human Activity Recognition (HAR) rises as a promising solution for many applications. However, device-free (or contactless) sensing is often more sensitive to environment changes than device-based (or wearable) sensing. Also, RF datasets strictly require on-line labeling during collection, starkly different from image and text data collections where human interpretations can be leveraged to perform off-line labeling. Therefore, existing solutions to RF-HAR entail a laborious data collection process for adapting to new environments. To this end, we propose RF-Net as a meta-learning based approach to one-shot RF-HAR; it reduces the labeling efforts for environment adaptation to the minimum level. In particular, we first examine three representative RF sensing techniques and two major meta-learning approaches. The results motivate us to innovate in two designs: i) a dual-path base HAR network, where both time and frequency domains are dedicated to learning powerful RF features including spatial and attention-based temporal ones, and ii) a metric-based meta-learning framework to enhance the fast adaption capability of the base network, including an RF-specific metric module along with a residual classification module. We conduct extensive experiments based on all three RF sensing techniques in multiple real-world indoor environments; all results strongly demonstrate the efficacy of RF-Net compared with state-of-the-art baselines.