  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.
