  It is ever-increasingly demanded to remotely monitor people in daily life
using radio-frequency probing signals. However, conventional systems can hardly
be deployed in real-world settings since they typically require objects to
either deliberately cooperate or carry a wireless active device or
identification tag. To accomplish the complicated successive tasks using a
single device in real time, we propose a smart metasurface imager and
recognizer simultaneously, empowered by a network of artificial neural networks
(ANNs) for adaptively controlling data flow. Here, three ANNs are employed in
an integrated hierarchy: transforming measured microwave data into images of
whole human body; classifying the specifically designated spots (hand and
chest) within the whole image; and recognizing human hand signs instantly at
Wi-Fi frequency of 2.4 GHz. Instantaneous in-situ imaging of full scene and
adaptive recognition of hand signs and vital signs of multiple non-cooperative
people have been experimentally demonstrated. We also show that the proposed
intelligent metasurface system work well even when it is passively excited by
stray Wi-Fi signals that ubiquitously exist in our daily lives. The reported
strategy could open a new avenue for future smart cities, smart homes,
human-device interactive interfaces, healthy monitoring, and safety screening
free of visual privacy issues.
