Intelligent Metasurface Imager and Recognizer

TL;DR

This paper introduces a smart metasurface system that uses neural networks to perform real-time imaging and recognition of humans and their gestures using Wi-Fi signals, without requiring cooperation or active tags.

Contribution

It presents a novel integrated metasurface and neural network framework capable of in-situ human imaging and gesture recognition at Wi-Fi frequencies, even with passive signals.

Findings

Successful real-time imaging and recognition demonstrated

Operates effectively with passive stray Wi-Fi signals

Works for multiple non-cooperative people

Abstract

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.