WiFi Signals Used to Track Human Movements Through Walls by Mapping Body Keypoints

An open-source artificial intelligence project named RuView, formerly known as WiFi DensePose, rose to the top of GitHub’s trending repositories.

The project stunned the cybersecurity community by proving that ordinary WiFi signals can sense human movement, body posture, and even vital signs through walls, all without using cameras or wearable devices.

While researchers describe RuView as a privacy-preserving tool for emergency rescue and elderly monitoring, experts warn it could mark the dawn of an era of undetectable physical surveillance.

Sponsored

class="wp-block-heading" id="how-the-technology-works">How the Technology Works

According to its GitHub documentation, RuView analyzes Channel State Information (CSI), the tiny variations in WiFi radio waves as they bounce around a room and reflect off human bodies.

Unlike typical signal strength readings, CSI captures detailed amplitude and phase data across multiple WiFi subcarriers.

RuView’s creators built it on research from Carnegie Mellon University and designed it to run on affordable hardware.

A mesh of just four to six ESP32-S3 WiFi sensor nodes, costing around $54 in total, creates a radio frequency map of a space. As people move, breathe, or even shift their weight, their bodies distort these signals.

Powered by deep neural networks and a highly optimized Rust codebase, RuView analyzes signal fluctuations at about 54,000 frames per second.

It can map 17 body keypoints, including the head, elbows, and knees, with accuracy comparable to traditional optical motion-capture systems.

The system operates fully on edge devices, requiring no internet connectivity or cloud processing, which makes its operation difficult to detect.

The same qualities that make RuView innovative also make it a potential privacy nightmare. Because WiFi waves pass through walls, attackers could hide ESP32 microchips outside offices or residential buildings to map movement patterns or detect occupancy anonymously.

Darkness, smoke, or physical obstructions would pose no barrier that a standard video camera cannot overcome.

Even the strongest WPA3 encryption offers no defense because the system does not intercept network traffic; it passively listens to how WiFi signals behave in the environment.

This method leaves no digital footprint, eluding firewalls and traditional intrusion detection systems.

Current mitigation strategies are mostly experimental. CSI randomization at the router level may distort the data that attackers rely on, but it remains a research concept.

Physical radio-frequency shielding using Faraday cages or RF-blocking paint offers the most reliable protection.

Other methods include injecting RF noise or reducing WiFi signal range, though these solutions trade usability for privacy.

As RuView highlights, the boundaries between network security and physical privacy are rapidly disappearing, raising urgent questions about how invisible sensing should be regulated and controlled.

Follow us on Google News , LinkedIn and X to Get More Instant Updates. Set Cyberpress as a Preferred Source in Google.

The post WiFi Signals Used to Track Human Movements Through Walls by Mapping Body Keypoints appeared first on Cyber Security News.