RuView: See Through Walls With WiFi — ESP32 Spatial Intelligence Platform
RuView turns ordinary WiFi signals into a spatial intelligence system. Detect people, measure breathing and heart rate, track movement through walls — no cameras, no wearables, just physics.
WiFi SensingESP32AI HardwareSpatial IntelligenceIoTPrivacy AI
Every WiFi router in your home is already a radar. It floods every room with radio waves. When someone moves, breathes, or even sits still, those waves scatter differently. RuView is an open-source platform that captures those disturbances and turns them into real spatial intelligence — and it works with a $9 ESP32 board.
No cameras. No wearables. No app on anyone's phone. Just physics.
TL;DR
Feature
Detail
Core hardware
ESP32-S3 ($9) or ESP32-C6 ($6–10)
Full system
Cognitum Seed (~$140 total BOM)
Sensing
Presence, breathing, heart rate, 17-keypoint pose, falls, room fingerprint
Range
Through-wall up to ~5 m (signal-dependent)
Privacy
No cameras, no wearables — GDPR/HIPAA imaging rules don't apply
AI integration
Claude Code plugin, MCP server, npx @ruvnet/ruview harness
Stars
74.9k on GitHub
How WiFi Sensing Actually Works
WiFi routers transmit on multiple subcarriers simultaneously. When the signal travels from an access point to a receiver (like an ESP32 node), every person or object in the room changes how each subcarrier arrives — a measurable effect called Channel State Information (CSI).
RuView reads CSI across 56 subcarriers per link. With a mesh of 3–6 ESP32 nodes, you get up to 168 virtual subcarriers across multiple viewing angles. That signal is enough to reconstruct what's happening in a room:
These map directly to Home Assistant entities for automation.
Why This Matters for AI Developers
WiFi sensing removes the hardest constraint in spatial AI: the camera.
Cameras require line-of-sight, consent, signage, data retention policies, and IR lighting in the dark. WiFi works in total darkness, through walls, through shelving, and in any space where WiFi already exists — which is everywhere.
Problem
Camera
WiFi (RuView)
Through-wall
No
Yes (up to ~5 m)
Dark rooms
Needs IR
Yes
Privacy/GDPR
Consent + signage
No imaging rules
Hardware cost
$200–$2,000/zone
$0–$8/zone (existing WiFi or ESP32)
Power
Constant + compute
$9 ESP32, runs for weeks on battery
The AI community has spent years improving computer vision models. RuView suggests a different question: what if the sensor itself was invisible, ubiquitous, and already in place?
Hardware Setup in 5 Minutes
Option 1: Docker (no hardware needed)
bash
docker pull ruvnet/wifi-densepose:latest
docker run -p 3000:3000 ruvnet/wifi-densepose:latest
# Open http://localhost:3000
RuView ships a complete Claude Code plugin that wraps the entire workflow into 9 skills, 7 slash commands, and 3 agents.
bash
# Add the marketplace and install
/plugin marketplace add ruvnet/RuView
/plugin install ruview@ruview
# Available commands:
/ruview-start # Onboarding: Docker demo / repo build / live ESP32
/ruview-flash # Build + flash ESP32 firmware
/ruview-provision # Provision WiFi credentials and mesh settings
/ruview-app # Run a sensing application
/ruview-train # Train / evaluate / publish a model
/ruview-advanced # Multistatic / tomography / cross-viewpoint
/ruview-verify # Tests + deterministic proof + witness bundle
For a lighter option that doesn't require cloning the repo:
bash
npx @ruvnet/ruview
The portable harness exposes RuView operator tools (onboard, verify, node_monitor, calibrate, node_flash) over an MCP server and works with Claude Code, Codex, Copilot, and other AI coding environments.
Smart Home Integration
RuView publishes 21 entities per node to any of the major home automation ecosystems:
Home Assistant: MQTT auto-discovery with 3 starter Blueprints (presence, fall alert, sleep quality)
Apple Home: HAP-1.1 bridge — pairs as a native HomeKit accessory
Google Home + Alexa: Via the Matter bridge or the same HA-DISCO publisher
Matter: Full Matter support via the HA bridge
This means Alexa can announce "possible distress detected in bedroom" without any skill development, because RuView maps its semantic states directly to standard smart-home entities.
AI: world model prediction (OccWorld), 3D point-cloud fusion (camera + WiFi + mmWave)
Browse and install at seed.cognitum.one/store or your local appliance.
Use Cases Worth Knowing
Healthcare: Contactless breathing rate and heart rate monitoring for patients who can't wear devices. Sleep-stage classification and apnea screening without a sleep clinic. Fall detection for elderly residents with immediate alerts.
Retail: Customer-flow counting without cameras, queue-length monitoring, occupancy zones across a store floor — all without any video that would require consent or compliance overhead.
Office: Meeting-room occupancy detection for space utilization, presence-based HVAC triggers, multi-room transition tracking.
Defense / Search and rescue: Through-wall sensing for situational awareness in collapsed buildings, hostage situations, or search operations where cameras cannot be deployed.
Robotics: Android and robot spatial awareness without cameras — relevant for any embodied AI system that operates in human environments.
What to Watch
A few known limitations and in-progress items:
The sensing-server's --model flag doesn't yet accept the Hugging Face JSONL format (RVFS binary parser mismatch). Use the Python weights for now.
Camera-free pose estimation accuracy is limited without ground-truth paired data (~2.5% PCK@20 with proxy labels). The camera-supervised fine-tune pipeline is implemented but pending data collection.
ESP32-C3 and original ESP32 are not supported (single-core, insufficient for CSI DSP).
Single ESP32 deployments have limited spatial resolution — 2+ nodes recommended.
Getting Started
bash
# Quickest path — no hardware, runs in Docker
docker run -p 3000:3000 ruvnet/wifi-densepose:latest
# Python
pip install ruview
# Claude Code
npx @ruvnet/ruview
# Full repo
git clone https://github.com/ruvnet/RuView
RuView is MIT-licensed with 74.9k stars, 10k forks, and 30 contributors. The GitHub repository has full documentation, ADRs, domain models, and a complete benchmark trail.
The physics of WiFi has been hiding in plain sight. RuView just makes it readable.
