π RuView

Start here	Hardware	Models
AI toolkit	Smart home	Cognitum Seed

See through walls with WiFi

Turn ordinary WiFi into a spatial intelligence / sensing system. Detect people, measure breathing and heart rate, track movement, and monitor rooms — through walls, in the dark, with no cameras or wearables. Just physics.

Works natively with the four major smart-home ecosystems: Home Assistant via the HA-DISCO MQTT publisher, Apple Home & HomePod as a discoverable HAP-1.1 bridge, Google Home + Amazon Alexa via the same HA bridge or a Matter endpoint. Siri, Google Assistant, and Alexa can voice presence and vitals by room with zero custom skills.

Drop into any Home Assistant install with one --mqtt flag. Or pair into Apple Home / Google Home / Alexa / SmartThings as a Matter Bridge. Ships 21 entities per node (11 raw signals + 10 inferred semantic states: someone-sleeping, possible-distress, room-active, elderly-inactivity-anomaly, meeting-in-progress, bathroom-occupied, fall-risk-elevated, bed-exit, no-movement, multi-room-transition) plus 3 starter HA Blueprints. See docs/integrations/home-assistant.md · ADR-115.

π RuView is a WiFi sensing platform that turns radio signals into spatial intelligence.

Every WiFi router already fills your space with radio waves. When people move, breathe, or even sit still, they disturb those waves in measurable ways. RuView captures these disturbances using Channel State Information (CSI) from low-cost ESP32 sensors and turns them into actionable data: who's there, what they're doing, and whether they're okay.

What it senses: - Presence and occupancy — detect people through walls, count them, track entries and exits - Vital signs — breathing rate and heart rate, contactless, while sleeping or sitting - Activity recognition — walking, sitting, gestures, falls — from temporal CSI patterns - Environment mapping — RF fingerprinting identifies rooms, detects moved furniture, spots new objects - Sleep quality — overnight monitoring with sleep stage classification and apnea screening

Also included:

• Camera-free pose — estimate 17 body keypoints from WiFi CSI
• Built-in model workflow — record CSI, train models, load RVF files, and switch LoRA profiles
• Local automation — HOMECORE provides state, history, automations, signed Wasm plugins, voice hooks, and HomeKit support
• Unified RF world model — combine WiFi CSI, radar, UWB, and cellular sensing in one privacy-bounded scene model; accuracy is still synthetic until real-data validation
• Governed evidence — attach privacy policy, uncertainty, provenance, and witness records to sensing events
• RuView MetaHarness — use an AI operator to onboard, calibrate, train, verify, and check sensing claims

Presence and motion	Breathing and vital signals
Pose research and limitations	Edge sensor mesh
Local automation	Guided AI operator