Ruview README

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

RuView MetaHarness — guided operation for humans and AI agents

The RuView-specific metaharness we created is published as @ruvnet/ruview. It provides:

source-cited guidance;
device access for CSI nodes, radar and LiDAR;
firmware flashing with boot evidence;
one MCP server, usable over stdio or HTTP for ChatGPT, with a live console widget;
a Claude Code mod;
guarded Claude Code/Codex agents;
deterministic verification;
an honesty check for accuracy claims.

Companion packages: homecore (Homecore developer metaharness) and @ruvnet/ruview-kernel (the vitals pipeline as WASM).

bash

# Check the local setup and get source-cited guidance
npx @ruvnet/ruview@0.9.1 doctor
npx @ruvnet/ruview@0.9.1 guidance --topic sensing --query "model loading"

# Hardware: what is plugged in, live CSI from your nodes, a 60 GHz radar kit
npx @ruvnet/ruview@0.9.1 devices
npx @ruvnet/ruview@0.9.1 esp32 --watch                       # ESP32 + Realtek RAC1 nodes, live view
npx @ruvnet/ruview@0.9.1 esp32 --seconds 45 --analyze        # live CSI through the vitals kernel
npx @ruvnet/ruview@0.9.1 mmwave --source esphome --host <kit-ip>

# Agents: MCP over stdio, or over HTTP for ChatGPT (token-protected, read tools only)
npx @ruvnet/ruview@0.9.1 mcp start
RUVIEW_MCP_GRANTS=device-access npx @ruvnet/ruview@0.9.1 mcp start --http

# Claude Code: a live sensing pane (/ruview), shipped in the package
npx @ruvnet/ruview@0.9.1 mod

# Run a read-only RuView agent through Codex
npx @ruvnet/ruview@0.9.1 agent run --host codex --repo . \
  --prompt "Find the nearest tests and cite the source files"

# Check claims, replay the deterministic proof, search the reviewed brain
npx @ruvnet/ruview@0.9.1 claim-check --file REPORT.md
npx @ruvnet/ruview@0.9.1 verify
npx @ruvnet/ruview@0.9.1 brain search --query "calibration"

Safety:

Agent runs are read-only by default. Workspace writes require both --allow-write and --confirm.
Hardware reads need the device-access grant.
Flashing and calibration never run over the HTTP transport.

Built on RuVector and Cognitum Seed, RuView runs entirely on edge hardware — an ESP32 mesh (as low as $9 per node) paired with a Cognitum Seed for persistent memory, cryptographic attestation, and AI integration. No cloud, no cameras, no internet required.

The system learns each environment locally using spiking neural networks that adapt in under 30 seconds, with multi-frequency mesh scanning across 6 WiFi channels that uses your neighbors' routers as free radar illuminators. Every measurement is cryptographically attested via an Ed25519 witness chain.

RuView turns ordinary WiFi into a contactless sensor. A $9 ESP32 board reads the radio reflections off the people in a room, and a small pretrained model — published on Hugging Face at ruvnet/wifi-densepose-pretrained — tells you who's there, how they're breathing, and how their heart rate is trending. The model fits in 8 KB (4-bit quantized) and runs in microseconds on a Raspberry Pi. (The v2 encoder reports an honest, label-free held-out temporal-triplet accuracy of 82.3% — up from 66.4% raw; the older "100% presence" figure was measured on a single-class recording and has been retracted in favor of this.) No cameras, no wearables, no app on the user's phone.

Built for low-power edge applications

Edge modules are small programs that run directly on the ESP32 sensor — no internet needed, no cloud fees, instant response.

What How Speed / scale
🫁 Breathing rate Bandpass 0.1–0.5 Hz on wrapped phase, circular variance, zero-crossing BPM (#593) 6–30 BPM, real-time
💓 Heart rate Bandpass 0.8–2.0 Hz, zero-crossing BPM 40–120 BPM, real-time
👤 Presence detection Trained head on Hugging Face (ruvnet/wifi-densepose-pretrained; v2 encoder = 82.3% held-out temporal-triplet acc, honestly re-benchmarked) + a phase-variance fallback that needs no model < 1 ms, ~30 s ambient calibration
🧬 CSI embeddings 128-dim contrastive encoder shipped on Hugging Face, 4-bit quantised variant fits in 8 KB 164,183 emb/s on M4 Pro
🦴 17-keypoint pose estimation cog-pose-estimation Cog v0.0.1 — signed aarch64 + x86_64 binaries on GCS, loads pose_v1.safetensors via Candle (the committed pose_v1 is a first-cut on-device model: PCK@20 = 3.0%, below the ADR-079 ≥35% target, and its runtime path is still a confidence=0 stub — see Model weights: what's real, what's not; the 82.69% figure below is the separate published MM-Fi benchmark, not this live cog). Train your own from paired data in 2.1 s on an RTX 5080 (ADR-101, benchmarks). SOTA on MM-Fi: hits (ensemble 83.59%), beating MultiFormer (72.25%) and CSI2Pose (68.41%) on the matched MM-Fi protocol — self-corrected and auditable on

What	How	Speed / scale
🫁 Breathing rate	Bandpass 0.1–0.5 Hz on wrapped phase, circular variance, zero-crossing BPM (#593)	6–30 BPM, real-time
💓 Heart rate	Bandpass 0.8–2.0 Hz, zero-crossing BPM	40–120 BPM, real-time
👤 Presence detection	Trained head on Hugging Face (`ruvnet/wifi-densepose-pretrained`; v2 encoder = 82.3% held-out temporal-triplet acc, honestly re-benchmarked) + a phase-variance fallback that needs no model	< 1 ms, ~30 s ambient calibration
🧬 CSI embeddings	128-dim contrastive encoder shipped on Hugging Face, 4-bit quantised variant fits in 8 KB	164,183 emb/s on M4 Pro
🦴 17-keypoint pose estimation	`cog-pose-estimation` Cog v0.0.1 — signed aarch64 + x86_64 binaries on GCS, loads `pose_v1.safetensors` via Candle (the committed `pose_v1` is a first-cut on-device model: PCK@20 = 3.0%, below the ADR-079 ≥35% target, and its runtime path is still a `confidence=0` stub — see Model weights: what's real, what's not; the 82.69% figure below is the separate published MM-Fi benchmark, not this live cog). Train your own from paired data in 2.1 s on an RTX 5080 (ADR-101, benchmarks). SOTA on MM-Fi: hits (ensemble 83.59%), beating MultiFormer (72.25%) and CSI2Pose (68.41%) on the matched MM-Fi protocol — self-corrected and auditable on

Quick start options — Docker, ESP32-S3/C6, Cognitum Seed, and Python

server.ts

# Option 1: Docker (simulated data, no hardware needed)
docker pull ruvnet/wifi-densepose:latest
export RUVIEW_API_TOKEN=$(openssl rand -hex 32)   # required; the container exits 64 without it
docker run -p 127.0.0.1:3000:3000 -e RUVIEW_API_TOKEN ruvnet/wifi-densepose:latest
# Open http://localhost:3000
# ESP32 nodes in Docker also need a UDP source guard (RUVIEW_UDP_ALLOW);
# see docs/user-guide.md "Receiving ESP32 frames in Docker".

# Option 2a: Live sensing with ESP32-S3 hardware ($9)
# Flash firmware, provision WiFi, and start sensing:
python -m esptool --chip esp32s3 --port COM9 --baud 460800 \
  write_flash 0x0 bootloader.bin 0x8000 partition-table.bin \
  0xf000 ota_data_initial.bin 0x20000 esp32-csi-node.bin
python firmware/esp32-csi-node/provision.py --port COM9 \
  --ssid "YourWiFi" --password "secret" --target-ip 192.168.1.20

# Option 2b: WiFi 6 + 802.15.4 research sensing with ESP32-C6 ($6-10, ADR-110)
# Same csi-node firmware compiled for the C6 target — picks up the C6
# overlay (sdkconfig.defaults.esp32c6) automatically.
cd firmware/esp32-csi-node
idf.py set-target esp32c6 && idf.py build
idf.py -p COM6 flash
# C6 boot extras (vs S3): HE-LTF subcarrier tagging in ADR-018 bytes 18-19,
#   802.15.4 mesh time-sync on channel 15, TWT setup when the AP supports it,
#   opt-in LP-core wake-on-motion for ~5 µA battery seed nodes.
# v0.6.7 adds: real LP-core RISC-V motion-gate program (debounce + motion
#   counter) and a Wi-Fi 6 soft-AP with TWT Responder so two C6 boards can
#   benchmark real iTWT without buying an 11ax router. Both default off,
#   flip CONFIG_C6_{LP_CORE,SOFTAP_HE}_ENABLE to turn them on.

# Option 3: Full system with Cognitum Seed ($140)
# ESP32 streams CSI → bridge forwards to Seed for persistent storage + kNN + witness chain
node scripts/rf-scan.js --port 5006           # Live RF room scan
node scripts/snn-csi-processor.js --port 5006  # SNN real-time learning
node scripts/mincut-person-counter.js --port 5006  # Correct person counting

# Option 4: Python — live on PyPI (ADR-117)
pip install ruview                        # or: pip install wifi-densepose
# Both ship the same compiled PyO3 wheel (~250 KB, abi3-py310, Linux/macOS/Windows).
# Add [client] for the asyncio WebSocket + paho-mqtt clients:
pip install "ruview[client]"              # or: pip install "wifi-densepose[client]"

# from ruview import BreathingExtractor, HeartRateExtractor   # equivalent to:
# from wifi_densepose import BreathingExtractor, HeartRateExtractor
# from ruview.client import SensingClient, RuViewMqttClient

[!NOTE] CSI-capable hardware recommended. Presence, vital signs, through-wall sensing, and all advanced capabilities require Channel State Information (CSI) from an ESP32-S3 ($9) or research NIC. The Docker image runs with simulated data for evaluation. Consumer WiFi laptops provide RSSI-only presence detection.

Hardware options for live CSI capture:

Option Hardware Cost Full CSI Capabilities
ESP32 + Cognitum Seed (recommended) ESP32-S3 + Cognitum Seed ~$140 Yes Presence, motion, breathing, heart rate, fall detection, multi-person counting, 17-keypoint pose (signed Cog binary — first-cut on-device model, see Model weights: what's real, what's not), 105-cog catalog, persistent vector store, kNN search, witness chain, MCP proxy
ESP32 Mesh 3-6× ESP32-S3 + WiFi router ~$54 Yes Same capabilities as above without the persistent-memory features
ESP32-C6 research node (ADR-110, witness, reviewer guide, firmware v0.7.0) ESP32-C6-DevKit ($6–10) ~$10 Yes (Wi-Fi 6 capable) Dual-target CSI with 99.56% measured ESP-NOW sync match and measured HE-LTF capture on IDF 5.5.2. TWT and ~5 µA operation still need hardware validation.
Research NIC Intel 5300 / Atheros AR9580 ~$50-100 Yes Full CSI with 3x3 MIMO
Qualcomm CSI beta (ADR-268)

Option	Hardware	Cost	Full CSI	Capabilities
ESP32 + Cognitum Seed (recommended)	ESP32-S3 + Cognitum Seed	~$140	Yes	Presence, motion, breathing, heart rate, fall detection, multi-person counting, 17-keypoint pose (signed Cog binary — first-cut on-device model, see Model weights: what's real, what's not), 105-cog catalog, persistent vector store, kNN search, witness chain, MCP proxy
ESP32 Mesh	3-6× ESP32-S3 + WiFi router	~$54	Yes	Same capabilities as above without the persistent-memory features
ESP32-C6 research node (ADR-110, witness, reviewer guide, firmware v0.7.0)	ESP32-C6-DevKit ($6–10)	~$10	Yes (Wi-Fi 6 capable)	Dual-target CSI with 99.56% measured ESP-NOW sync match and measured HE-LTF capture on IDF 5.5.2. TWT and ~5 µA operation still need hardware validation.
Research NIC	Intel 5300 / Atheros AR9580	~$50-100	Yes	Full CSI with 3x3 MIMO
Qualcomm CSI beta (ADR-268)

WiFi DensePose — Live pose detection with setup guide

Real-time pose skeleton from WiFi CSI signals — no cameras, no wearables (demo visualization; the live CSI-only single-ESP32 17-keypoint model is still first-cut — see Model weights: what's real, what's not)

▶ Live Observatory Demo | ▶ Dual-Modal Pose Fusion Demo | ▶ Live 3D Point Cloud | ▶ three.js Demos (5)

The server is optional for visualization and aggregation — the ESP32 runs independently for presence detection, vital signs, and fall alerts.

Live ESP32 pipeline: Connect an ESP32-S3 node → run the sensing server → open the pose fusion demo for real-time dual-modal pose estimation (webcam + WiFi CSI). See ADR-059. (The webcam supplies ground-truth pose in this dual-modal demo; the CSI-only on-device 17-keypoint model is still first-cut — see Model weights: what's real, what's not.)

three.js scene gallery at /three.js/ — five progressively richer ADR-097 demos: helpers, cinematic, GLTF skinned, FBX skinned, and a live MediaPipe→Mixamo retargeting feed driven by ESP32 CSI. Demos 04 and 05 require a local Mixamo X Bot.fbx (license boundary — not redistributed).

🤗 Pretrained model on Hugging Face

Pretrained CSI weights live at ruvnet/wifi-densepose-pretrained — 12.2M training steps on 60K frames / 610K contrastive triplets, 82.3% held-out temporal-triplet accuracy (up from 66.4% raw; the older "100% presence" figure was measured on a single-class recording and has been retracted), 4-bit quantized variant fits in 8 KB. The release includes a contrastive CSI encoder producing 128-dim embeddings (164,183 emb/s on M4 Pro) and a presence-detection head. Per-node LoRA adapters are included for environment-specific fine-tuning.

bash

# Download the model bundle
pip install huggingface_hub
huggingface-cli download ruvnet/wifi-densepose-pretrained --local-dir models/wifi-densepose-pretrained

What works today vs. what's pending wiring:

Consumer	Format used	Status
Python training / evaluation / embedding extraction	`model.safetensors`	⚠️ The published file's header is NUL-padded, which the reference `safetensors.torch.load_file` rejects (issue #1522) — pending a corrected re-upload. `csi-embed-v2.safetensors` in the same repo is unaffected and loads normally.
Inspect / re-export the bundle	`model.rvf.jsonl` (line-by-line JSON)	✅ Works — plain JSONL
Sensing-server `--model <PATH>` flag	native RVF, `model.safetensors`, or `model.rvf.jsonl`	✅ Native RVF loads directly; safetensors and JSONL auto-convert in memory

Loader scope: --model now accepts native RVF and auto-converts the published safetensors or JSONL files. The quantized model-q*.bin files still need a compatible reader, and loading weights does not supply the matching pose-decoder architecture or establish end-to-end pose accuracy.

Quantization choices (all in the HF repo): model-q2.bin (4 KB) · model-q4.bin ⭐ recommended (8 KB) · model-q8.bin (16 KB) · model.safetensors full (48 KB)

The separate 17-keypoint pose-estimation model is now published at ruvnet/wifi-densepose-mmfi-pose — 82.69% torso-PCK@20 on MM-Fi (single model) / 83.59% (3-model ensemble + TTA), beating the prior published SOTA MultiFormer (72.25%) and CSI2Pose (68.41%) on the matched random_split protocol. See Results & proof below.

Results & proof

See the measured benchmarks, witness records, and one-command reproducibility check.

View benchmark and proof details

What	Where	Numbers
MM-Fi pose model (SOTA)	`ruvnet/wifi-densepose-mmfi-pose`	82.69% torso-PCK@20 (single) · 83.59% (ensemble+TTA) · 75K-param micro variant 74.30%
AetherArena benchmark Space	`ruvnet/aether-arena`	self-correcting, auditable MM-Fi leaderboard
Full MM-Fi study (honest picture)	`docs/benchmarks/mmfi-wifi-sensing-study.md`	pose + action; zero-shot cross-subject ~64%, labeled in-room calibration → 72.2%
Efficiency frontier	`docs/benchmarks/wifi-pose-efficiency-frontier.md`	SOTA-beating MM-Fi pose in a ~37 KB int4 model; live ESP32 compatibility not established
Pretrained encoder	`ruvnet/wifi-densepose-pretrained`	82.3% held-out temporal-triplet, 8 KB int4
Reproducible proof (Trust Kill Switch)	`archive/v1/data/proof/verify.py` +

Model weights: what's real, what's not

See which checkpoints are validated, experimental, or architecture-only.

View model maturity details

"WiFi → pose" means three different things in this repo, at three different maturity levels. Read the label, not the headline (ADR-187):

Tier	Checkpoint(s)	Honest status
Real & validated	`ruvnet/wifi-densepose-pretrained` (CSI encoder + presence head) · `ruvnet/wifi-densepose-mmfi-pose` (17-keypoint pose) · `cog-person-count/count_v1`	MEASURED / published. Presence = 82.3% held-out temporal-triplet accuracy (the old "100% presence" figure was retracted); MM-Fi pose = 82.69% torso-PCK@20 on the `random_split` protocol. These are the pose/presence numbers the project stands behind today.
Real but weak (honestly labeled)	committed `v2/crates/cog-pose-estimation/cog/artifacts/pose_v1.safetensors`	First-cut on-device model. PCK@20 = 3.0% / PCK@50 = 18.5% on a 217-sample holdout — below the ADR-079 target of ≥ 35%. Learns coarse structure (`r_hip` 77% PCK@50); distal/face joints near-random. Its runtime path in `cog-pose-estimation/src/inference.rs` is still a centred-skeleton stub returning `confidence=0` — the weights are not yet wired in. Full disclosure in the cog README.
Architecture only, no weights	`archive/v1` `DensePoseHead`	Random `kaiming_normal_` init, no checkpoint of any kind (zero `.pth`/`.onnx`/`.safetensors` files under `archive/v1/`). Deprecated and superseded — see . Do not expect real pose output from it.

🧩 Edge Module Catalog

Add signed modules for health, security, buildings, industry, research, AI, and more.

Browse the full edge module catalog

Browse and install modules at seed.cognitum.one/store or on your appliance at http://<appliance>:9000/cogs. Each module is a small signed binary that runs beside the sensing stack. The appliance updates the catalog over the air and verifies every module before installation (ADR-100, ADR-102).

🫀 Health — _{14 modules}

ID	What it does	Size	Difficulty
`air-quality-index`	Track indoor air quality with CO2 and particle sensors	8 KB	Easy
`baby-cry`	Sustained mid-band energy detector for nursery / infant monitoring. Audio-only, no camera.	451 KB	Easy
`breathing-sync`	Detects when two people breathe in sync	10 KB	Hard
`cardiac-arrhythmia`	Spots irregular heartbeats and abnormal heart rhythms	8 KB	Hard
`cough-detect`	Acoustic transient + spectral cough detector with 30s cluster aggregation. Early-warning signal for respiratory illness.	451 KB	Easy
`dream-stage`	Tracks your sleep stages — light, deep, and dreaming	14 KB	Hard
`fall-detect`	Two-stage impact + stillness fall detector over ambient feature stream (ESP32 motion / mic). Optional ruview-mode for CSI-based pose reinforcement.

🔬 How It Works

WiFi routers flood every room with radio waves. When a person moves — or even breathes — those waves scatter differently. WiFi DensePose reads that scattering pattern and reconstructs what happened:

Code

WiFi Router → radio waves pass through room → hit human body → scatter
    ↓
ESP32 mesh (4-6 nodes) captures CSI on channels 1/6/11 via TDM protocol
    ↓
Multi-Band Fusion: 3 channels × 56 subcarriers = 168 virtual subcarriers per link
    ↓
Multistatic Fusion: N×(N-1) links → attention-weighted cross-viewpoint embedding
    ↓
Coherence Gate: accept/reject measurements → stable for days without tuning
    ↓
Signal Processing: Hampel, SpotFi, Fresnel, BVP, spectrogram → clean features
    ↓
AI Backbone (RuVector): attention, graph algorithms, compression, field model
    ↓
Signal-Line Protocol (CRV): 6-stage gestalt → sensory → topology → coherence → search → model
    ↓
Neural Network: processed signals → 17 body keypoints + vital signs + room model
    ↓
Output: real-time pose, breathing, heart rate, room fingerprint, drift alerts

The Self-Learning system (ADR-024) provides camera-free representation-learning components. Cross-room pose remains a separate, data-gated problem: MERIDIAN (ADR-027) is Proposed, while the measured calibration reference requires labeled CSI/keypoint pairs and model-specific adapters. See the model compatibility boundary.

🏢 Use Cases & Applications

Safety boundary: these are research and prototype applications, not medical devices, emergency systems, or safety-certified controls. Vital-sign and pose outputs require independent validation on the exact hardware, room, subjects, and failure conditions before operational use.

WiFi sensing works anywhere WiFi exists. No new hardware in most cases — just software on existing access points or a $8 ESP32 add-on. Because there are no cameras, deployments avoid privacy regulations (GDPR video, HIPAA imaging) by design.

Scaling: Each AP distinguishes ~3-5 people (56 subcarriers). Multi-AP multiplies linearly — a 4-AP retail mesh covers ~15-20 occupants. No hard software limit; the practical ceiling is signal physics.

	Why WiFi sensing wins	Traditional alternative
🔒	No video, no GDPR/HIPAA imaging rules	Cameras require consent, signage, data retention policies
🧱	Works through walls, shelving, debris	Cameras need line-of-sight per room
🌙	Works in total darkness	Cameras need IR or visible light
💰	$0-$8 per zone (existing WiFi or ESP32)	Camera systems: $200-$2,000 per zone
🔌	WiFi already deployed everywhere	PIR/radar sensors require new wiring per room

🏥 Everyday — Healthcare, retail, office, hospitality (commodity WiFi)

Use Case	What It Does	Hardware	Key Metric	Edge Module
Elderly care / assisted living	Fall detection, nighttime activity monitoring, breathing rate during sleep — no wearable compliance needed	1 ESP32-S3 per room ($8)	Fall alert <2s	Sleep Apnea, Gait Analysis
Hospital patient monitoring	Continuous breathing + heart rate for non-critical beds without wired sensors; nurse alert on anomaly	1-2 APs per ward	Breathing: 6-30 BPM	Respiratory Distress, Cardiac Arrhythmia
Emergency room triage	Automated occupancy count + wait-time estimation; detect patient distress (abnormal breathing) in waiting areas	Existing hospital WiFi	Occupancy accuracy >95%	Queue Length, Panic Motion
Retail occupancy & flow	Real-time foot traffic, dwell time by zone, queue length — no cameras, no opt-in, GDPR-friendly	Existing store WiFi + 1 ESP32	Dwell resolution ~1m	Customer Flow,

🏟️ Specialized — Events, fitness, education, civic (CSI-capable hardware)

Use Case	What It Does	Hardware	Key Metric	Edge Module
Smart home automation	Room-level presence triggers (lights, HVAC, music) that work through walls — no dead zones, no motion-sensor timeouts	2-3 ESP32-S3 nodes ($24)	Through-wall range ~5m	HVAC Presence, Lighting Zones
Fitness & sports research	Explore motion and breathing cadence without a wearable or camera; reliable posture correction requires a validated compatible pose model	3+ ESP32-S3 mesh + edge host	Prototype; no live S3 pose accuracy claim	Breathing Sync, Gait Analysis
Childcare & schools	Naptime breathing monitoring, playground headcount, restricted-area alerts — privacy-safe for minors	2-4 ESP32-S3 per zone	Breathing: ±1 BPM	Sleep Apnea, Perimeter Breach
Event venues & concerts	Crowd density mapping, crush-risk detection via breathing compression, emergency evacuation flow tracking	Multi-AP mesh (4-8 APs)	Density per m²	Customer Flow,

🤖 Robotics & Industrial — Autonomous systems, manufacturing, android spatial awareness

WiFi sensing gives robots and autonomous systems a spatial awareness layer that works where LIDAR and cameras fail — through dust, smoke, fog, and around corners. The CSI signal field acts as a "sixth sense" for detecting humans in the environment without requiring line-of-sight.

Use Case	What It Does	Hardware	Key Metric	Edge Module
Cobot safety zones	Detect human presence near collaborative robots — auto-slow or stop before contact, even behind obstructions	2-3 ESP32-S3 per cell	Presence latency <100ms	Forklift Proximity, Perimeter Breach
Warehouse AMR navigation	Autonomous mobile robots sense humans around blind corners, through shelving racks — no LIDAR occlusion	ESP32 mesh along aisles	Through-shelf detection	Forklift Proximity, Loitering
Android / humanoid spatial awareness	Ambient human pose sensing for social robots — detect gestures, approach direction, and personal space without cameras always on	Onboard ESP32-S3 module	17-keypoint pose	Gesture Language, Emotion Detection

🔥 Extreme — Through-wall, disaster, defense, underground

These scenarios exploit WiFi's ability to penetrate solid materials — concrete, rubble, earth — where no optical or infrared sensor can reach. The WiFi-Mat disaster module (ADR-001) is specifically designed for this tier.

Use Case	What It Does	Hardware	Key Metric	Edge Module
Search & rescue (WiFi-Mat)	Detect survivors through rubble/debris via breathing signature, START triage color classification, 3D localization	Portable ESP32 mesh + laptop	Through 30cm concrete	Respiratory Distress, Seizure Detection
Firefighting	Locate occupants through smoke and walls before entry; breathing detection confirms life signs remotely	Portable mesh on truck	Works in zero visibility	Sleep Apnea, Panic Motion
Prison & secure facilities	Cell occupancy verification, distress detection (abnormal vitals), perimeter sensing — no camera blind spots	Dedicated AP infrastructure	24/7 vital signs	Cardiac Arrhythmia, Loitering
Military / tactical	Through-wall personnel detection, room clearing confirmation, hostage vital signs at standoff distance

🧠 Self-Learning WiFi AI

Learn compact room fingerprints from raw CSI and adapt the model to each environment.

View self-learning architecture and commands

Every WiFi signal that passes through a room creates a unique fingerprint of that space. WiFi-DensePose already reads these fingerprints to track people, but until now it threw away the internal "understanding" after each reading. The Self-Learning WiFi AI captures and preserves that understanding as compact, reusable vectors — and continuously optimizes itself for each new environment.

What it does in plain terms:

Turns any WiFi signal into a 128-number "fingerprint" that uniquely describes what's happening in a room
Learns entirely on its own from raw WiFi data — no cameras, no labeling, no human supervision needed
Recognizes rooms, detects intruders, and classifies activities using only WiFi (named person-identity is an experimental, data-gated research capability — see below, not a shipped feature)
Runs on an $8 ESP32 chip (the entire model fits in 55 KB of memory)
Produces both body pose tracking AND environment fingerprints in a single computation

Key Capabilities

What	How it works	Why it matters
Self-supervised learning	The model watches WiFi signals and teaches itself what "similar" and "different" look like, without any human-labeled data	Deploy anywhere — just plug in a WiFi sensor and wait 10 minutes
Room identification	Each room produces a distinct WiFi fingerprint pattern	Know which room someone is in without GPS or beacons
Anomaly detection	An unexpected person or event creates a fingerprint that doesn't match anything seen before	Automatic intrusion and fall detection as a free byproduct
Person re-identification (experimental, research)	A real per-channel similarity matcher (Soul Signature §3.6, `wifi-densepose-bfld`); measured result: on WiFi-only cardiac+respiratory channels alone two people are not separable (gap ~0.0005)	Honest research capability — named identity is not claimed and is data-gated on enrollment with the decisive AETHER/body-resonance channel. See #1021

🧩 Claude Code & Codex Plugin

Use the in-repo plugin for guided setup, sensing, training, and verification in Claude Code or Codex.

View plugin installation and commands

RuView's Claude Code plugin and Codex prompt mirror cover onboarding, ESP32 setup, sensing apps, model training, advanced sensing, CLI/API/WASM, mmWave radar, and witness verification. The source lives in plugins/ruview/; the marketplace manifest is .claude-plugin/marketplace.json.

bash

# In Claude Code — add this repo as a plugin marketplace, then install:
/plugin marketplace add ruvnet/RuView
/plugin install ruview@ruview

# Or try it for one session without installing (from a local clone of the repo):
claude --plugin-dir ./plugins/ruview

# Then, in Claude Code:
#   /ruview-start      → onboarding (Docker demo / repo build / live ESP32)
#   /ruview-flash      → build + flash ESP32 firmware
#   /ruview-provision  → provision WiFi creds, sink IP, channel/MAC, mesh slots
#   /ruview-app        → run a sensing application (presence / vitals / pose / sleep / MAT / point cloud)
#   /ruview-train      → train / evaluate / publish a model (incl. GPU on GCloud)
#   /ruview-advanced   → multistatic / tomography / cross-viewpoint / mesh-security
#   /ruview-verify     → tests + deterministic proof + witness bundle

Codex (OpenAI CLI): cp plugins/ruview/codex/prompts/*.md ~/.codex/prompts/ — the seven /ruview-* commands are mirrored as Codex prompts; plugins/ruview/codex/AGENTS.md carries the project rules. See plugins/ruview/codex/README.md.

Verify the plugin structure: bash plugins/ruview/scripts/smoke.sh. Full details: plugins/ruview/README.md.

Live sensing pane (Claude Code mod): the same marketplace also lists , a Claude Code mod.

📖 Documentation

Start with the user, build, and calibration guides; expand for the full reference map.

Browse all documentation

Document	Description
User Guide	Step-by-step guide: installation, first run, API usage, hardware setup, training
Build Guide	Building from source (Rust and Python)
Calibration & Room Training Guide	What `calibrate`/`enroll`/`train-room` actually enforce: minimum frame counts, per-anchor quality gates, the pet/small-motion presence-detection caveat, and empty-room baseline conditions — grounded in the real code, not just ADR-135/151
Trust State & Engine Errors	What `engine_error_count` and `demoted` mean on `/api/v1/status`, exact trigger conditions, the current diagnostic gap (no per-cause breakdown), the `WDP_GUARD_INTERVAL_US` recovery path, and why a converted Hugging Face model isn't shown to be the cause in code
Home Assistant + Matter Integration	Works with Home Assistant via MQTT auto-discovery + Works with Matter (Apple Home / Google Home / Alexa / SmartThings) — full entity catalog, 3 starter blueprints, Lovelace dashboards, privacy mode, threshold tuning (ADR-115).

🚧 Beta software

Beta Software — Under active development. APIs and firmware may change. Known limitations:

ESP32-C3 and original ESP32 are not supported (single-core, insufficient for CSI DSP)

Single ESP32 deployments have limited spatial resolution — use 2+ nodes or add a Cognitum Seed for best results

Camera-free pose accuracy is limited (PCK@20 ≈ 2.5% with proxy labels) — camera ground-truth training targets 35%+ PCK@20; the pipeline is implemented, but the data-collection and evaluation phases (ADR-079 P7–P9) are still pending.

Contributions and bug reports welcome at Issues.

📄 License

MIT License — see LICENSE for details.

🤝 Creator Affiliate Program

For TikTok · Instagram · YouTube creators — earn 25% on every Cognitum sale you refer. The RuFlo, RuView, and RuVector videos you're already making have done millions of views; get paid for the orders they drive. Click-tracking activates instantly; commissions activate after a quick manual review (usually under 24 hours).

Apply now → cognitum.one/affiliate

📞 Support

GitHub Issues | Discussions | PyPI

WiFi DensePose — Privacy-preserving human pose estimation through WiFi signals.

ID	What it does	Size	Difficulty
`audit-logger`	Record every action for compliance — tamper-proof log	8 KB	Easy
`behavioral-profiler`	Learns normal behavior and flags anything unusual	12 KB	Hard
`fleet-auth`	Manage device certificates and access across all seeds	12 KB	Med
`glass-break`	Two-phase bang + shatter acoustic detector. Distinguishes glass break from ordinary impulse noise.	451 KB	Easy
`gunshot-detect`	Saturating peak + exponential decay acoustic detector with optional ruview CSI motion-drop reinforcement.	451 KB	Easy
`intrusion`	Alerts when an unauthorized person enters a room	6 KB	Med
`intrusion-detect-ml`	Detect network attacks using machine learning	14 KB	Hard
`loitering`	Alerts when someone lingers too long in one spot	3 KB	Easy
`network-firewall`	Block unauthorized network access per cog	6 KB	Easy
`panic-motion`	Detects sudden panicked or erratic movement	6 KB	Med
`perimeter-breach`	Guards multiple zones and shows entry direction	10 KB	Med
`prompt-shield`	Blocks signal replay and injection attacks on the seed	10 KB	Med
`tailgating`	Catches when someone sneaks in behind a badge holder	6 KB	Med
`weapon-detect`	Detects concealed metal objects on a person	8 KB	Hard

ID	What it does	Size	Difficulty
`emotion-detect`	Reads stress and calm from body language and breathing	10 KB	Hard
`energy-harvester`	Optimize solar and battery for off-grid seed deployment	6 KB	Med
`gesture-language`	Recognizes sign language gestures in real time	12 KB	Hard
`ghost-hunter`	Finds unexplained environmental anomalies — for fun	10 KB	Hard
`happiness-score`	Estimates well-being from movement and mood signals	8 KB	Med
`hyperbolic-space`	Maps data into curved space for tree-like structures	12 KB	Hard
`music-conductor`	Reads a conductor's gestures for tempo and dynamics	12 KB	Hard
`plant-growth`	Tracks plant growth rate and day/night cycles	8 KB	Med
`rain-detect`	Detects when rain starts, stops, and how heavy it is	6 KB	Med
`ruview-densepose`	Full body pose tracking from WiFi — no cameras needed	50 KB	Hard
`sound-classifier`	Identify sounds like glass break, alarm, or baby cry	16 KB	Hard
`time-crystal`	Experiments with repeating time-pattern symmetry	12 KB	Hard

ID	What it does	Size	Difficulty
`anomaly-attractor`	Learns what's normal and catches anything weird	10 KB	Hard
`cognitive-pipeline`	FastGRNN anomaly gate + SmolLM2 sparse-LLM inference for on-device Pi Zero 2W cognitive events	320 KB	Hard
`dtw-gesture-learn`	Teach custom hand gestures by showing examples	14 KB	Med
`ewc-lifelong`	Learns new things without forgetting old lessons	8 KB	Hard
`federated-learning`	Train AI across seeds without sharing raw data	18 KB	Hard
`goap-autonomy`	Plans and executes goals on its own	14 KB	Hard
`meta-adapt`	Automatically tunes itself for best performance	10 KB	Hard
`micro-hnsw`	Fast on-device fingerprinting and classification	12 KB	Med
`neural-trader`	Spot market patterns and trends from live data	20 KB	Hard
`pagerank-influence`	Finds the most influential person in a group	12 KB	Med
`pattern-sequence`	Detects daily routines and repeated habits	10 KB	Med
`rag-local`	Search your documents using AI — runs on the seed	14 KB	Med
`spiking-tracker`	Brain-inspired tracker that runs on tiny hardware	16 KB	Hard
`temporal-logic`	Enforces safety rules on live event streams	12 KB	Hard
`time-series-forecast`	Predict sensor trends using historical patterns	12 KB	Med

ID	What it does	Size	Difficulty
`coherence-gate`	Filters out noisy signals and keeps clean ones	8 KB	Med
`flash-attention`	Focuses sensing on specific areas for better accuracy	12 KB	Med
`optimal-transport`	Measures motion using shape-aware signal comparison	12 KB	Hard
`person-matching`	Tells apart multiple people in the same room	18 KB	Hard
`sparse-recovery`	Recovers missing signal data from partial readings	16 KB	Hard
`temporal-compress`	Shrinks old data to save memory without losing meaning	14 KB	Med

Mode	What you need	What you get
Self-Supervised	Just raw WiFi data	A model that understands WiFi signal structure
Supervised	WiFi data + body pose labels	Full pose tracking + environment fingerprints
Cross-Modal	WiFi data + camera footage	Fingerprints aligned with visual understanding

π RuView

See through walls with WiFi

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

Built for low-power edge applications

🤗 Pretrained model on Hugging Face

Results & proof

Model weights: what's real, what's not

🧩 Edge Module Catalog

🫀 Health — _{14 modules}

🔬 How It Works

🏢 Use Cases & Applications

🧠 Self-Learning WiFi AI

🧩 Claude Code & Codex Plugin

📖 Documentation

🚧 Beta software

📄 License

🤝 Creator Affiliate Program

📞 Support

🔒 Security — _{14 modules}

🏢 Building — _{11 modules}

🛍️ Retail — _{7 modules}

🏭 Industrial — _{7 modules}

🔬 Research — _{12 modules}

🤖 Ai — _{15 modules}

🐝 Swarm — _{11 modules}

📡 Signal — _{6 modules}

🌐 Network — _{1 modules}

🛠️ Developer — _{7 modules}

ID	What it does	Size	Difficulty
`beehive-monitor`	Acoustic hive state classifier. Detects healthy / chaotic / queenless / swarming / robbing via hum-band energy + chaos + piping autocorr.	451 KB	Easy
`elevator-count`	Counts how many people are in an elevator	8 KB	Med
`energy-audit`	Learns your schedule and cuts wasted energy	6 KB	Med
`frost-warning`	Predicts frost 6 hours ahead via temperature trend + dewpoint-depression gate. Field/orchard agriculture.	451 KB	Easy
`hvac-presence`	Turns heating and cooling on when you arrive	3 KB	Easy
`lighting-zones`	Turns lights on and off as people move between rooms	4 KB	Easy
`meeting-room`	Shows if a meeting room is free or occupied	5 KB	Easy
`occupancy-zones`	Counts people in each room through walls	8 KB	Med
`predictive-maintenance`	Vibration harmonic analyzer for rotating equipment. Tracks F1 / 2×F1 / high-order / sideband energy to score degradation severity.	451 KB	Easy
`smoke-fire`	Multi-signal smoke and fire detector. Fuses acoustic crackle, thermal drift proxy, and optional ruview CSI plume signature. Not a UL-listed replacement for code-required smoke alarms.	451 KB	Easy
`water-leak`	Persistent low-amplitude hiss + periodic drip acoustic detector with multi-minute persistence gate. Two-stage likely → confirmed.	451 KB	Easy

ID	What it does	Size	Difficulty
`customer-flow`	Counts foot traffic in and out of each entrance	8 KB	Med
`dwell-heatmap`	Shows where customers spend the most time	6 KB	Med
`package-detect`	Sustained CSI-shift detector for porch / loading bay package arrivals and departures. Requires ESP32 CSI ruview input.	451 KB	Easy
`parking-occupancy`	Per-zone parking occupancy via ESP32 CSI subcarrier-amplitude shift. Tracks utilization and churn-per-hour. Requires ruview.	451 KB	Easy
`queue-length`	Estimates line length and wait time	6 KB	Med
`shelf-engagement`	Detects when customers interact with products	6 KB	Med
`table-turnover`	Tracks which restaurant tables are free or occupied	4 KB	Easy

ID	What it does	Size	Difficulty
`clean-room`	Enforces max headcount in controlled environments	4 KB	Easy
`confined-space`	Monitors workers in tight spaces for safety	5 KB	Med
`forklift-proximity`	Warns if a forklift gets too close to workers	10 KB	Hard
`livestock-monitor`	Monitors animals for distress, escape, or illness	6 KB	Med
`ppe-compliance`	Cog-composition layer: alerts when ruview-densepose detects presence in a restricted zone without an accompanying PPE-camera-cog confirmation vector.	387 KB	Easy
`slip-fall-zone`	Pre-fall risk detector. Fires when motion-variance drop, splash audio, and optional cautious-gait CSI all signal elevated slip risk.	451 KB	Easy
`structural-vibration`	Detects dangerous vibrations in buildings or machines	8 KB	Hard

ID	What it does	Size	Difficulty
`swarm-backup-restore`	Auto-backup data to other seeds — one-click restore	8 KB	Easy
`swarm-cluster-monitor`	Live dashboard of every seed's health and status	6 KB	Easy
`swarm-consensus`	Seeds vote before making critical changes together	16 KB	Hard
`swarm-delta-sync`	Auto-sync data between seeds — only sends changes	8 KB	Med
`swarm-deploy`	Install or remove cogs on all seeds at once	10 KB	Med
`swarm-distributed-store`	Spread data across seeds and search them all at once	14 KB	Hard
`swarm-edge-orchestrator`	Manage all ESP32 sensor nodes from one place	14 KB	Hard
`swarm-load-balancer`	Spread queries across seeds so no single one overloads	10 KB	Med
`swarm-mesh-manager`	Find, connect, and monitor all seeds on your network	12 KB	Easy
`swarm-mqtt-bridge`	Share events between seeds over MQTT messaging	6 KB	Easy
`swarm-witness-federation`	Share tamper-proof audit trails across seeds	12 KB	Hard

ID	What it does	Size	Difficulty
`adversarial`	Detects tampered or spoofed sensor signals	4 KB	Easy
`coherence`	Monitors signal quality across multiple channels	4 KB	Easy
`gesture`	Core gesture recognition building block for cogs	6 KB	Med
`interference-search`	Searches many possibilities at once for fast answers	14 KB	Hard
`psycho-symbolic`	Reasons over knowledge graphs with multiple styles	16 KB	Hard
`quantum-coherence`	Quantum-inspired model for advanced signal states	16 KB	Hard
`self-healing-mesh`	Keeps sensor mesh running even when nodes drop out	14 KB	Hard

Index	What it stores	Real-world use
`env_fingerprint`	Average room fingerprint	"Is this the kitchen or the bedroom?"
`activity_pattern`	Activity boundaries	"Is someone cooking, sleeping, or exercising?"
`temporal_baseline`	Normal conditions	"Something unusual just happened in this room"
`person_track`	Individual movement signatures	"Person A just entered the living room"

Component	Parameters	Memory (on ESP32)
Transformer backbone	~28,000	28 KB
Embedding projection head	~25,000	25 KB
Per-room MicroLoRA adapter	~1,800	2 KB
Total	~55,000	55 KB (of 520 KB available)

π RuView

See through walls with WiFi

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

Built for low-power edge applications

🤗 Pretrained model on Hugging Face

Results & proof

Model weights: what's real, what's not

🧩 Edge Module Catalog

🫀 Health — 14 modules

🔬 How It Works

🏢 Use Cases & Applications

🧠 Self-Learning WiFi AI

🧩 Claude Code & Codex Plugin

📖 Documentation

🚧 Beta software

📄 License

🤝 Creator Affiliate Program

📞 Support

🔒 Security — 14 modules

🏢 Building — 11 modules

🛍️ Retail — 7 modules

🏭 Industrial — 7 modules

🔬 Research — 12 modules

🤖 Ai — 15 modules

🐝 Swarm — 11 modules

📡 Signal — 6 modules

🌐 Network — 1 modules

🛠️ Developer — 7 modules

🫀 Health — _{14 modules}

🔒 Security — _{14 modules}

🏢 Building — _{11 modules}

🛍️ Retail — _{7 modules}

🏭 Industrial — _{7 modules}

🔬 Research — _{12 modules}

🤖 Ai — _{15 modules}

🐝 Swarm — _{11 modules}

📡 Signal — _{6 modules}

🌐 Network — _{1 modules}

🛠️ Developer — _{7 modules}