π RuView turns commodity WiFi signals into real-time spatial intelligence, vital sign monitoring, and presence detection — all without a single pixel of video. https://Cognitum.One/RuView 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
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).
# 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 # 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.
Device-reported vitals are labelled as unvalidated.
Retrieved brain content is evidence, not authority.
A single npx ruview package bundling all of this is ready (ADR-376). It is waiting on npm to release the unscoped name.
Full walkthrough: user guide → RuView npm toolkit .
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:
ruvnet/wifi-densepose-mmfi-pose hits 82.69% torso-PCK@20 (ensemble 83.59%), beating MultiFormer (72.25%) and CSI2Pose (68.41%) on the matched MM-Fi random_split protocol — self-corrected and auditable on AetherArena
8.4 ms cold-start on a Pi 5
🚶 Motion / activity
Motion-band power + phase acceleration
Real-time
🤸 Fall detection
Phase-acceleration threshold + 3-frame debounce + 5 s cooldown ( #263 )
< 200 ms
🧮 Multi-person count
Adaptive P95 normalisation + runtime-tunable dedup factor ( /api/v1/config/dedup-factor , #491 ). Six specialised learned counters available as Cogs: occupancy-zones , elevator-count , queue-length , customer-flow , clean-room , person-matching
Real-time, self-calibrating
🌍 World model prediction
OccWorld TransVQVAE — 15-frame future occupancy prediction, 209 ms inference, 3.4 GB VRAM on RTX 5080; fine-tune on your space with occworld_retrain.py ( ADR-147 )
15 frames × 200×200×16 vox
🧱 Through-wall sensing
Fresnel-zone geometry + multipath modeling
Up to ~5 m, signal-dependent
🧠 Edge intelligence
105-cog catalog ( ADR-102 ) live from app-registry.json — health, security, building, retail, industrial, research, AI, swarm, signal, network, and developer modules. Optional Cognitum Seed adds persistent vector store + kNN + witness chain
$140 total BOM
🎯 Camera-free pre-training
Self-supervised contrastive encoder, 12.2M training steps on 60K frames, shipped on Hugging Face
84 s/epoch retrain on M4 Pro
📷 Camera-supervised fine-tune
MediaPipe + ESP32 CSI paired training, end-to-end Candle pipeline on RTX 5080 ( ADR-079 )
2.1 s for 400 epochs (~5 ms/epoch)
📡 Multi-frequency mesh
Channel hopping across 6 bands, TDM slot scheduling ( ADR-029 )
3× sensing bandwidth
🌐 3D point cloud fusion
Camera depth (MiDaS) + WiFi CSI + mmWave radar → unified spatial model
22 ms pipeline · 19K+ points/frame
Browse the full 105-module catalog (with practical descriptions, sizes, and difficulty) below in 🧩 Edge Module Catalog , or visit seed.cognitum.one/store .
🤗 Pretrained weights : download from ruvnet/wifi-densepose-pretrained — see Loading the pretrained model below for one-command setup.
Quick start options — Docker, ESP32-S3/C6, Cognitum Seed, and Python
# Option 1: Docker (simulated data, no hardware needed) docker pull ruvnet/wifi-densepose:latest export RUVIEW_API_TOKEN=$(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 )
QCA9300 now; QCN9074/QCN9274 experimental
~$30-200
Simulator now; hardware adapter gated
Rust QCS1 codec, deterministic replay, UDP/API integration; modern ath11k/ath12k profiles do not claim public CSI export
Vendor provider beta ( ADR-270 )
Origin, Plume, Mist, NETGEAR, Electric Imp, RF Solutions, Luma, Nest, Linksys, Wifigarden
Varies
Capability-dependent
Bounded Rust adapters and deterministic fixtures; telemetry/network-only/unsupported states cannot masquerade as CSI
Any WiFi
Windows, macOS, or Linux laptop
$0
No
RSSI-only: coarse presence and motion (see tutorial #36 )
No hardware? Verify the signal processing pipeline with the deterministic reference signal: python archive/v1/data/proof/verify.py
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.
# 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 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 + expected_features.sha256
one-command deterministic pipeline replay (SHA-256 of output vs published hash)
Benchmark-proof ADR
ADR-168
how the numbers are produced and verified
Witness attestation
docs/WITNESS-LOG-028.md
33-row capability attestation matrix with per-claim evidence
# Reproduce the deterministic pipeline proof yourself (must print VERDICT: PASS): python archive/v1/data/proof/verify.py
Tracked in #509 ; see ADR-079 phases P7–P9 for the camera-supervised fine-tune path.
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 archive/v1/DEPRECATED.md . Do not expect real pose output from it.
On the ESP32-SISO question ( #509 ): a single-antenna, 56-subcarrier CSI stream at a 20-frame window does not carry the fine-grained spatial information the multi-antenna NIC research relies on — the cog measurements above show distal/face joints near-random. The shippable pose accuracy the project can stand behind today is the MM-Fi benchmark number , not a live single-ESP32 number.
The path to a first reproducible on-device baseline (PCK@20 ≥ 35%) is tracked in ADR-079 / #645 — do not advertise the live single-ESP32 17-keypoint feature without the "first-cut, below-target, runtime-stub" caveat until that baseline is measured.
🧩 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:// :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.
402 KB
Easy
gait-analysis
Detects walking problems and scores fall risk
12 KB
Hard
health-monitor
Contactless heart rate, breathing, sleep, and fall alerts
30 KB
Med
respiratory-distress
Alerts when breathing becomes labored or dangerously fast
10 KB
Hard
seizure-detect
Recognizes seizures and sends immediate alerts
10 KB
Hard
sleep-apnea
Detects when someone stops breathing during sleep
4 KB
Easy
snore-monitor
Periodic low-band energy tracker for sleep-quality / apnea-risk trending. Companion to sleep-apnea cog.
451 KB
Easy
vital-trend
Tracks breathing and heart rate trends over weeks
6 KB
Med
🔒 Security — 14 modules
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
🏢 Building — 11 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
🛍️ Retail — 7 modules
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
🏭 Industrial — 7 modules
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
🔬 Research — 12 modules
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
🤖 Ai — 15 modules
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
🐝 Swarm — 11 modules
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
📡 Signal — 6 modules
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
🌐 Network — 1 modules
ID
What it does
Size
Difficulty
tailscale
Reach the seed from anywhere via a private WireGuard mesh (Tailscale). Userspace mode — no root.
700 KB
Med
🛠️ Developer — 7 modules
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
ℹ️ Build your own cog: see ADR-100 for the packaging spec. The first cog this repo ships into the catalog lives in v2/crates/cog-pose-estimation/ (17-keypoint WiFi pose, ADR-101 ).
🔬 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:
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 , Dwell Heatmap
Office space utilization
Which desks/rooms are actually occupied, meeting room no-shows, HVAC optimization based on real presence
Existing enterprise WiFi
Presence latency <1s
Meeting Room , HVAC Presence
Hotel & hospitality
Room occupancy without door sensors, minibar/bathroom usage patterns, energy savings on empty rooms
Existing hotel WiFi
15-30% HVAC savings
Energy Audit , Lighting Zones
Restaurants & food service
Table turnover tracking, kitchen staff presence, restroom occupancy displays — no cameras in dining areas
Existing WiFi
Queue wait ±30s
Table Turnover , Queue Length
Parking garages
Pedestrian presence in stairwells and elevators where cameras have blind spots; security alert if someone lingers
Existing WiFi
Through-concrete walls
Loitering , Elevator Count
🏟️ 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 , Panic Motion
Stadiums & arenas
Section-level occupancy for dynamic pricing, concession staffing, emergency egress flow modeling
Enterprise AP grid
15-20 per AP mesh
Dwell Heatmap , Queue Length
Houses of worship
Attendance counting without facial recognition — privacy-sensitive congregations, multi-room campus tracking
Existing WiFi
Zone-level accuracy
Elevator Count , Energy Audit
Warehouse & logistics
Worker safety zones, forklift proximity alerts, occupancy in hazardous areas — works through shelving and pallets
Industrial AP mesh
Alert latency <500ms
Forklift Proximity , Confined Space
Civic infrastructure
Public restroom occupancy (no cameras possible), subway platform crowding, shelter headcount during emergencies
Municipal WiFi + ESP32
Real-time headcount
Customer Flow , Loitering
Museums & galleries
Visitor flow heatmaps, exhibit dwell time, crowd bottleneck alerts — no cameras near artwork (flash/theft risk)
Existing WiFi
Zone dwell ±5s
Dwell Heatmap , Shelf Engagement
🤖 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
Manufacturing line monitoring
Worker presence at each station, ergonomic posture alerts, headcount for shift compliance — works through equipment
Industrial AP per zone
Pose + breathing
Confined Space , Gait Analysis
Construction site safety
Exclusion zone enforcement around heavy machinery, fall detection from scaffolding, personnel headcount
Ruggedized ESP32 mesh
Alert <2s, through-dust
Panic Motion , Structural Vibration
Agricultural robotics
Detect farm workers near autonomous harvesters in dusty/foggy field conditions where cameras are unreliable
Weatherproof ESP32 nodes
Range ~10m open field
Forklift Proximity , Rain Detection
Drone landing zones
Verify landing area is clear of humans — WiFi sensing works in rain, dust, and low light where downward cameras fail
Ground ESP32 nodes
Presence: >95% accuracy
Perimeter Breach , Tailgating
Clean room monitoring
Personnel tracking without cameras (particle contamination risk from camera fans) — gown compliance via pose
Existing cleanroom WiFi
No particulate emission
Clean Room , Livestock Monitor
🔥 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
Directional WiFi + custom FW
Range: 5m through wall
Perimeter Breach , Weapon Detection
Border & perimeter security
Detect human presence in tunnels, behind fences, in vehicles — passive sensing, no active illumination to reveal position
Concealed ESP32 mesh
Passive / covert
Perimeter Breach , Tailgating
Mining & underground
Worker presence in tunnels where GPS/cameras fail, breathing detection after collapse, headcount at safety points
Ruggedized ESP32 mesh
Through rock/earth
Confined Space , Respiratory Distress
Maritime & naval
Below-deck personnel tracking through steel bulkheads (limited range, requires tuning), man-overboard detection
Ship WiFi + ESP32
Through 1-2 bulkheads
Structural Vibration , Panic Motion
Wildlife research
Non-invasive animal activity monitoring in enclosures or dens — no light pollution, no visual disturbance
Weatherproof ESP32 nodes
Zero light emission
Livestock Monitor , Dream Stage
🧠 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
Environment adaptation
MicroLoRA adapters (1,792 parameters per room) fine-tune the model for each new space
Adapts to a new room with minimal data — 93% less than retraining from scratch
Memory preservation
EWC++ regularization remembers what was learned during pretraining
Switching to a new task doesn't erase prior knowledge
Hard-negative mining
Training focuses on the most confusing examples to learn faster
Better accuracy with the same amount of training data
Architecture
WiFi Signal [56 channels] → Transformer + Graph Neural Network ├→ 128-dim environment fingerprint (for search + identification) └→ 17-joint body pose (for human tracking)
Quick Start
# Step 1: Learn from raw WiFi data (no labels needed) cargo run -p wifi-densepose-sensing-server -- --pretrain --dataset data/csi/ --pretrain-epochs 50
# Step 2: Fine-tune with pose labels for full capability cargo run -p wifi-densepose-sensing-server -- --train --dataset data/mmfi/ --epochs 100 --save-rvf model.rvf
# Step 3: Use the model — extract fingerprints from live WiFi cargo run -p wifi-densepose-sensing-server -- --model model.rvf --embed
# Step 4: Search — find similar environments or detect anomalies cargo run -p wifi-densepose-sensing-server -- --model model.rvf --build-index env
Training Modes
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
Fingerprint Index Types
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"
Model Size
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)
The self-learning system builds on the AI Backbone (RuVector) signal-processing layer — attention, graph algorithms, and compression — adding contrastive learning on top.
See docs/adr/ADR-024-contrastive-csi-embedding-model.md for full architectural details.
🧩 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 .
# 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 ruview-live , a Claude Code mod.
Install it with /plugin install ruview-live@ruview , or run npx @ruvnet/ruview@0.9.1 mod for its path inside the npm package.
/ruview opens a pane beside the transcript with your CSI nodes and radar, refreshed on a timer, plus a one-line status.
It is read-only.
Mods are early access; if /ruview is missing, start Claude Code with CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1 . See ADR-377 .
For the portable RuView MetaHarness, use npx @ruvnet/ruview@0.9.1 . The quick commands and a fuller explanation are in the collapsed MetaHarness section near the top of this README, in harness/ruview/ , and in the user guide .
📖 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 ).
BFLD — Beamforming Feedback Layer for Detection
New privacy-gated WiFi sensing layer that measures + structurally prevents identity leakage from 802.11ac/ax Beamforming Feedback Information. Three type-enforced invariants (raw BFI never exits node, identity embedding is in-RAM-only, cross-site correlation cryptographically impossible via per-site BLAKE3 keyed hash + daily rotation).
Ships full operator surface ( BfldPipeline , BfldPipelineHandle , the Soul Signature §3.6 per-channel matcher EnrolledMatcher / SoulMatchOracle — experimental; named identity is data-gated, measured as not-separable on WiFi-only channels alone), MQTT topic router + HA-DISCO + availability + LWT, 3 operator HA blueprints, two runnable examples, eclipse-mosquitto:2 CI service container. 327+ tests.
ADR-118 umbrella + sub-ADRs / / / / . Research dossier: docs/research/BFLD/ (11 files, 13,544 words).
SENSE-BRIDGE — rvagent MCP server
Dual-transport MCP server ( @ruvnet/rvagent ) bridging the RuView sensing stack to AI agents (Claude Code, Cursor, ruflo swarms). 6 tools wired: ruview.presence.now , ruview.vitals.get_{breathing,heart_rate,all} , ruview.bfld.last_scan , ruview.bfld.subscribe . stdio + Streamable HTTP ( POST /mcp , Origin-validated, bearer-token auth, 127.0.0.1 bind). Full 20-tool Zod schema barrel + 5 RUVIEW-POLICY governance tools. 93 tests. ADR-124 . Try: npx @ruvnet/rvagent stdio .
Semantic Primitives — Precision/Recall
Per-primitive F1 on the held-out paired-capture set: someone-sleeping, possible-distress, room-active, elderly-inactivity-anomaly, meeting, bathroom, fall-risk, bed-exit, no-movement, multi-room.
Claude Code / Codex Plugin
The ruview plugin + marketplace — skills, /ruview-* commands, agents, and the Codex prompt mirror
Portable harness — npx @ruvnet/ruview
MetaHarness-minted, host-portable RuView operator harness — ruview.* MCP tools + the MEASURED-vs-CLAIMED honesty guardrail enforced in code ( ADR-182 ). A lighter, multi-host companion to the in-repo plugin.
CSI Frame Selection — Measured
Which rate gate actually buys cross-node pairing, measured on a nine-node ESP32-C6 fleet as a 2x2 factorial in three Latin-square blocks. Selecting frames by 802.11 rx_seq roughly doubled pairing (+31.7 pp) while mesh-time alignment bought +0.5 pp, inside noise. Includes the trade it makes, a prediction that turned out wrong, the limits of the measurement, and run_arms.py so the comparison can be re-run rather than taken on trust.
ESP32-C6 Firmware Runbook
What to run, in what order, and what to check: the three-file build that keeps OTA rollback compiled in, a five-key pre-flash gate, which flash regions OTA can and cannot reach, a single-board diagnostic flash with a revert path, and the tunables whose right value depends on your house rather than on the chip.
Architecture Decisions
205 ADRs — why each technical choice was made, organized by domain (hardware, signal processing, ML, platform, infrastructure)
Domain Models
8 DDD models (RuvSense, Signal Processing, Training Pipeline, Hardware Platform, Sensing Server, WiFi-Mat, CHCI, rvCSI) — bounded contexts, aggregates, domain events, and ubiquitous language
rvCSI — edge RF sensing runtime
Rust-first / TypeScript-accessible / hardware-abstracted CSI runtime: multi-source ingestion (incl. real nexmon_csi .pcap from a Raspberry Pi 5 / Pi 4 / Pi 3B+ — CYW43455 / BCM43455c0) → validation → DSP → typed events → RuVector RF memory ( ADR-095 , ADR-096 , domain model ). Now its own repo — ruvnet/rvcsi — vendored here under vendor/rvcsi ; 9 rvcsi-* crates on crates.io , @ruv/rvcsi on npm, plus a Claude Code plugin.
Desktop App
WIP — Tauri v2 desktop app for node management, OTA updates, WASM deployment, and mesh visualization
ruview-swarm
Drone swarm control system (ADR-148) — hierarchical-mesh topology, Raft consensus, MARL, CSI sensing payload, MAVLink/PX4/ArduPilot compatibility, Ruflo AI-agent integration
ruview-unified
Unified RF spatial world model ( ADR-273 .. ) — canonical RF tensor + hardware adapters (WiFi CSI / FMCW radar / UWB / 5G SRS), universal RF foundation encoder with ≤1% task adapters, RF-aware Gaussian spatial memory with channel-gain queries + inverse updates, physics-guided synthetic RF worlds, and an 802.11bf/ETSI-ISAC-aligned sensing policy plane (raw RF structurally unexportable). All accuracy numbers SYNTHETIC until real-data validation.
Medical Examples
Contactless blood pressure, heart rate, breathing rate via 60 GHz mmWave radar — $15 hardware, no wearable
Extended Documentation
Latest additions, key features, installation, quick start, signal processing, training, CLI, testing, deployment, and changelog
🚧 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.