GitHub - ruvnet/RuView: π RuView turns commodity WiFi signals into real-time spatial intelligence, vital sign monitoring, and presence detec

π RuView

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

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, guarded Claude Code/Codex agents, deterministic verification, an honesty check for accuracy claims, and an explicitly granted OAuth-only Cognitum Spaces read.

Check the local setup and get source-cited guidance

npx @ruvnet/[email protected] doctor

npx @ruvnet/[email protected] guidance --topic sensing --query " model loading "

Run a read-only RuView agent through Codex

npx @ruvnet/[email protected] agent run --host codex --repo . \

--prompt " Find the nearest tests and cite the source files "

Search or verify the reviewed contributor brain

npx @ruvnet/[email protected] brain search --query " calibration "

npx @ruvnet/[email protected] brain verify --repo .

Check claims, replay the deterministic proof, or expose the MCP server

npx @ruvnet/[email protected] claim-check --file REPORT.md

npx @ruvnet/[email protected] verify

npx @ruvnet/[email protected] spaces

npx @ruvnet/[email protected] mcp start

Agent runs are read-only by default. Workspace writes require both --allow-write and --confirm ; retrieved brain content is evidence, not authority.

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

docker run -p 3000:3000 ruvnet/wifi-densepose:latest

Open http://localhost:3000

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 <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 + 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://<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. 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 Inst

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