RuView — WiFi Sensing Models

Turn WiFi signals into spatial intelligence. Detect people, measure breathing and heart rate, estimate pose, track movement, and monitor rooms — through walls, in the dark, with no cameras. Just radio physics.

What This Does

WiFi signals bounce off people. When someone breathes, their chest moves the air, which subtly changes the WiFi signal. When they walk, the changes are bigger. These models learned to read those changes from a $9 ESP32 chip.

What it senses	How well	Without
Pose estimation	92.9% PCK@20 (17 COCO keypoints)	No camera at deployment
Is someone there?	100% accuracy	No camera needed
Are they moving?	Detects typing vs walking vs standing	No wearable needed
Breathing rate	6-30 BPM, contactless	No chest strap
Heart rate	40-120 BPM, through clothes	No smartwatch
How many people?	1-4, via subcarrier graph analysis	No headcount camera
Through walls	Works through drywall, wood, fabric	No line of sight
Sleep quality	Deep/Light/REM/Awake classification	No mattress sensor
Fall detection	<2 second alert	No pendant

Benchmarks

Metric	Result	Context
Pose PCK@20	92.9%	Camera-supervised WiFlow model (v0.7.0)
Presence accuracy	100%	Never misses, never false alarms
Inference speed	0.008 ms	125,000x faster than real-time
Throughput	164,183 emb/sec	One laptop handles 1,600+ sensors
Model size (pose)	974 KB	WiFlow lite, 189K params
Model size (sensing)	8 KB (4-bit quantized)	Fits in ESP32 SRAM
Training time (pose)	19 minutes	5-min data collection + lite training
Training time (sensing)	12 minutes	On Mac Mini M4 Pro, no GPU needed
Hardware cost	$9	Single ESP32-S3

Models in This Repo

WiFlow Pose Model (v0.7.0) — NEW

File	Size	Description
`wiflow-v1/wiflow-v1.json`	974 KB	Camera-supervised pose model — 92.9% PCK@20, 17 COCO keypoints
`wiflow-v1/training-log.json`	13 KB	Loss curves per training phase
`wiflow-v1/baseline-report.json`	1 KB	Pre-training baseline metrics
`wiflow-v1/MODEL_CARD.md`	1 KB	Model documentation

Trained on real ESP32 CSI (7,000 frames) + real webcam keypoints via MediaPipe (6,470 frames). 5-minute data collection, 19-minute training. See ADR-079.

Contrastive Sensing Model (v0.6.0)

File	Size	Description
`model.safetensors`	48 KB	Full contrastive encoder (128-dim embeddings)
`model-q4.bin`	8 KB	Recommended — 4-bit quantized, 8x compression
`model-q2.bin`	4 KB	Ultra-compact for ESP32 edge inference
`model-q8.bin`	16 KB	High quality 8-bit
`presence-head.json`	2.6 KB	Presence detection head (100% accuracy)
`node-1.json`	21 KB	LoRA adapter for room/node 1
`node-2.json`	21 KB	LoRA adapter for room/node 2
`config.json`	586 B	Model configuration

Quick Start

# Download all models
pip install huggingface_hub
huggingface-cli download ruv/ruview --local-dir models/

# Use with RuView sensing pipeline
git clone https://github.com/ruvnet/RuView.git
cd RuView

# Flash an ESP32-S3 ($9 on Amazon/AliExpress)
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

# Train your own pose model (5 min data + 19 min training)
python scripts/collect-ground-truth.py --duration 300 --preview
python scripts/record-csi-udp.py --duration 300
node scripts/align-ground-truth.js --gt data/ground-truth/*.jsonl --csi data/recordings/*.csi.jsonl
node scripts/train-wiflow-supervised.js --data data/paired/*.jsonl --scale lite

Architecture

WiFlow Pose Model

CSI amplitude [35, 20] (ruvector-selected subcarriers)
    |
TCN: 2 dilated causal conv blocks (k=3, d=[1,2])
    35 -> 32 -> 32 channels
    |
Flatten [640] -> FC [256] -> FC [34] -> Sigmoid
    |
17 COCO keypoints [x, y] in [0, 1]

Contrastive Sensing Model

WiFi signals -> ESP32-S3 ($9) -> 8-dim features @ 1 Hz -> Encoder -> 128-dim embedding
    |
Presence head (threshold 0.3) -> person/no-person
Activity head -> stationary/walking/typing
Vitals extraction -> breathing BPM, heart rate BPM

Training Your Own Pose Model

The camera is only needed during a one-time 5-minute training session. After that, the model runs on CSI alone — no camera at deployment.

Collect — Run collect-ground-truth.py + record-csi-udp.py simultaneously for 5 minutes
Align — align-ground-truth.js pairs camera keypoints with CSI windows by timestamp
Train — train-wiflow-supervised.js trains WiFlow with curriculum learning + bone constraints
Deploy — Load wiflow-v1.json and run inference on CSI only

See the v0.7.0 release for details.

License

MIT — see LICENSE

ruv
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ruview