Turn WiFi signals into spatial intelligence. Detect people, measure breathing and heart rate, 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. This model learned to read those changes from a $9 ESP32 chip.

What it senses	How well	Without
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

🆕 v2 update — honest re-benchmark + properly-converged encoder (2026-05-31)

The v1 contrastive encoder shipped with a flat training loss (every epoch logged the same 0.13517 — the optimizer was not actually learning), and its headline "100% presence accuracy" was measured on a single-class recording (an overnight capture of one sleeping person: 6,062 of 6,063 frames are labelled "present", 1 is "absent"). A constant "yes" predictor scores 99.98% on that split — so the number is real but says nothing about generalization. We are correcting that publicly rather than leaving it to stand.

v2 retrains the same 8 -> 64 -> 128 encoder with a working InfoNCE objective and reports an honest, label-free, time-disjoint metric: held-out temporal-triplet accuracy = P( d(anchor, temporal-positive) < d(anchor, temporal-negative) ), evaluated on the last 20% of the recording by time (no leakage into training).

Encoder	Held-out temporal-triplet accuracy	Notes
Raw 8-dim features (no encoder)	66.4%	baseline
Random-init encoder	69.6%	untrained
v2 trained encoder	82.3%	+15.9 pts over raw, properly converged

Plain language: the embedding now reliably places two CSI snapshots taken moments apart closer together than two taken far apart — i.e. it has learned the temporal structure of the radio environment, which is exactly what a useful self-supervised sensing embedding should do. v1, with its flat loss, was barely better than random on this same test.

Technical: 2-layer FC (BatchNorm + GELU) -> L2-normalized 128-dim embedding, 9,280 params, trained with InfoNCE (temperature 0.1, in-batch + temporal-far negatives), AdamW, 60 epochs. Temporal positives within 2 s; negatives >30 s apart. Time-disjoint 80/20 split.

v2 files & proof

File	Size	Use
`csi-embed-v2.safetensors`	~40 KB	fp32 trained encoder
`csi-embed-v2-int4.bin`	4.56 KB	4-bit packed encoder + fp16 standardizer — fits the 8 KB ESP32 SRAM budget
`csi-embed-v2.py`	<1 KB	`Enc` definition + loader
`csi-embed-v2-metrics.json`	—	full honest metrics + quantization scales

Encoder weights SHA-256: 3b37bca66e6050c50ccbc0f6e0501824f258bfdd8675dc0f4541b1e2e96feecd
Repro: python aether-arena/staging/train_csi_embed.py in github.com/ruvnet/RuView
Trained on the same local capture (data/recordings/overnight-1775217646.csi.jsonl, 6,063 feature frames).

What v2 does not claim. This is one room, one capture, two nodes. The triplet metric measures embedding quality, not downstream presence/vitals accuracy (which needs multi-class, multi-room labelled data we don't yet have for this 2.4 GHz feature). For pose SOTA on a public benchmark, see the separate 5 GHz model ruvnet/wifi-densepose-mmfi-pose (82.69% torso-PCK@20 on MM-Fi).

Benchmarks

Validated on real hardware (Apple M4 Pro + 2x ESP32-S3):

Metric	Result	Context
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
Contrastive learning	51.6% improvement	Trained on 8 hours of overnight data
Model size	8 KB (4-bit quantized)	Fits in ESP32 SRAM
Training time	12 minutes	On Mac Mini M4 Pro, no GPU needed
Camera required	No	Trained from 10 sensor signals

Models in This Repo

File	Size	Use
`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
`training-metrics.json`	3.1 KB	Loss curves and training history

Quick Start

bash

# Download 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

# Provision WiFi
python firmware/esp32-csi-node/provision.py --port COM9 \
  --ssid "YourWiFi" --password "secret" --target-ip YOUR_IP

# See what WiFi reveals about your room
node scripts/deep-scan.js --bind YOUR_IP --duration 10

Architecture

WiFi signals → ESP32-S3 ($9) → 8-dim features @ 1 Hz → Encoder → 128-dim embedding
                                                                    ↓
                                         ┌──────────────────────────┼──────────────────┐
                                         ↓                          ↓                  ↓
                                    Presence head            Activity head         Vitals head
                                    (100% accuracy)          (still/walk/talk)     (BR, HR)

The encoder converts 8 WiFi Channel State Information (CSI) features into a 128-dimensional embedding:

Dim	Feature	What it captures
0	Presence	How much the WiFi signal is disturbed
1	Motion	Rate of signal change (walking > typing > still)
2	Breathing	Chest movement modulates subcarrier phase at 6-30 BPM
3	Heart rate	Blood pulse creates micro-Doppler at 40-120 BPM
4	Phase variance	Signal quality — higher = more movement
5	Person count	Independent motion clusters via min-cut graph
6	Fall detected	Sudden phase acceleration followed by stillness
7	RSSI	Signal strength — indicates distance from sensor

Training Details

No camera was used. Trained using self-supervised contrastive learning:

Data: 60,630 samples from 2 ESP32-S3 nodes over 8 hours
Method: Triplet loss + InfoNCE (nearby frames = similar, distant = different)
Augmentation: 10x via temporal interpolation, noise, cross-node blending
Supervision: PIR sensor, BME280, RSSI triangulation, subcarrier asymmetry
Quantization: TurboQuant 2/4/8-bit with <0.5% quality loss
Adaptation: LoRA rank-4 per room, EWC to prevent forgetting

17 Sensing Applications

Built on these embeddings (RuView):

Core: Presence, person counting, RF scanning, SNN learning, CNN fingerprinting

Health: Sleep monitoring, apnea screening, stress detection, gait analysis

Environment: Room fingerprinting, material detection, device fingerprinting

Multi-frequency: RF tomography, passive radar, material classification, through-wall motion

Hardware

Component	Cost	Purpose
ESP32-S3 (8MB)	~$9	WiFi CSI sensing
Cognitum Seed (optional)	$131	Persistent storage, kNN, witness chain, AI proxy

Limitations

Room-specific (use LoRA adapters for new rooms)
Camera-free pose: 2.5% PCK@20 (camera labels improve significantly)
Health features are for screening only, not medical diagnosis
Breathing/HR less accurate during active movement

Citation

bibtex

@software{ruview2026,
  title={RuView: WiFi Sensing with Self-Supervised Contrastive Learning},
  author={rUv},
  year={2026},
  url={https://github.com/ruvnet/RuView},
  note={Models: https://huggingface.co/ruv/ruview}
}

aarongid/wifi-densepose-pretrained

RuView — WiFi Sensing Models