ruvnet--RuView/docs/huggingface/MODEL_CARD.md

license, tags, language, library_name, pipeline_tag

license	tags	language	library_name	pipeline_tag
mit	wifi-sensing vital-signs presence-detection edge-ai esp32 self-supervised cognitum through-wall privacy-preserving spiking-neural-network ruvector	en	onnxruntime	other

RuView — WiFi Sensing Models

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?	presence detection (v1 "100%" retracted — single-class)	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
CSI embedding quality	82.3% held-out	Honest temporal-triplet metric; v1 single-class "100% presence" retracted (#882)
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 (v1 "100%" retracted — single-class; #882)
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

# 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

Using with the Rust sensing server (RVF conversion)

model.safetensors does not carry the RVFS binary-container magic that wifi-densepose-sensing-server's --model loader expects natively — it needs converting first (issue #894). As of #1480, --model auto-detects and converts model.safetensors / model.rvf.jsonl in-memory, so the one-liner below is enough for most uses:

cargo run -p wifi-densepose-sensing-server -- --model model.safetensors

To pre-convert once and skip re-conversion on every startup (recommended for repeated runs, or to inspect the converted container), use --convert-model:

cargo run -p wifi-densepose-sensing-server -- \
  --convert-model model.safetensors --convert-out model.rvf

cargo run -p wifi-densepose-sensing-server -- \
  --model model.rvf --load-rvf model.rvf

--model loads the weights for inference; --load-rvf separately populates the container metadata that /api/v1/model/info reports — pass both if you want that endpoint to reflect the loaded container.

Converting model.safetensors wires the format/load path (magic, version, segments, weights all valid) but the pose-decoder architecture published on HF differs from this crate's inference head, so the converted weights are not claimed to reproduce pose accuracy end-to-end (tracked in #894). model-q2/q4/q8.bin (quantized HF blobs) have no reader in this build yet — convert the full-precision model.safetensors instead.

Architecture

WiFi signals → ESP32-S3 ($9) → 8-dim features @ 1 Hz → Encoder → 128-dim embedding
                                                                    ↓
                                         ┌──────────────────────────┼──────────────────┐
                                         ↓                          ↓                  ↓
                                    Presence head            Activity head         Vitals head
                                    (v1 "100%" retracted)          (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

@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}
}

Links

• GitHub: https://github.com/ruvnet/RuView
• Cognitum Seed: https://cognitum.one
• RuVector: https://github.com/ruvnet/ruvector
• License: MIT