ruview

π RuView

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 — use camera ground-truth training for 92.9% PCK@20

Contributions and bug reports welcome at Issues.

See through walls with WiFi

Turn ordinary WiFi into a spacial 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.

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

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 also supports pose estimation (17 COCO keypoints via the WiFlow architecture), trained entirely without cameras using 10 sensor signals — a technique pioneered from the original DensePose From WiFi research at Carnegie Mellon University.

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
🦴 Pose estimation	CSI subcarrier amplitude/phase → 17 COCO keypoints	171K emb/s (M4 Pro)
🫁 Breathing detection	Bandpass 0.1-0.5 Hz → zero-crossing BPM	6-30 BPM
💓 Heart rate	Bandpass 0.8-2.0 Hz → zero-crossing BPM	40-120 BPM
👤 Presence sensing	Trained model + PIR fusion — 100% accuracy	0.012 ms latency
🧱 Through-wall	Fresnel zone geometry + multipath modeling	Up to 5m depth
🧠 Edge intelligence	8-dim feature vectors + RVF store on Cognitum Seed	$140 total BOM
🎯 Camera-free training	10 sensor signals, no labels needed	84s on M4 Pro
📷 Camera-supervised training	MediaPipe + ESP32 CSI → 92.9% PCK@20	19 min on laptop
📡 Multi-frequency mesh	Channel hopping across 6 bands, neighbor APs as illuminators	3x sensing bandwidth
🌐 3D point cloud (optional fusion)	Camera depth (MiDaS) + WiFi CSI + mmWave radar → unified spatial model	22 ms pipeline · 19K+ points/frame