dearsky/wifi-densepose
1
0
Fork 0
Code Issues 20 Pull Requests 5 Actions Packages Projects Releases 1 Wiki Activity

docs: break Key Features into three titled tables with descriptions

Browse Source 
Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
ruv
2026-03-01 11:34:44 -05:00
parent f89b81cdfa
commit 9e483e2c0f
1 changed files with 18 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

21
README.md
View File

@@ -54,18 +54,33 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest
## 🚀 Key Features
### Sensing
See people, breathing, and heartbeats through walls — using only WiFi signals already in the room.
| | Feature | What It Means |
|---|---------|---------------|
| | ***Sensing*** | |
| 🔒 | **Privacy-First** | Tracks human pose using only WiFi signals — no cameras, no video, no images stored |
| 💓 | **Vital Signs** | Detects breathing rate (6-30 breaths/min) and heart rate (40-120 bpm) without any wearable |
| 👥 | **Multi-Person** | Tracks multiple people simultaneously, each with independent pose and vitals — no hard software limit (physics: ~3-5 per AP with 56 subcarriers, more with multi-AP) |
| 🧱 | **Through-Wall** | WiFi passes through walls, furniture, and debris — works where cameras cannot |
| 🚑 | **Disaster Response** | Detects trapped survivors through rubble and classifies injury severity (START triage) |
| | ***Intelligence*** | |
### Intelligence
The system learns on its own and gets smarter over time — no hand-tuning, no labeled data required.
| | Feature | What It Means |
|---|---------|---------------|
| 🧠 | **Self-Learning** | Teaches itself from raw WiFi data — no labeled training sets, no cameras needed to bootstrap ([ADR-024](#self-learning-wifi-ai-adr-024)) |
| 🎯 | **AI Signal Processing** | Attention networks, graph algorithms, and smart compression replace hand-tuned thresholds — adapts to each room automatically ([RuVector](#ai-backbone-ruvector)) |
| | ***Performance & Deployment*** | |
### Performance & Deployment
Fast enough for real-time use, small enough for edge devices, simple enough for one-command setup.
| | Feature | What It Means |
|---|---------|---------------|
| ⚡ | **Real-Time** | Analyzes WiFi signals in under 100 microseconds per frame — fast enough for live monitoring |
| 🦀 | **810x Faster** | Complete Rust rewrite: 54,000 frames/sec pipeline, 132 MB Docker image, 542+ tests |
| 🐳 | **One-Command Setup** | `docker pull ruvnet/wifi-densepose:latest` — live sensing in 30 seconds, no toolchain needed |