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docs: organize Key Features into Sensing, Intelligence, and Performance groups

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Co-Authored-By: claude-flow <ruv@ruv.net>
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2026-03-01 11:33:26 -05:00
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@@ -56,17 +56,20 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest
| | Feature | What It Means | | | Feature | What It Means |
|---|---------|---------------| |---|---------|---------------|
| | ***Sensing*** | |
| 🔒 | **Privacy-First** | Tracks human pose using only WiFi signals — no cameras, no video, no images stored | | 🔒 | **Privacy-First** | Tracks human pose using only WiFi signals — no cameras, no video, no images stored |
| ⚡ | **Real-Time** | Analyzes WiFi signals in under 100 microseconds per frame — fast enough for live monitoring |
| 💓 | **Vital Signs** | Detects breathing rate (6-30 breaths/min) and heart rate (40-120 bpm) without any wearable | | 💓 | **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) | | 👥 | **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 | | 🧱 | **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) | | 🚑 | **Disaster Response** | Detects trapped survivors through rubble and classifies injury severity (START triage) |
| 🐳 | **One-Command Setup** | `docker pull ruvnet/wifi-densepose:latest` — live sensing in 30 seconds, no toolchain needed | | | ***Intelligence*** | |
| 📦 | **Portable Models** | Trained models package into a single `.rvf` file — runs on edge, cloud, or browser (WASM) |
| 🧠 | **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)) | | 🧠 | **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)) | | 🎯 | **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*** | |
| ⚡ | **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 | | 🦀 | **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 |
| 📦 | **Portable Models** | Trained models package into a single `.rvf` file — runs on edge, cloud, or browser (WASM) |
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