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

ruvnet · ruvnet · commit f89b81cdfa12 · 2026-03-01T11:33:26.000-05:00
Co-Authored-By: claude-flow &lt;ruv@ruv.net&gt;
diff --git a/README.md b/README.md
@@ -56,17 +56,20 @@ docker run -p 3000:3000 ruvnet/wifi-densepose:latest
 
 | | Feature | What It Means |
 |---|---------|---------------|
+| | ***Sensing*** | |
 | 🔒 | **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 |
 | 👥 | **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) |
-| 🐳 | **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) |
+| | ***Intelligence*** | |
 | 🧠 | **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*** | |
+| ⚡ | **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 |
+| 📦 | **Portable Models** | Trained models package into a single `.rvf` file — runs on edge, cloud, or browser (WASM) |
 
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