feat: Sensing-only UI mode with Gaussian splat visualization and Rust migration ADR

- Add Python WebSocket sensing server (ws_server.py) with ESP32 UDP CSI
  and Windows RSSI auto-detect collectors on port 8765
- Add Three.js Gaussian splat renderer with custom GLSL shaders for
  real-time WiFi signal field visualization (blue→green→red gradient)
- Add SensingTab component with RSSI sparkline, feature meters, and
  motion classification badge
- Add sensing.service.js WebSocket client with reconnect and simulation fallback
- Implement sensing-only mode: suppress all DensePose API calls when
  FastAPI backend (port 8000) is not running, clean console output
- ADR-019: Document sensing-only UI architecture and data flow
- ADR-020: Migrate AI/model inference to Rust with RuVector ONNX Runtime,
  replacing ~2.7GB Python stack with ~50MB static binary
- Add ruvnet/ruvector as upstream remote for RuVector crate ecosystem

Co-Authored-By: claude-flow <ruv@ruv.net>

docs/adr/ADR-013-feature-level-sensing-commodity-gear.md

@@ -1,7 +1,7 @@
 # ADR-013: Feature-Level Sensing on Commodity Gear (Option 3)
 
 ## Status
-Proposed
+Accepted — Implemented (36/36 unit tests pass, see `v1/src/sensing/` and `v1/tests/unit/test_sensing.py`)
 
 ## Date
 2026-02-28
@@ -373,6 +373,24 @@ class CommodityBackend(SensingBackend):
 - **Not a "pose estimation" demo**: This module honestly cannot do what the project name implies
 - **Lower credibility ceiling**: RSSI sensing is well-known; less impressive than CSI
 
+### Implementation Status
+
+The full commodity sensing pipeline is implemented in `v1/src/sensing/`:
+
+| Module | File | Description |
+|--------|------|-------------|
+| RSSI Collector | `rssi_collector.py` | `LinuxWifiCollector` (live hardware) + `SimulatedCollector` (deterministic testing) with ring buffer |
+| Feature Extractor | `feature_extractor.py` | `RssiFeatureExtractor` with Hann-windowed FFT, band power (breathing 0.1-0.5 Hz, motion 0.5-3 Hz), CUSUM change-point detection |
+| Classifier | `classifier.py` | `PresenceClassifier` with ABSENT/PRESENT_STILL/ACTIVE levels, confidence scoring |
+| Backend | `backend.py` | `CommodityBackend` wiring collector → extractor → classifier, reports PRESENCE + MOTION capabilities |
+
+**Test coverage**: 36 tests in `v1/tests/unit/test_sensing.py` — all passing:
+- `TestRingBuffer` (4), `TestSimulatedCollector` (5), `TestFeatureExtractor` (8), `TestCusum` (4), `TestPresenceClassifier` (7), `TestCommodityBackend` (6), `TestBandPower` (2)
+
+**Dependencies**: `numpy`, `scipy` (for FFT and spectral analysis)
+
+**Note**: `LinuxWifiCollector` requires a connected Linux WiFi interface (`/proc/net/wireless` or `iw`). On Windows or disconnected interfaces, use `SimulatedCollector` for development and testing.
+
 ## References
 
 - [Youssef et al. - Challenges in Device-Free Passive Localization](https://doi.org/10.1145/1287853.1287880)