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docs: move AI Backbone into collapsed section under Models & Training

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- Remove RuVector AI section from Rust Crates details block
- Add as own collapsed <details> in Models & Training with anchor link
- Add cross-reference from crates table to new section
- Link to issue #67 for deep dive with code examples

Co-Authored-By: claude-flow <ruv@ruv.net>
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2026-03-01 11:23:15 -05:00
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@@ -364,33 +364,7 @@ cargo add wifi-densepose-ruvector # RuVector v2.0.4 integration layer (ADR-017
| [`wifi-densepose-config`](https://crates.io/crates/wifi-densepose-config) | Configuration management | -- | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-config.svg)](https://crates.io/crates/wifi-densepose-config) |
| [`wifi-densepose-db`](https://crates.io/crates/wifi-densepose-db) | Database persistence (PostgreSQL, SQLite, Redis) | -- | [![crates.io](https://img.shields.io/crates/v/wifi-densepose-db.svg)](https://crates.io/crates/wifi-densepose-db) |
#### AI Backbone: [RuVector v2.0.4](https://github.com/ruvnet/ruvector)
Raw WiFi signals are noisy, redundant, and environment-dependent. [RuVector](https://github.com/ruvnet/ruvector) is the AI intelligence layer that transforms them into clean, structured input for the DensePose neural network. It uses **attention mechanisms** to learn which signals to trust, **graph algorithms** that automatically discover which WiFi channels are sensitive to body motion, and **compressed representations** that make edge inference possible on an $8 microcontroller.
```
Raw WiFi CSI (56 subcarriers, noisy)
|
+-- ruvector-mincut ---------- Which channels carry body-motion signal? (learned graph partitioning)
+-- ruvector-attn-mincut ----- Which time frames are signal vs noise? (attention-gated filtering)
+-- ruvector-attention ------- How to fuse multi-antenna data? (learned weighted aggregation)
|
v
Clean, structured signal --> DensePose Neural Network --> 17-keypoint body pose
--> FFT Vital Signs -----------> breathing rate, heart rate
--> ruvector-solver ------------> physics-based localization
```
The [`wifi-densepose-ruvector`](https://crates.io/crates/wifi-densepose-ruvector) crate ([ADR-017](docs/adr/ADR-017-ruvector-signal-mat-integration.md)) connects all 7 integration points:
| AI Capability | What It Replaces | RuVector Crate | Result |
|--------------|-----------------|----------------|--------|
| **Self-optimizing channel selection** | Hand-tuned thresholds that break when rooms change | `ruvector-mincut` | Graph min-cut adapts to any environment automatically |
| **Attention-based signal cleaning** | Fixed energy cutoffs that miss subtle breathing | `ruvector-attn-mincut` | Learned gating amplifies body signals, suppresses noise |
| **Learned signal fusion** | Simple averaging where one bad channel corrupts all | `ruvector-attention` | Transformer-style attention downweights corrupted channels |
| **Physics-informed localization** | Expensive nonlinear solvers | `ruvector-solver` | Sparse least-squares Fresnel geometry in real-time |
| **O(1) survivor triangulation** | O(N^3) matrix inversion | `ruvector-solver` | Neumann series linearization for instant position updates |
| **75% memory compression** | 13.4 MB breathing buffers that overflow edge devices | `ruvector-temporal-tensor` | Tiered 3-8 bit quantization fits 60s of vitals in 3.4 MB |
All crates integrate with [RuVector v2.0.4](https://github.com/ruvnet/ruvector) — see [AI Backbone](#ai-backbone-ruvector) below.
</details>
@@ -724,6 +698,41 @@ See [ADR-014](docs/adr/ADR-014-sota-signal-processing.md) for full mathematical
## 🧠 Models & Training
<details>
<summary><a id="ai-backbone-ruvector"></a><strong>🤖 AI Backbone: RuVector</strong> — Attention, graph algorithms, and edge-AI compression powering the sensing pipeline</summary>
Raw WiFi signals are noisy, redundant, and environment-dependent. [RuVector](https://github.com/ruvnet/ruvector) is the AI intelligence layer that transforms them into clean, structured input for the DensePose neural network. It uses **attention mechanisms** to learn which signals to trust, **graph algorithms** that automatically discover which WiFi channels are sensitive to body motion, and **compressed representations** that make edge inference possible on an $8 microcontroller.
Without RuVector, WiFi DensePose would need hand-tuned thresholds, brute-force matrix math, and 4x more memory — making real-time edge inference impossible.
```
Raw WiFi CSI (56 subcarriers, noisy)
|
+-- ruvector-mincut ---------- Which channels carry body-motion signal? (learned graph partitioning)
+-- ruvector-attn-mincut ----- Which time frames are signal vs noise? (attention-gated filtering)
+-- ruvector-attention ------- How to fuse multi-antenna data? (learned weighted aggregation)
|
v
Clean, structured signal --> DensePose Neural Network --> 17-keypoint body pose
--> FFT Vital Signs -----------> breathing rate, heart rate
--> ruvector-solver ------------> physics-based localization
```
The [`wifi-densepose-ruvector`](https://crates.io/crates/wifi-densepose-ruvector) crate ([ADR-017](docs/adr/ADR-017-ruvector-signal-mat-integration.md)) connects all 7 integration points:
| AI Capability | What It Replaces | RuVector Crate | Result |
|--------------|-----------------|----------------|--------|
| **Self-optimizing channel selection** | Hand-tuned thresholds that break when rooms change | `ruvector-mincut` | Graph min-cut adapts to any environment automatically |
| **Attention-based signal cleaning** | Fixed energy cutoffs that miss subtle breathing | `ruvector-attn-mincut` | Learned gating amplifies body signals, suppresses noise |
| **Learned signal fusion** | Simple averaging where one bad channel corrupts all | `ruvector-attention` | Transformer-style attention downweights corrupted channels |
| **Physics-informed localization** | Expensive nonlinear solvers | `ruvector-solver` | Sparse least-squares Fresnel geometry in real-time |
| **O(1) survivor triangulation** | O(N^3) matrix inversion | `ruvector-solver` | Neumann series linearization for instant position updates |
| **75% memory compression** | 13.4 MB breathing buffers that overflow edge devices | `ruvector-temporal-tensor` | Tiered 3-8 bit quantization fits 60s of vitals in 3.4 MB |
See [issue #67](https://github.com/ruvnet/wifi-densepose/issues/67) for a deep dive with code examples, or [`cargo add wifi-densepose-ruvector`](https://crates.io/crates/wifi-densepose-ruvector) to use it directly.
</details>
<details>
<summary><a id="rvf-model-container"></a><strong>📦 RVF Model Container</strong> — Single-file deployment with progressive loading</summary>