Contactless breathing and heart rate monitoring extracted from WiFi CSI signals using the rvdna (RuVector DNA) signal processing pipeline. All processing runs locally on-device with no raw data leaving the host.

| Capability | Range | Method |

|------------|-------|--------|

| **Breathing Rate** | 6-30 BPM (0.1-0.5 Hz band) | Bandpass filter + FFT peak detection |

| **Heart Rate** | 40-120 BPM (0.8-2.0 Hz band) | Bandpass filter + FFT peak detection |

| **Sampling Rate** | 20 Hz (ESP32 CSI frames) | Real-time streaming |

| **Confidence Score** | 0.0-1.0 per vital sign | Spectral coherence + signal quality |

| Endpoint | Method | Description |

|----------|--------|-------------|

| `/api/v1/vital-signs` | GET | Latest breathing rate, heart rate, and confidence scores |

| `/ws/sensing` | WebSocket | Real-time stream with `vital_signs` field in each frame |

cd rust-port/wifi-densepose-rs

cargo build --release -p wifi-densepose-sensing-server

./target/release/sensing-server --source simulate --ui-path ../../ui

curl http://localhost:8080/api/v1/vital-signs

./target/release/sensing-server --source simulate --save-rvf model.rvf

See [ADR-021](docs/adr/ADR-021-vital-sign-detection-rvdna-pipeline.md) for the full signal processing pipeline design.

**wifi-densepose-wifiscan**: Multi-BSSID WiFi scanning domain layer for enhanced Windows WiFi sensing (ADR-022). Features an 8-stage pure-Rust signal intelligence pipeline: predictive gating, attention weighting, spatial correlation, motion estimation, breathing extraction, quality gating, fingerprint matching, and orchestration. Transforms multi-AP RSSI data into presence, motion, breathing rate, and posture estimates.

| Stage | Purpose |

|-------|---------|

| **Predictive Gating** | Pre-filter scan results using temporal prediction |

| **Attention Weighting** | Weight BSSIDs by signal relevance |

| **Spatial Correlation** | Cross-AP spatial signal correlation |

| **Motion Estimation** | Detect movement from RSSI variance |

| **Breathing Extraction** | Extract respiratory rate from sub-Hz RSSI oscillations |

| **Quality Gating** | Reject low-confidence estimates |

| **Fingerprint Matching** | Location and posture classification via RF fingerprints |

| **Orchestration** | Fuse all stages into unified sensing output |

**Build & Test:**

cd rust-port/wifi-densepose-rs

cargo test -p wifi-densepose-wifiscan

See [ADR-022](docs/adr/ADR-022-windows-wifi-enhanced-fidelity-ruvector.md) for the full pipeline design.

The RuVector Format (RVF) packages model weights, HNSW index, metadata, and WASM runtime into a single `.rvf` file for portable, single-file deployment.

| Property | Detail |

|----------|--------|

| **Format** | Segment-based binary (magic `0x52564653`) with 64-byte segment headers |

| **Progressive Loading** | Layer A in <5ms (entry points), Layer B in 100ms-1s (hot adjacency), Layer C (full graph) |

| **Signing** | Ed25519-signed training proofs for verifiable provenance |

| **Quantization** | Temperature-tiered (f32/f16/u8) via `rvf-quant` with SIMD distance |

| **CLI Flags** | `--save-rvf <path>` and `--load-rvf <path>` for model persistence |

An RVF container is a self-contained artifact: no external model files, no Python runtime, no pip dependencies. Load it on any host with the Rust binary.

See [ADR-023](docs/adr/ADR-023-trained-densepose-model-ruvector-pipeline.md) for the full trained DensePose pipeline.

The DensePose model uses a three-tier data strategy:

1. **Pre-train** on public datasets (MM-Fi, Wi-Pose) for cross-environment generalization. These provide paired WiFi CSI + camera pose labels across diverse environments and subjects.

2. **Fine-tune** with self-collected ESP32 data using a camera-based teacher model to generate pseudo-labels. Environment-specific multipath patterns are learned at this stage.

3. **SONA runtime adaptation** via micro-LoRA + EWC++ for continuous on-device learning. The model adapts to furniture changes, new occupants, and seasonal RF propagation shifts without retraining from scratch.

cargo run -p wifi-densepose-train -- \

--dataset mmfi --epochs 100 --lr 1e-3

cargo run -p wifi-densepose-train -- \

--dataset local --fine-tune --base-model pretrained.rvf \

--epochs 20 --lr 1e-4 --save-rvf finetuned.rvf

./target/release/sensing-server --source esp32 --load-rvf finetuned.rvf --sona-adapt

Eleven RuVector crates from `vendor/ruvector/` power the signal intelligence and neural network layers:

| Crate | Description | Used For |

|-------|-------------|----------|

| `ruvector-core` | VectorDB, HNSW index, SIMD distance, quantization | Waveform template matching, RVF container I/O |

| `ruvector-attention` | Scaled dot-product, MoE, PDE, sparse attention | Subcarrier weighting, spatial decoder |

| `ruvector-gnn` | Graph neural network, graph attention, EWC training | Body-graph reasoning, subcarrier correlation |

| `ruvector-nervous-system` | PredictiveLayer, OscillatoryRouter, EventBus, Hopfield | CSI preprocessing, frequency band isolation |

| `ruvector-coherence` | Spectral coherence, HNSW health, Fiedler value | Signal quality scoring, breathing/heartbeat isolation |

| `ruvector-temporal-tensor` | Tiered temporal compression (8/7/5/3-bit) | CSI frame buffering, vital sign storage |

| `ruvector-mincut` | Subpolynomial dynamic min-cut | Multi-person assignment |

| `ruvector-attn-mincut` | Attention-gated min-cut | Noise-suppressed spectrogram |

| `ruvector-solver` | Sparse Neumann solver O(sqrt(n)) | Subcarrier resampling, Fresnel geometry |

| `ruvector-graph-transformer` | Proof-gated graph transformer | CSI-to-pose cross-attention |

| `ruvector-sparse-inference` | PowerInfer-style sparse execution | Edge deployment, low-latency inference |

See `vendor/ruvector/` for the full crate source and documentation.

- [Vital Sign Detection](#-vital-sign-detection-adr-021)

- [WiFi Scan Domain Layer](#-wifi-scan-domain-layer-adr-022)

- [RVF Model Container](#-rvf-model-container-format)

- [Training and Fine-Tuning](#-training-and-fine-tuning)

- [RuVector Crates](#-ruvector-crates)

cargo test -p wifi-densepose-wifiscan

- **WiFi scan domain layer (ADR-022)** — Multi-BSSID WiFi scanning with 8-stage pure-Rust signal intelligence pipeline for enhanced Windows WiFi sensing: predictive gating, attention weighting, spatial correlation, motion estimation, breathing extraction, quality gating, fingerprint matching, and orchestration

- **Vital sign detection pipeline (ADR-021)** — Contactless breathing and heart rate monitoring via rvdna signal processing