Ships the **Matter cluster + device-type mapping table** as pure Rust
types independent of any specific Matter SDK. SDK choice between
`matter-rs` and chip-tool FFI per ADR-115 §9.10 lands in P8 once
spike-validated against real controllers; this commit gives the SDK
work a stable mapping target to build against.
## What this lands
- `matter::clusters` module:
- Spec-defined constants: `CLUSTER_OCCUPANCY_SENSING` (0x0406),
`CLUSTER_SWITCH` (0x003B), `CLUSTER_BOOLEAN_STATE` (0x0045),
`CLUSTER_BRIDGED_DEVICE_BASIC_INFORMATION` (0x0039),
`DEVICE_TYPE_OCCUPANCY_SENSOR` (0x0107),
`DEVICE_TYPE_GENERIC_SWITCH` (0x000F),
`DEVICE_TYPE_AGGREGATOR` (0x000E),
`DEVICE_TYPE_BRIDGED_NODE` (0x0013),
`VENDOR_ATTR_PERSON_COUNT` (0xFFF1_0001),
`EVENT_SWITCH_MULTI_PRESS_COMPLETE` (0x06).
Values transcribed from Matter Core Spec 1.3 §A.1 + Device Library 1.3.
- `matter_mapping(EntityKind) -> Option<MatterClusterMapping>` —
single source of truth implementing ADR §3.11.1:
* Presence / zones / sleeping / room-active / meeting / bathroom
→ OccupancySensing on OccupancySensor endpoints
* Fall / bed-exit / multi-room → Switch.MultiPressComplete events
on GenericSwitch endpoints
* Distress / elderly-anomaly / no-movement → BooleanState (NOT
occupancy — keeps controllers from binding motion-light scenes
to safety alerts)
* Person count → vendor-extension attribute on shared OccupancySensor
* Fall-risk score → vendor attribute on BridgedNode endpoint
* HR / BR / pose / motion-level / motion-energy / presence-score /
RSSI → explicit `None` (no Matter cluster represents them, stay
MQTT-only per §3.11.4)
- `entity_on_matter` + `next_endpoint` helpers.
## Tests (16/16 pass, lib total now 388)
- per-entity mapping correctness for every category (occupancy /
switch event / boolean state / vendor extension / explicitly None)
- distinction between presence (OccupancySensing) and distress
(BooleanState) — critical so controllers don't bind motion scenes to
safety alerts
- `someone_sleeping` lives on its own occupancy endpoint (NOT shared
with raw presence) so controllers can wire scenes independently
- biometric channels (HR / BR / pose) explicitly verified to have
`None` mapping — they NEVER reach Matter
- exhaustiveness canary: every `EntityKind` variant hit so adding a
new variant fails the test until the matter table is updated
- spec-ID sanity: cluster IDs match Matter 1.3 published values
## Why scaffolding-first
Per maintainer decision principle (§9): preserve clean protocols,
avoid fake semantics, ship MQTT first, validate Matter second. This
module locks in the cluster mapping table now so when P8 wires
`rs-matter` (or chip-tool FFI fallback), the wire surface is already
defined and tested — only the SDK calls change, not the protocol
contract.
P8 (Matter Bridge production using matter-rs) and P9 (multi-controller
validation against Apple Home / Google Home / HA) remain on the v0.7.1
docket per §9.10.
Refs #776, PR #778.
Co-Authored-By: claude-flow <ruv@ruv.net>
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| benches | ||
| examples | ||
| src | ||
| tests | ||
| Cargo.toml | ||
| README.md | ||
README.md
wifi-densepose-sensing-server
Lightweight Axum server for real-time WiFi sensing with RuVector signal processing.
Overview
wifi-densepose-sensing-server is the operational backend for WiFi-DensePose. It receives raw CSI
frames from ESP32 hardware over UDP, runs them through the RuVector-powered signal processing
pipeline, and broadcasts processed sensing updates to browser clients via WebSocket. A built-in
static file server hosts the sensing UI on the same port.
The crate ships both a library (wifi_densepose_sensing_server) exposing the training and inference
modules, and a binary (sensing-server) that starts the full server stack.
Integrates wifi-densepose-wifiscan for multi-BSSID WiFi scanning per ADR-022 Phase 3.
Features
- UDP CSI ingestion -- Receives ESP32 CSI frames on port 5005 and parses them into the internal
CsiFramerepresentation. - Vital sign detection -- Pure-Rust FFT-based breathing rate (0.1--0.5 Hz) and heart rate (0.67--2.0 Hz) estimation from CSI amplitude time series (ADR-021).
- RVF container -- Standalone binary container format for packaging model weights, metadata, and
configuration into a single
.rvffile with 64-byte aligned segments. - RVF pipeline -- Progressive model loading with streaming segment decoding.
- Graph Transformer -- Cross-attention bottleneck between antenna-space CSI features and the
COCO 17-keypoint body graph, followed by GCN message passing (ADR-023 Phase 2). Pure
std, no ML dependencies. - SONA adaptation -- LoRA + EWC++ online adaptation for environment drift without catastrophic forgetting (ADR-023 Phase 5).
- Contrastive CSI embeddings -- Self-supervised SimCLR-style pretraining with InfoNCE loss, projection head, fingerprint indexing, and cross-modal pose alignment (ADR-024).
- Sparse inference -- Activation profiling, sparse matrix-vector multiply, INT8/FP16 quantization, and a full sparse inference engine for edge deployment (ADR-023 Phase 6).
- Dataset pipeline -- Training dataset loading and batching.
- Multi-BSSID scanning -- Windows
netshintegration for BSSID discovery viawifi-densepose-wifiscan(ADR-022). - WebSocket broadcast -- Real-time sensing updates pushed to all connected clients at
ws://localhost:8765/ws/sensing. - Static file serving -- Hosts the sensing UI on port 8080 with CORS headers.
Modules
| Module | Description |
|---|---|
vital_signs |
Breathing and heart rate extraction via FFT spectral analysis |
rvf_container |
RVF binary format builder and reader |
rvf_pipeline |
Progressive model loading from RVF containers |
graph_transformer |
Graph Transformer + GCN for CSI-to-pose estimation |
trainer |
Training loop orchestration |
dataset |
Training data loading and batching |
sona |
LoRA adapters and EWC++ continual learning |
sparse_inference |
Neuron profiling, sparse matmul, INT8/FP16 quantization |
embedding |
Contrastive CSI embedding model and fingerprint index |
Quick Start
# Build the server
cargo build -p wifi-densepose-sensing-server
# Run with default settings (HTTP :8080, UDP :5005, WS :8765)
cargo run -p wifi-densepose-sensing-server
# Run with custom ports
cargo run -p wifi-densepose-sensing-server -- \
--http-port 9000 \
--udp-port 5005 \
--static-dir ./ui
Using as a library
use wifi_densepose_sensing_server::vital_signs::VitalSignDetector;
// Create a detector with 20 Hz sample rate
let mut detector = VitalSignDetector::new(20.0);
// Feed CSI amplitude samples
for amplitude in csi_amplitudes.iter() {
detector.push_sample(*amplitude);
}
// Extract vital signs
if let Some(vitals) = detector.detect() {
println!("Breathing: {:.1} BPM", vitals.breathing_rate_bpm);
println!("Heart rate: {:.0} BPM", vitals.heart_rate_bpm);
}
Architecture
ESP32 ──UDP:5005──> [ CSI Receiver ]
|
[ Signal Pipeline ]
(vital_signs, graph_transformer, sona)
|
[ WebSocket Broadcast ]
|
Browser <──WS:8765── [ Axum Server :8080 ] ──> Static UI files
Related Crates
| Crate | Role |
|---|---|
wifi-densepose-wifiscan |
Multi-BSSID WiFi scanning (ADR-022) |
wifi-densepose-core |
Shared types and traits |
wifi-densepose-signal |
CSI signal processing algorithms |
wifi-densepose-hardware |
ESP32 hardware interfaces |
wifi-densepose-wasm |
Browser WASM bindings for the sensing UI |
wifi-densepose-train |
Full training pipeline with ruvector |
wifi-densepose-mat |
Disaster detection module |
License
MIT OR Apache-2.0