wifi-densepose/v2/crates/wifi-densepose-sensing-server
rUv 9b07dff298
feat(beyond-sota): ADR-155 metric unification + ADR-156 RaBitQ Pass-2 (honest negative + latent topk bugfix) (#1053)
* refactor(train): hoist canonical PCK/OKS to un-gated metrics_core; fold test_metrics onto production (ADR-155 M1 §8)

ADR-155 §8 deferred item: test_metrics.rs reference kernels validated
production against their OWN reimplementation — a test that cannot catch a
canonical-impl bug (both could be wrong the same way).

- Extract canonical_torso_size / pck_canonical / oks_canonical / sigmas /
  bounding_box_diagonal into a new NON-tch-gated `metrics_core` module, so
  the single metric definition is reachable under
  `cargo test --no-default-features` (the `metrics` module is tch-gated).
  `metrics` re-exports every item → still exactly ONE implementation.
- Rewrite tests/test_metrics.rs to assert the PRODUCTION pck_canonical /
  oks_canonical equal hand-computed fixtures (not a reimplementation):
  canonical_pck_matches_hand_computed_fixture (corr=3/total=4/pck=0.75),
  hip↔hip normalizer pin, zero-visible⇒0.0, OKS perfect⇒1.0, fake-Gold pin.
- Keep an INDEPENDENT raw-threshold reference kernel only as a differential
  cross-check: test_kernel_agrees_with_canonical asserts it AGREES with
  canonical where torso==1.0 (genuine cross-check, not duplication).

Grade: MEASURED. test_metrics 10→12 tests, 0 failed.

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

* fix(sensing-server): relabel divergent live PCK/OKS so they're never conflated with canonical (ADR-155 M1 §2.1/§8 Goal C)

Goal C named training_api.rs:804 (torso-HEIGHT PCK). Auditing it surfaced
TWO findings the ADR-155 §1 table missed:

1. training_api.rs is an ORPHAN file — not declared `mod` in lib.rs OR main.rs,
   so it does NOT compile into the crate. It does not drive the live server.
2. The REAL live `best_pck`/`best_oks` (main.rs training path → RVF metadata
   JSON read by model_manager.rs) come from trainer.rs:
   - `pck_at_threshold` = RAW-threshold PCK, NO torso normalization (the most
     divergent kind), printed/serialized as bare "PCK@0.2".
   - `oks_map` calls `oks_single(area=1.0)` = the EXACT fake-Gold pattern
     ADR-155 §2.1 claimed closed elsewhere — still live here, inflating best_oks.

Resolution = RELABEL (torso/raw math is load-bearing on different data; the
pub fns can't be renamed without breaking API; sensing-server has no train/
ndarray dep). Honest unify is a tracked §8 backlog item.

- training_api.rs: `compute_pck` → `compute_pck_torso_height` + divergence doc;
  val_pck/best_pck/val_oks struct fields documented as torso-HEIGHT proxies;
  logs say `pck_torso_h@0.2`. Test torso_pck_is_labelled_distinctly_from_canonical.
- trainer.rs (LIVE): `pck_at_threshold` documented raw-unnormalized; `oks_map`
  area=1.0 flagged fake-Gold; test pck_at_threshold_is_raw_unnormalized_not_canonical.
- main.rs: live print relabelled `pck_raw@0.2` / `oks_map(area=1.0 proxy)`.

No wire-format field renames (back-compat); no pub-API rename (no silent break).
Grade: MEASURED (relabel + divergence pinned). sensing-server 450→451 lib tests, 0 failed.

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

* docs(adr-155): mark §8 metric items RESOLVED + audit map + honest §1 under-count correction (M1b Goals A/D)

- §8.1: full PCK/OKS audit map (every def: file:line, basis, canonical/
  legacy/distinct), the two §8 items marked RESOLVED with resolution+why.
- Honest finding: §1's "seven divergent metrics" was an UNDER-count —
  sensing-server's LIVE trainer.rs has a raw-unnormalized PCK and an
  area=1.0 fake-Gold OKS the table omitted, and the file §8 named
  (training_api.rs) is orphaned dead code. §9 honest-limits updated.
- Goal D: metrics.rs *_v2 variants confirmed caller-less + deprecated;
  noted for future cleanup, NOT deleted (public API, tch-gated).
- CHANGELOG [Unreleased] Fixed entry.

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

* feat(ruvector): RaBitQ Pass-2 randomized rotation + topk bugfix (ADR-156 §8)

Implements the deferred "Multi-bit / Extended RaBitQ Pass 2" backlog item
from ADR-156 §8: a deterministic randomized orthogonal rotation applied
before sign-quantization, the published RaBitQ construction (Gao & Long,
SIGMOD 2024).

Rotation construction: Fast Hadamard Transform + seeded ±1 sign flips
("HD" / randomized Hadamard), O(d log d) time and O(d) memory — a dense
d×d rotation is O(d²) and infeasible at the 65,535-d the wire format
provisions for. Pads to the next power of two; SplitMix64 seeds the sign
stream so index-time and query-time rotations are bit-identical.

API is additive and backward-compatible: Pass 1 (`from_embedding`) is
untouched; Pass 2 is opt-in via `Sketch::from_embedding_rotated` and
`SketchBank::with_rotation` (+ `insert_embedding` / `topk_embedding` /
`novelty_embedding` helpers that rotate consistently). Default behaviour
is unchanged.

While building the Pass-2 coverage harness, found and fixed a PRE-EXISTING
correctness bug in `SketchBank::topk`: the n>k heap path used
`BinaryHeap<Reverse<(d,id)>>` (a min-heap) but treated its peek as the
max, so it returned the k FARTHEST sketches as "nearest". The shipped unit
tests only exercised the n≤k fast path, so it went unnoticed. Fixed to a
plain max-heap; pinned by `topk_heap_path_returns_nearest` and
`tight_clusters_give_high_coverage_with_overfetch` (the latter measured
0.072 on the old code).

New tests (+17, 100→117 in the crate): rotation determinism/norm-preservation
(`rotation_is_deterministic_for_seed`, `rotation_preserves_norm`), Pass-2
shape-compatibility, `pass2_coverage_not_worse_than_pass1`, and a
deterministic coverage report.

MEASURED top-K coverage (anisotropic planted-cluster fixture, cosine ground
truth; dim=128 N=2048 K=8 64 clusters noise=0.35 128 queries):
  candidate_k=K=8 : Pass1 36.13% -> Pass2 46.39%  (both << 90% bar)
  candidate_k=24  : Pass1 83.89% -> Pass2 91.60%  (Pass2 clears 90%)
  candidate_k=32  : Pass1/Pass2 100%
Honest result: rotation consistently helps (+10pp at strict K), but neither
pass clears the ADR-084 90% bar at candidate_k==K on this distribution.
Pass 2 reaches 90% only with ~3x over-fetch (the ADR-084 "candidate set"
deployment pattern). Multi-bit Pass 3 evaluated separately.

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

* feat(ruvector): multi-bit Pass-3 experiment + ADR-156/084 measured results

Adds the multi-bit half of the ADR-156 §8 "Multi-bit / Extended RaBitQ"
item as a MEASURED experiment (coverage::measure_multibit): rotate, then
b-bit uniform scalar-quantize each coord, rank by L1 over codes — the
natural multi-bit generalization of hamming. Measures the bit/coverage
tradeoff the backlog item asked for.

MEASURED at the strict bar (candidate_k=K=8, anisotropic planted-cluster
fixture, cosine ground truth):
  Pass1 (1-bit, no rot)  36.13%   16 B/vec
  Pass2 (1-bit, rot)     46.39%   16 B/vec
  Pass3 (rot, 2-bit)     54.39%   32 B/vec
  Pass3 (rot, 3-bit)     66.70%   48 B/vec
  Pass3 (rot, 4-bit)     74.22%   64 B/vec
Honest: multi-bit monotonically helps but even 4-bit (4x memory) reaches
only 74% at the strict bar — neither rotation nor <=4-bit multi-bit clears
the strict-K 90% bar on this distribution. The bar is met via over-fetch
(Pass2 @ candidate_k=24). Tests: multibit_tradeoff_report,
multibit_1bit_matches_pass2_approx (+ sanity that 1-bit ~= Pass-2).

Docs:
- ADR-156 §8 item #2 marked RESOLVED-PARTIAL; §5 #2 grade CLAIMED ->
  MEASURED-on-our-hardware; new §10 with full measured tables, the topk
  bugfix disclosure, and graded deferred sub-items.
- ADR-084: "Pass 2" section answering the rotation open-question with
  measured numbers + the topk bug note.
- CHANGELOG [Unreleased]: Added (Pass-2 milestone) + Fixed (topk heap).

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-06-13 16:02:18 -04:00
..
benches ADR-115: Home Assistant + Matter integration (#778) 2026-05-23 16:13:28 -04:00
examples fix(sensing-server): wire MQTT publisher into the binary — closes #872 2026-05-31 09:39:21 -04:00
src feat(beyond-sota): ADR-155 metric unification + ADR-156 RaBitQ Pass-2 (honest negative + latent topk bugfix) (#1053) 2026-06-13 16:02:18 -04:00
tests fix(firmware): C6 IDF v5.5 guard + HE-LTF host ingest + WITNESS-LOG-110 B1 resolution (#1005) (#1011) 2026-06-11 11:00:37 -04:00
Cargo.toml release: bump signal 0.3.4 / sensing-server 0.3.3 / cli 0.3.1 (fixes #1009, #1004) 2026-06-12 16:55:27 -04:00
README.md chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427) 2026-04-25 21:28:13 -04:00

README.md

wifi-densepose-sensing-server

Crates.io Documentation License

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 CsiFrame representation.
  • 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 .rvf file 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 netsh integration for BSSID discovery via wifi-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
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