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ruvnet--RuView/docs/adr/README.md
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rUv 2a307138f2 feat: per-room calibration system (ADR-151) + cognitum-v0 appliance integration spec (#989) 
* docs(adr): ADR-151 — Per-Room Calibration & Specialized Model Training

Room-first calibration -> bank of small specialised ruVector models
(breathing, heartbeat, restlessness, posture, presence, anomaly) distilled
from the frozen Hugging-Face-published RF Foundation Encoder (ADR-150).

Four-stage local-first pipeline: baseline (ADR-135 environmental fingerprint)
-> guided enrollment (NEW EnrollmentProtocol, clean anchors not hours) ->
feature extraction (reuse signal_features + ruvsense) -> specialist bank
training (rapid_adapt LoRA heads, RVF storage, HNSW prototypes).

Invariants: specialisation over scale; local heads over a shared public base;
honest STALE degradation on baseline drift. Indexes ADR-149/150/151.

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

* feat(cli): calibration HTTP API for UI-driven baseline capture (ADR-135/151)

Adds `wifi-densepose calibrate-serve` — an Axum HTTP API that wraps the
ADR-135 CalibrationRecorder so a UI (or any client) can drive an empty-room
baseline capture remotely. Stage 1 ("teach the room") of the ADR-151 room
calibration & training pipeline.

A single background task owns the UDP socket (ESP32 0xC511_0001 frames) and
the optional active recorder; HTTP handlers talk to it over an mpsc command
channel and read a shared status snapshot, keeping the &mut recorder
lock-free. CORS permissive so a browser UI can call it.

Endpoints (/api/v1/calibration/*):
  GET  /health      liveness + UDP ingest stats (frames_seen, streaming)
  POST /start       { tier?, duration_s?, room_id?, min_frames? }
  GET  /status      live progress (state, frames, progress, z, eta) — poll for UI
  POST /stop        finalize the current session early
  GET  /result      finalized baseline summary (amp/phase-dispersion averages)
  GET  /baselines   list persisted baseline .bin files

Reuses the existing calibrate.rs ESP32 wire parser (made pub(crate)); honest
abort when <10 frames arrive in the window (e.g. ESP32 not streaming).

Verified end-to-end over loopback: start -> 300 replayed HT20 frames ->
state=complete, 52-subcarrier baseline, phase_dispersion_avg=0.00096
(concentrated/valid), persisted to disk; all 6 endpoints exercised.
CLI: 19 tests pass; crate builds clean.

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

* test(cli): firewall-free CSI UDP relay for local Windows ESP32 testing

Windows Defender blocks inbound LAN UDP to a freshly-built binary without an
admin allow-rule; python.exe is already allowed. This relay binds the public
CSI port and forwards each datagram verbatim to a loopback port where
`calibrate-serve --udp-bind 127.0.0.1 --udp-port 5006` listens (loopback is
firewall-exempt). No admin required.

Validated: ESP32-format 0xC5110001 frames -> :5005 -> relay -> :5006 ->
calibrate-serve -> state=complete, 52-subcarrier baseline,
phase_dispersion_avg=0.00098 (clean). Completes the no-admin live-test path.

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

* docs(changelog): record ADR-151 calibration API (calibrate-serve)

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

* feat(calibration): ADR-151 Stages 2–5 — enrollment, extraction, specialist bank, runtime

New crate wifi-densepose-calibration implementing the per-room pipeline beyond
Stage-1 baseline:

- anchor.rs: guided-anchor sequence + event-sourced EnrollmentSession (Stage 2)
- enrollment.rs: AnchorQualityGate + AnchorRecorder — gates anchors against the
  ADR-135 baseline deviation (presence/motion), re-prompts bad captures
- extract.rs: Features + AnchorFeature — autocorrelation periodicity (breathing/
  HR bands), variance/motion (Stage 3)
- specialist.rs: 6 small room-calibrated models — presence (learned threshold),
  posture (nearest-prototype), breathing/heartbeat (band periodicity),
  restlessness (calm/active normalization), anomaly (novelty vs anchors) (Stage 4)
- bank.rs: SpecialistBank — train/persist + baseline-drift STALE invalidation
- runtime.rs: MixtureOfSpecialists — presence short-circuit + anomaly veto +
  stale flagging (Stage 5)

Statistical heads make the pipeline runnable/validatable today; the ADR-150 HF
RF Foundation Encoder backbone is the documented upgrade path. 29 unit tests pass.

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

* feat(cli): wire ADR-151 enroll / train-room / room-status / room-watch

Integrates the wifi-densepose-calibration crate into the CLI as four
subcommands driving the full Stage 2–5 pipeline against a live ESP32 raw-CSI
stream (edge_tier=0):

- enroll: walks the guided anchor sequence, gates each capture against the
  ADR-135 baseline deviation (re-prompts bad anchors), writes labelled features
- train-room: fits the SpecialistBank from the enrollment, persists JSON
- room-status: prints a trained bank's summary
- room-watch: live mixture-of-specialists readout (presence/posture/breathing/
  heart/restless) over a rolling window, with anomaly veto + STALE flagging

Per-frame scalar is the mean CSI amplitude (carries presence/motion + breathing
modulation). Validated end-to-end on the live ESP32 (COM8, edge_tier=0): the
real parser → feature extraction → runtime detected breathing (~16–31 BPM) on
hardware. Full multi-anchor enrollment accuracy requires the operator to perform
the poses; phase-based breathing extraction is a noted refinement.

48 tests pass (29 calibration + 19 CLI).

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

* docs(adr-151): mark Stages 1–5 implemented; expand CHANGELOG

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

* fix(cli): keep proven mean-amplitude carrier for room features

The max-variance-subcarrier carrier locked onto motion artifacts (not
breathing) and also had an out-of-bounds bug on variable CSI subcarrier
counts. Reverted to the mean-amplitude carrier, which is validated live to
detect breathing. Phase-based extraction on a stable subcarrier remains the
proper higher-SNR refinement (ADR-151 §4).

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

* feat(calibration): multistatic fusion of co-located nodes (ADR-029/151)

MultiNodeMixture fuses several co-located nodes (each with its own
room-calibrated SpecialistBank) into one RoomState:
- presence: OR across nodes (any node seeing a person wins)
- posture/breathing/heartbeat: highest-confidence node (best viewpoint)
- restlessness/anomaly: max across nodes
- veto: any node's physically-implausible signal vetoes the room's vitals
  (anti-hallucination, same as single-node runtime) + presence short-circuit
- stale: any node's STALE flag propagates

Same-room multistatic only; cross-room is federation (ADR-105), not fusion.
6 unit tests (presence OR, best-confidence breathing, single-node veto,
staleness). 35 calibration tests pass.

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

* feat(cli): multistatic room-watch — fuse co-located nodes (ADR-029/151)

`room-watch --node-bank N:path` (repeatable) groups live CSI frames by node_id
and fuses per-node banks via MultiNodeMixture. Validated live on COM8 (node 9,
edge_tier=0): frames grouped + fused end-to-end. True 2-node fusion is covered
by unit tests; a second raw-CSI node is the hardware blocker. 54 tests pass.

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

* docs(integration): calibration → cognitum-v0 appliance integration overview

Detailed cross-repo integration spec for cognitum-one/v0-appliance: data
contracts (CSI wire format, ADR-135 baseline binary, enrollment/bank/RoomState
JSON schemas), calibrate-serve HTTP API, public crate API, Pi5+Hailo tiering,
and a 5-step appliance integration plan. Grounded in the verified cognitum-v0
inventory (aarch64, cargo 1.96, HAILO10H, ruview-vitals-worker:50054).

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

* fix(calibration): address PR review — aarch64 decouple, API auth, path traversal, throttle

Resolves the review on #989:

- **Cross-compile (the appliance blocker):** make wifi-densepose-mat optional
  and feature-gate it (`mat`), so `cargo build -p wifi-densepose-cli
  --no-default-features` excludes the mat→nn→ort(ONNX)→openssl-sys chain.
  Verified: `cargo tree --no-default-features` shows 0 ort/openssl deps →
  calibration cross-compiles clean for the Pi.
- **Security (must-fix before LAN):**
  - `--token` / CALIBRATE_TOKEN bearer-auth middleware on every route; warns if
    bound non-loopback without a token.
  - sanitize client-supplied `room_id` to [A-Za-z0-9_-] (≤64) before it reaches
    the baseline write path — kills the `../` file-write primitive. + test.
- **Perf:** stop locking shared status + cloning SessionStatus on every UDP
  frame — counters/snapshot flush on the 200 ms tick instead (no CPU
  starvation under flood). finalize write moved to async `tokio::fs::write`.
- **Docs:** ADR-151 STALE wording matches the impl (baseline-id change;
  drift-threshold = P6 refinement); integration doc gets the
  `--no-default-features` build + auth/sanitize notes.

35 calibration + 15 CLI tests (no-default) / 20 CLI (default) pass.

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

* docs(worldgraph,worldmodel): add crates.io READMEs

Plain-language overviews + feature lists, comparison tables (symbolic graph vs
predictive occupancy; graph vs grid vs event-log), usage, and technical
details. Adds readme = "README.md" to both manifests so they render on
crates.io on the next release.

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

* release: worldgraph & worldmodel 0.3.1 (READMEs on crates.io)

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

* docs: precise calibration validation scope (capture+API+auth proven; clean enroll→train→infer not yet on-target)

Aligns ADR-151 §7 + the appliance integration doc with the PR #989 scope
clarification: nothing has run a clean baseline → enroll → train → infer on
live CSI; the live breathing read used the stateless head, not a trained bank.
Adds --source-format adr018v6 to the backlog.

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

* feat(calibrate-serve): live GET /room/state endpoint (mixture over CSI window)

Adds a live RoomState readout over HTTP — the appliance UI's main need. The
ingest task maintains a rolling per-frame scalar window (flushed on the 200 ms
tick, no per-frame lock); the handler loads a bank (resolved as a sanitized
name under output_dir — same path-traversal defense as room_id), runs the
MixtureOfSpecialists over the window, returns RoomState JSON.

Validated live (ESP32-S3 via relay): breathing 14-19 BPM over HTTP; a
bank=../../etc/passwd query is neutralized to 'etcpasswd' (no traversal).

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

* feat(calibrate-serve): POST /room/train + fix AnchorLabel JSON to snake_case

- POST /api/v1/room/train: { room_id, baseline_id, anchors[] } → trains a
  SpecialistBank and persists it as <output_dir>/<room_id>.json (path-sanitized),
  readable via /room/state?bank=<room_id>. Completes the HTTP train→infer loop.
- Fix data-contract bug: AnchorLabel serialized as PascalCase variant names
  (serde default) while as_str() + the integration doc used snake_case. Added
  #[serde(rename_all = "snake_case")] so the JSON wire format matches the
  documented contract (empty/stand_still/…). Locked with a roundtrip test.

Validated live (ESP32-S3): POST train (4 anchors → 6 specialists, persisted) →
GET /room/state returns RoomState with the trained presence/restlessness; the
synthetic-vs-real scale mismatch correctly triggers the anomaly veto. 36
calibration tests pass.

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

* feat(calibrate-serve): live enroll-over-HTTP (POST /enroll/anchor + /enroll/status)

Closes the last HTTP gap — the appliance can now drive the ENTIRE calibration
pipeline over HTTP without the CLI:
  baseline (start/stop) -> enroll/anchor x8 -> room/train -> room/state

- POST /enroll/anchor { room_id, baseline, label, duration_s? }: the ingest task
  loads the baseline (sanitized name under output_dir), captures the anchor for
  the duration against it (AnchorRecorder + per-frame series), runs the quality
  gate, and on completion replies with the verdict + accumulates the AnchorFeature
  in an in-server enrollment map keyed by room_id. Re-prompts on rejection.
- GET /enroll/status?room=<id>: accepted anchors, next, complete.
- POST /room/train now falls back to the in-server enrollment when anchors[] is
  omitted.

Validated live (ESP32-S3): capture baseline -> enroll stand_still (271 frames,
6s) -> gate correctly rejects "no person detected (presence_z 0.90 < 1.50)"
relative to a same-occupancy baseline (a clean empty-room baseline is the
documented on-target prerequisite). Builds clean; CLI tests pass.

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

* test(calibrate-serve): HTTP integration tests for the room/enroll endpoints

Factor the router into build_router() (shared by execute + tests) and add
tower-oneshot integration tests (no network/ingest needed):
- health + descriptor → 200
- POST /room/train persists the bank; GET /room/state → 200; train with no
  anchors/enrollment → 400
- path-traversal: /room/state?bank=../../etc/passwd → 404 (sanitized, never
  reads outside output_dir)
- enroll/status empty; /enroll/anchor with an unknown label → 400

CI regression coverage for the endpoints added this session. 18 CLI tests pass.

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

* fix(mat): make serde non-optional — unblocks `cargo test --workspace --no-default-features`

Making wifi-densepose-mat optional in the CLI (for the aarch64/ort decouple)
exposed a latent feature bug: mat's `api` module compiles unconditionally and
uses serde, but `serde` was an optional dep enabled only via the `api`/`serde`
features. Previously the CLI's *unconditional* mat dependency enabled those
features transitively, so `--workspace --no-default-features` still got serde;
once mat became optional+gated, the workspace build lost it →
`error[E0432]: unresolved import serde` across mat's api/* (CI red).

mat already pulls serde_json + axum unconditionally, so making `serde`
non-optional has no real cost and restores the workspace build. Does NOT affect
the aarch64 CLI build (mat isn't built there at all): verified
`cargo tree -p wifi-densepose-cli --no-default-features` still shows 0
ort/openssl deps, and `cargo test --workspace --no-default-features` compiles
clean.

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

* docs(claude.md): add wifi-densepose-calibration to crate table (pre-merge)

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

* docs(adr): ADR-152 — WiFi-pose SOTA 2026 intake (geometry-conditioned calibration, external benchmarks, encoder recipe)

Records the 2026-06-10 deep-research run (22 sources, 110 claims, 25
adversarially verified: 24 confirmed / 1 refuted) and the decisions it
implies:

- §2.1 ACCEPTED: geometry-condition the ADR-151 calibration system —
  NodeGeometry at enrollment, geometry embeddings for future LoRA heads,
  PerceptAlign-style two-checkerboard camera↔WiFi alignment for the
  ADR-079 supervised path. PerceptAlign (MobiCom'26) names the failure
  mode ("coordinate overfitting") that matches our own ADR-150 cross-
  subject collapse.
- §2.2 ACCEPTED: benchmark protocol vs external "WiFlow-STD (DY2434)"
  (claimed 97.25% PCK@20, Apache-2.0 weights+dataset) with a no-citation
  rule until measured on our 17-keypoint ESP32 eval set. Name collision
  with our internal WiFlow is disambiguated.
- §2.3 ACCEPTED: amend ADR-150 training recipe per UNSW MAE study —
  80% masking, (30,3) patches, data-over-capacity priority (log-linear,
  unsaturated at 1.3M samples).
- §2.4 watch items: IEEE 802.11bf-2025 published 2025-09-26;
  esp_wifi_sensing as external presence baseline (drop-in claim REFUTED
  0-3); ZTECSITool 160MHz/512-subcarrier anchor node (procurement-gated).
- §2.5 NOT adopted: non-WiFi "foundation model" papers; DensePose-UV
  (no 2025-2026 work does UV regression from commodity WiFi).

Every number is evidence-graded CLAIMED vs MEASURED in the source
register. Re-check horizon 2026-12.

Co-Authored-By: RuFlo <ruv@ruv.net>

* test(calibration): full-loop integration test — baseline→enroll→train→infer proven in-process (ADR-151 §7 gap, software half)

Closes the software half of PR #989's headline validation gap: the
complete calibration loop had never run end-to-end anywhere, even
in-process. tests/full_loop.rs (412 lines, deterministic xorshift32
room simulator, HT20/52-subcarrier/20Hz, same fingerprint family as
the ADR-135 roundtrip test) now drives the CLI's exact stage order
through the public API:

  1. baseline  — 600 static frames, zero motion flags post-warmup,
                 calibration_uuid() exactly as the CLI derives it
  2. enroll    — all 8 AnchorLabel::SEQUENCE anchors through
                 AnchorQualityGate::default(), session is_complete()
  3. extract   — AnchorFeature::from_series recovers injected 0.25Hz
                 and 0.125Hz breathing within ±0.04Hz
  4. train     — SpecialistBank::train fits all 6 specialists; JSON
                 round-trip and the runtime consumes the RELOADED bank
  5. infer     — positive: never-enrolled 0.30Hz subject reads present,
                 18±2 BPM; negative: empty window reads absent;
                 degradation: foreign baseline_id flags STALE

Seed-robust (5 seeds), passes with and without default features:
36 unit + 1 integration green.

Validation docs updated (ADR-151 §7 + integration doc §7 matrix): what
remains is strictly the on-target hardware session (real CSI, physically
empty room, operator performing the guided anchors). Three behavioral
findings from building the test are recorded for pre-session triage:
z-band squeeze between baseline motion flagging (z>2.0) and the still-
anchor gate (presence_z≥1.5) — likeliest on-hardware enroll failure;
variance-only PresenceSpecialist missing motionless-person mean shift;
ungated breathing_hz/heart_hz in noise-window embeddings.

Co-Authored-By: RuFlo <ruv@ruv.net>

* fix(calibration): close all four ADR-152 behavioral findings pre-hardware-session

The full-loop integration test surfaced three findings; fixing the third
exposed a fourth. All four are fixed and regression-guarded:

1. z-band squeeze (enrollment.rs) — anchor motion is now measured from
   frame-to-frame deltas of the deviation series (|Δz| > Z_DELTA_MOTION
   0.5 ∨ |Δφ| > π/6), not from the absolute motion_flagged, which fires
   at amplitude_z_median > 2.0 vs the EMPTY baseline and so conflated
   presence strength with motion. A strongly-reflecting still person
   (z = 3.0 — every frame flagged by the old heuristic) now enrolls.
   The old unit tests mocked (z=3.0, motion=false), a combination the
   real deviation() can never emit — which is exactly how the squeeze
   hid; tests now derive the flag from z the way the producer does.

2. variance-only presence (specialist.rs) — PresenceSpecialist gains a
   mean-shift channel: present when variance > threshold OR
   |mean − empty_mean| > mean_dist_threshold (trained at half the
   empty→occupied mean distance, None when the means don't separate).
   Detects the motionless person whose body raises the scalar mean but
   not its variance. Old persisted banks deserialize with the channel
   inert (serde default None) — variance-only behavior preserved,
   proven by a fixture test against pre-change JSON.

3. ungated hz embedding (extract.rs) — Features::embedding() zeroes
   breathing_hz/heart_hz below EMBED_MIN_SCORE (0.25), keeping the
   random in-band peaks of noise windows out of the posture/anomaly
   prototype space. Raw fields stay ungated (specialists have their
   own stricter gates).

4. heart-band lag-floor leakage (extract.rs, found while fixing 3) —
   a pure 0.30 Hz breathing signal scored 0.67 in the heart band at
   3.33 Hz: out-of-band rhythm leaks as a monotonic slope whose max
   sits at the band's lag floor, so score gating alone cannot stop it.
   autocorr_dominant now requires the winning lag to be an interior
   local maximum; band-edge "peaks" are rejected, true in-band peaks
   (interior by definition) are preserved.

full_loop.rs strengthened to drive the fixes end-to-end: the StandStill
anchor is now a z=3.0 strong reflector (unenrollable pre-fix), and a new
motionless-person runtime case proves mean-channel detection at empty-
level variance.

Validation: 41 calibration unit + 1 full-loop integration + 23 CLI tests
green; cargo test --workspace --no-default-features exit 0.

Co-Authored-By: RuFlo <ruv@ruv.net>
2026-06-10 15:21:09 -04:00

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Architecture Decision Records

This folder contains 45 Architecture Decision Records (ADRs) that document every significant technical choice in the RuView / WiFi-DensePose project.

Why ADRs?

Building a system that turns WiFi signals into human pose estimation involves hundreds of non-obvious decisions: which signal processing algorithms to use, how to bridge ESP32 firmware to a Rust pipeline, whether to run inference on-device or on a server, how to handle multi-person separation with limited subcarriers.

ADRs capture the context, options considered, decision made, and consequences for each of these choices. They serve three purposes:

  1. Institutional memory — Six months from now, anyone (human or AI) can read why we chose IIR bandpass filters over FIR for vital sign extraction, not just see the code.

  2. AI-assisted development — When an AI agent works on this codebase, ADRs give it the constraints and rationale it needs to make changes that align with the existing architecture. Without them, AI-generated code tends to drift — reinventing patterns that already exist, contradicting earlier decisions, or optimizing for the wrong tradeoffs.

  3. Review checkpoints — Each ADR is a reviewable artifact. When a proposed change touches the architecture, the ADR forces the author to articulate tradeoffs before writing code, not after.

ADRs and Domain-Driven Design

The project uses Domain-Driven Design (DDD) to organize code into bounded contexts — each with its own language, types, and responsibilities. ADRs and DDD work together:

  • ADRs define boundaries: ADR-029 (RuvSense) established multistatic sensing as a separate bounded context from single-node CSI. ADR-042 (CHCI) defined a new aggregate root for coherent channel imaging.
  • DDD models define the language: The RuvSense domain model defines terms like "coherence gate", "dwell time", and "TDM slot" that ADRs reference precisely.
  • Together they prevent drift: An AI agent reading ADR-039 knows that edge processing tiers are configured via NVS keys, not compile-time flags — because the ADR says so. The DDD model tells it which aggregate owns that configuration.

How ADRs are structured

Each ADR follows a consistent format:

  • Context — What problem or gap prompted this decision
  • Decision — What we chose to do and how
  • Consequences — What improved, what got harder, and what risks remain
  • References — Related ADRs, papers, and code paths

Statuses: Proposed (under discussion), Accepted (approved and/or implemented), Superseded (replaced by a later ADR).

ADR Index

Hardware and firmware

ADR Title Status
ADR-012 ESP32 CSI Sensor Mesh for Distributed Sensing Accepted (partial)
ADR-018 ESP32 Development Implementation Path Proposed
ADR-028 ESP32 Capability Audit and Witness Record Accepted
ADR-029 RuvSense Multistatic Sensing Mode (TDM, channel hopping) Proposed
ADR-032 Multistatic Mesh Security Hardening Accepted
ADR-039 ESP32-S3 Edge Intelligence Pipeline (on-device vitals) Accepted (hardware-validated)
ADR-040 WASM Programmable Sensing (Tier 3) Accepted
ADR-041 WASM Module Collection (65 edge modules) Accepted (hardware-validated)
ADR-044 Provisioning Tool Enhancements Proposed
ADR-110 ESP32-C6 firmware extension — Wi-Fi 6 / 802.15.4 / TWT / LP-core Accepted, P1-P10 complete, firmware-side substrate closed at v0.7.0-esp32. Companion docs: WITNESS-LOG-110 (13 §A0.x entries · 99.56 % cross-board RX · 104.1 µs smoothed sync stdev · ≤100 µs target met), ADR-110-REVIEW-GUIDE (one-page reviewer tour), ADR-110-BRANCH-STATE (coordination map vs feat/adr-115-ha-mqtt-matter). Host decoders + tests: Python SyncPacketParser (10) + Rust wifi_densepose_hardware::SyncPacket (15), cross-language hex pin gates drift.

Signal processing and sensing

ADR Title Status
ADR-013 Feature-Level Sensing on Commodity Gear Accepted
ADR-014 SOTA Signal Processing Algorithms Accepted
ADR-021 Vital Sign Detection (breathing, heart rate) Partial
ADR-030 Persistent Field Model and Drift Detection Proposed
ADR-033 CRV Signal Line Sensing Integration Proposed
ADR-037 Multi-Person Pose Detection from Single ESP32 Proposed
ADR-042 Coherent Human Channel Imaging (beyond CSI) Proposed
ADR-134 First-Class Channel Impulse Response (CIR) Support Proposed
ADR-135 Empty-Room Baseline Calibration (per-subcarrier Welford statistics) Proposed

Machine learning and training

ADR Title Status
ADR-005 SONA Self-Learning for Pose Estimation Partial
ADR-006 GNN-Enhanced CSI Pattern Recognition Partial
ADR-015 Public Dataset Strategy (MM-Fi, Wi-Pose) Accepted
ADR-016 RuVector Training Pipeline Integration Accepted
ADR-017 RuVector Signal + MAT Integration Proposed
ADR-020 Migrate AI Inference to Rust (ONNX Runtime) Accepted
ADR-023 Trained DensePose Model with RuVector Pipeline Proposed
ADR-024 Project AETHER: Contrastive CSI Embeddings Required
ADR-027 Project MERIDIAN: Cross-Environment Generalization Proposed
ADR-149 AetherArena: public spatial-intelligence benchmark on Hugging Face Proposed
ADR-150 RF Foundation Encoder: pose-preserving, subject/room/device-invariant CSI embedding Proposed
ADR-151 Per-Room Calibration & Specialized Model Training (room-first → bank of small ruVector specialists) Proposed
ADR-152 WiFi-Pose SOTA 2026 Intake: geometry-conditioned calibration, external benchmarks, foundation-encoder recipe Proposed

Platform and UI

ADR Title Status
ADR-019 Sensing-Only UI with Gaussian Splats Accepted
ADR-022 Windows WiFi Enhanced Fidelity (multi-BSSID) Partial
ADR-025 macOS CoreWLAN WiFi Sensing Proposed
ADR-031 RuView Sensing-First RF Mode Proposed
ADR-034 Expo React Native Mobile App Accepted
ADR-035 Live Sensing UI Accuracy and Data Transparency Accepted
ADR-036 Training Pipeline UI Integration Proposed
ADR-043 Sensing Server UI API Completion (14 endpoints) Accepted
ADR-115 Home Assistant integration via MQTT auto-discovery + Matter bridge (HA-DISCO + HA-FABRIC + HA-MIND) Accepted (MQTT track) / Proposed (Matter SDK P8b)
ADR-147 adam-mode — light theme toggle for the three.js realtime demo Proposed
ADR-148 yoga-mode — yoga pose detection, classification, and scoring for the three.js realtime demo Proposed

Architecture and infrastructure

ADR Title Status
ADR-001 WiFi-Mat Disaster Detection Architecture Accepted
ADR-002 RuVector RVF Integration Strategy Superseded
ADR-003 RVF Cognitive Containers for CSI Proposed
ADR-004 HNSW Vector Search for Fingerprinting Partial
ADR-007 Post-Quantum Cryptography for Sensing Proposed
ADR-008 Distributed Consensus for Multi-AP Proposed
ADR-009 RVF WASM Runtime for Edge Deployment Proposed
ADR-010 Witness Chains for Audit Trail Integrity Proposed
ADR-011 Proof-of-Reality and Mock Elimination Proposed
ADR-026 Survivor Track Lifecycle (MAT crate) Accepted
ADR-038 Sublinear GOAP for Roadmap Optimization Proposed
ADR-095 rvCSI — Edge RF Sensing Runtime Platform Proposed
ADR-096 rvCSI — Crate Topology, the napi-c Shim, and the napi-rs Node Surface Proposed
ADR-097 Adopt rvCSI as RuView's primary CSI runtime (phased adoption) Proposed
ADR-098 Evaluate ruvnet/midstream for RuView's CSI / WebSocket / mesh pipeline Rejected
ADR-099 Adopt midstream as RuView's real-time introspection + low-latency tap Proposed

Related

  • DDD Domain Models — Bounded context definitions, aggregate roots, and ubiquitous language
  • User Guide — Setup, API reference, and hardware instructions
  • Build Guide — Building from source
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