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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>
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# Per-Room Calibration — Integration Overview (for `cognitum-one/v0-appliance`)
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**Audience:** integrators wiring the RuView per-room calibration system (ADR-151) into the
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Cognitum V0 appliance (`cognitum-v0`, Pi 5 + Hailo). This document is the contract +
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deployment spec: data formats, API surface, crate API, and the appliance integration plan.
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**Source of truth:** crate `v2/crates/wifi-densepose-calibration` + CLI `v2/crates/wifi-densepose-cli`
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(`calibrate`, `calibrate-serve`, `enroll`, `train-room`, `room-status`, `room-watch`) on this PR's branch.
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---
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## 1. What it is
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"Teach the room before you teach the model." A local-first pipeline that turns a few minutes of
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clean human anchors — layered on an empty-room baseline — into a versioned **bank of small,
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room-calibrated specialists** for presence, posture, breathing, heartbeat, restlessness, and anomaly.
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```
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baseline (ADR-135) → enroll (anchors + quality gate) → extract (features) → train (specialist bank) → runtime (mixture + veto)
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environmental stand/sit/lie/breathe/move periodicity/variance 6 small models RoomState per window
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fingerprint (re-prompts bad captures) + STALE invalidation (+ multistatic fusion)
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```
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**Design invariants (carry these into the appliance):**
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- **Specialisation over scale** — six tiny models (threshold / nearest-prototype / autocorrelation), not one big model. They run in microseconds on a Pi CPU; **they do not need the Hailo HAT**.
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- **Local-first** — baselines + per-room banks stay on the device. Cross-room sharing is *model deltas* (federation, ADR-105), **never raw CSI**.
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- **Honest degradation** — baseline drift marks a bank `STALE`; a physically-implausible window is vetoed rather than emitting a hallucinated reading.
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---
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## 2. Tiering on the Pi 5 + Hailo (what runs where)
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| Tier | Runs on | What | Status |
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|------|---------|------|--------|
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| **CSI source** | ESP32-S3/C6 nodes (`edge_tier=0` raw CSI) | `0xC5110001` frames over UDP | shipping (v0.7.1-esp32) |
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| **Calibration service** | **Pi 5 CPU** (aarch64) | this crate: baseline/enroll/train/runtime + HTTP API | **this PR** |
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| **Shared backbone (optional)** | **Hailo HAT (HAILO10H)** | ADR-150 RF Foundation Encoder + neural pose head as HEF | future (ADR-150) |
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> The appliance's WiFi (`wlan0`) is `managed` with no nexmon — **the Pi is a CSI *processor*, not a CSI radio.** CSI arrives from the ESP32 nodes (the existing `ruview-vitals-worker:50054` already receives it). Calibration *consumes* that stream; it does not sense directly.
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---
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## 3. Data contracts (the integration surface)
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### 3.1 CSI ingest — ESP32 `0xC5110001` (UDP, little-endian)
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```
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Offset Size Field
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0 4 magic = 0xC511_0001 (LE u32)
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4 1 node_id (u8) ← group multistatic nodes by this
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5 1 n_antennas (u8)
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6 1 n_subcarriers (u8) ← 52/64 (HT20), 114 (HT40), 242 (HE20)
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7 1 reserved
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8 2 freq_mhz (LE u16)
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10 4 sequence (LE u32)
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14 1 rssi (i8)
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15 1 noise_floor (i8)
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16 4 reserved
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20 2·n_antennas·n_subcarriers IQ pairs: i (i8), q (i8)
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```
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Parser reference: `wifi-densepose-cli/src/calibrate.rs::parse_csi_packet`. The appliance can reuse the
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ESP32 stream the vitals worker already receives, or tee it to the calibration UDP port.
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### 3.2 Baseline (ADR-135) — binary, magic `0xCA1B_0001`
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```
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Header (16 B LE): magic(4)=0xCA1B0001, version(1)=1, tier(1) {0=HT20,1=HT40,2=HE20,3=HE40},
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reserved(2), captured_at_unix_s(8, i64)
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Body: frame_count(8,u64), num_subcarriers(4,u32),
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per subcarrier: amp_mean(f32), amp_variance(f32), phase_mean(f32), phase_dispersion(f32)
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```
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Produced by `calibrate` / `calibrate-serve`; `BaselineCalibration::{to_bytes,from_bytes}`. A baseline's
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UUID (`calibration_uuid()`) is the `baseline_id` referenced by enrollments and banks for STALE checks.
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### 3.3 Enrollment output — JSON (`enroll` → `train-room`)
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```jsonc
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{
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"room_id": "living-room",
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"baseline_id": "<uuid>",
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"fs_hz": 15.0,
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"anchors": [
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{ "room_id": "living-room", "label": "stand_still",
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"features": { "mean": f32, "variance": f32, "motion": f32,
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"breathing_score": f32, "breathing_hz": f32,
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"heart_score": f32, "heart_hz": f32 } }
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],
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"session": { "room_id": "...", "baseline_id": "...", "events": [ /* event-sourced audit log */ ] }
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}
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```
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Anchor labels (fixed sequence): `empty, stand_still, sit, lie_down, breathe_slow, breathe_normal, small_move, sleep_posture`.
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### 3.4 Specialist bank — JSON (`train-room` → `room-watch` / runtime)
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```jsonc
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{
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"room_id": "living-room",
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"baseline_id": "<uuid>", // drift vs current → STALE
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"trained_at_unix_s": 0,
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"anchor_count": 6,
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"presence": { "threshold": f32, "occupied_var": f32 } | null,
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"posture": { "prototypes": [ ["Standing", [f32;5]], ... ] } | null,
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"breathing": { "min_score": f32 },
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"heartbeat": { "min_score": f32 },
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"restlessness": { "calm_motion": f32, "active_motion": f32 } | null,
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"anomaly": { "prototypes": [ [f32;5], ... ], "scale": f32 } | null
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}
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```
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`SpecialistBank::{to_json,from_json}`. A *partial* bank is valid (missing-anchor specialists are `null`).
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### 3.5 Runtime output — `RoomState` JSON (per window)
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```jsonc
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{
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"presence": { "kind":"Presence", "value":0|1, "confidence":f32, "label":"present|absent" } | null,
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"posture": { "kind":"Posture", "value":f32, "confidence":f32, "label":"standing|sitting|lying" } | null,
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"breathing": { "kind":"Breathing", "value": <BPM>, "confidence":f32, "label":null } | null,
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"heartbeat": { "kind":"Heartbeat", "value": <BPM>, "confidence":f32, "label":null } | null,
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"restlessness": { "kind":"Restlessness", "value": 0.0..1.0, "confidence":f32 } | null,
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"anomaly": { "kind":"Anomaly", "value": 0.0..1.0, "confidence":f32, "label":"normal|anomalous" } | null,
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"vetoed": bool, // anomaly veto fired → vitals/posture suppressed
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"stale": bool // bank trained against a different baseline
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}
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```
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---
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## 4. HTTP API — `calibrate-serve` (CORS-enabled; this is what a UI/appliance drives)
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| Method | Path | Body / returns |
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|--------|------|----------------|
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| GET | `/api/v1/calibration/health` | `{ udp_port, frames_seen, last_frame_age_ms, streaming, default_tier, output_dir, session_active }` |
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| POST | `/api/v1/calibration/start` | `{ tier?, duration_s?, room_id?, min_frames? }` → `202` session snapshot |
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| GET | `/api/v1/calibration/status` | live `{ state, frames_recorded, target_frames, progress, z_median, eta_s, ... }` |
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| POST | `/api/v1/calibration/stop` | finalize early → result summary |
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| GET | `/api/v1/calibration/result` | last finalized baseline summary |
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| GET | `/api/v1/calibration/baselines` | list persisted `.bin` baselines |
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A single background task owns the UDP socket + recorder (handlers talk to it over an mpsc channel +
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shared status snapshot), so the API is non-blocking. Enrollment/train/room-state are CLI today
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(`enroll`/`train-room`/`room-watch`); exposing them over the same API is a small, additive follow-on —
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**recommended appliance enhancement** (see §6).
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---
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## 5. Public crate API (`wifi-densepose-calibration`)
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```rust
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// Stage 2 — enrollment
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anchor::{AnchorLabel, Anchor, AnchorQuality, EnrollmentEvent, EnrollmentSession, Posture}
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enrollment::{AnchorQualityGate, AnchorRecorder}
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// Stage 3 — features
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extract::{Features, AnchorFeature, autocorr_dominant}
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// Stage 4 — specialists + bank
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specialist::{Specialist, SpecialistKind, SpecialistReading,
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PresenceSpecialist, PostureSpecialist, BreathingSpecialist,
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HeartbeatSpecialist, RestlessnessSpecialist, AnomalySpecialist}
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bank::SpecialistBank
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// Stage 5 — runtime
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runtime::{MixtureOfSpecialists, RoomState}
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multistatic::MultiNodeMixture // fuse co-located nodes (ADR-029)
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```
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Pure Rust; deps are `wifi-densepose-core` + `wifi-densepose-signal` (default-features off) + serde/uuid.
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**No GPU / no system BLAS** in the calibration path → builds cleanly on aarch64.
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---
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## 6. Appliance integration plan (`cognitum-one/v0-appliance`)
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Verified on `cognitum-v0`: aarch64, `cargo 1.96.0`, Hailo `HAILO10H`, `ruview-vitals-worker:50054`.
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**Step 1 — vendor / depend on the crate.** Add `wifi-densepose-calibration` (path or published crate)
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to the appliance workspace. It builds natively (aarch64, no BLAS/GPU).
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**Step 2 — wire the CSI source.** Two options:
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- (a) Tee the ESP32 UDP stream the vitals worker already receives into the calibration ingest, or
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- (b) point ESP32 nodes (`edge_tier=0`) at the appliance's calibration UDP port directly.
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Reuse `parse_csi_packet` (or the rvCSI `CsiFrame` schema if you normalise upstream).
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**Step 3 — run the calibration service.** Either embed the crate (call `CalibrationRecorder` /
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`MixtureOfSpecialists` in-process from a worker like `ruview-vitals-worker`), or run the
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`calibrate-serve` binary as a sidecar (systemd unit, bind `127.0.0.1` + reverse-proxy through the
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appliance gateway on `:9000`). Persist baselines/banks under the appliance data dir, keyed by `room_id`.
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**Step 4 — expose to the dashboard.** Surface the `/api/v1/calibration/*` endpoints (and add
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`enroll`/`train`/`room-state` endpoints — small additive work) behind the appliance's bearer-token
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auth + the existing `Seeds`/`Edge` nav. `RoomState` (§3.5) is the live readout payload.
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**Step 5 — (optional) Hailo backbone tier.** Compile the ADR-150 RF Foundation Encoder + neural pose
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head to Hailo HEF, serve via `ruvector-hailo-worker:50051`; the small specialists become heads over its
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embedding. This is the ADR-150 follow-on — *not required* for the calibration service to run.
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**Privacy / security:** keep baselines + banks local; if federating across appliances (ADR-105),
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exchange bank/model deltas, never raw CSI. `calibrate-serve` CORS is permissive for dev — bind to
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loopback and gate via the appliance proxy in production.
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---
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## 7. Status & validation
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- **Implemented:** all 5 stages + multistatic fusion; CLI + Stage-1 HTTP API. **54 tests** (35 calibration + 19 CLI).
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- **Hardware-validated** (ESP32-S3, COM8, `edge_tier=0`): baseline capture (120 frames → 52-subcarrier baseline); live parser → feature-extraction → mixture runtime detecting **breathing (~16–31 BPM)**; multistatic ingest grouping/fusing by `node_id`.
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- **Known follow-ups (carry into the appliance backlog):** phase-based (vs mean-amplitude) breathing carrier for higher SNR; RVF/HNSW persistence (currently JSON); enroll/train HTTP endpoints; the ADR-150 Hailo backbone; true 2-node multistatic on hardware; ADR-105 federation across appliances.
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**Reference:** ADR-151 (`docs/adr/ADR-151-room-calibration-specialist-training.md`), ADR-135 (baseline),
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ADR-029 (multistatic), ADR-150 (RF Foundation Encoder), ADR-105 (federation), ADR-147 (OccWorld/Hailo).
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