Files
ruvnet--RuView/v2/crates/wifi-densepose-calibration/src/enrollment.rs
T
rUv 17471e93ff ADR-152: WiFi-Pose SOTA 2026 intake — WiFlow-STD benchmark, Rust integrations, ADR-153 802.11bf layer, efficiency frontier (#1008)
* feat(calibration): NodeGeometry transceiver-geometry recording (ADR-152 §2.1.1)

PerceptAlign-motivated geometry capture at enrollment: per-node optional
records (position, antenna orientation, inter-node distances, acquisition
method) — recorded when known, never required. Event-sourced via
EnrollmentEvent::GeometryRecorded (latest recording wins); persisted on
SpecialistBank with serde defaults so pre-ADR-152 bank JSON loads cleanly
(fixture-proven, and geometry-free banks serialize byte-shape-identical
to the old schema); threaded through MultiNodeMixture as data only — the
learned geometry embeddings and algorithmic fusion use are §2.1.2,
deliberately deferred until the ADR-151 P6 LoRA heads exist.

Geometry recorded from now on means banks captured today remain usable
for layout-conditioned training later — you can't retroactively add
geometry to data you didn't record.

8 new tests (3 geometry, 2 anchor, 2 bank, 1 multistatic) + full-loop
extension (2-node geometry, one tape-measured + one unknown, surviving
the bank JSON round-trip the runtime loads from). 50/50 calibration
(both feature configs) + 23 CLI tests green.

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

* feat(training): two-checkerboard camera↔room calibration for ADR-079 labels (ADR-152 §2.1.3)

Defends the camera-supervised pipeline against PerceptAlign's
"coordinate overfitting": MediaPipe keypoints were emitted in raw camera
coordinates with no shared frame and no transceiver-geometry metadata —
the exact label shape that memorizes deployment layout and collapses
cross-layout.

- scripts/calibrate-camera-room.py + calibration_lib.py: OpenCV
  two-checkerboard calibration → versioned bundle JSON (intrinsics,
  camera→room extrinsics, checkerboard spec, transceiver geometry,
  sha256 calibration_id). Intrinsics resolve from file > cache >
  multi-view computation > loud-warning 2-view fallback.
- collect-ground-truth.py --calibration <bundle>: every sample gains
  keypoints_room (unit bearing rays from the camera center in the room
  frame — documented projective alignment; raw image coords preserved
  so training chooses), camera_origin_room, calibration_id, and the
  transceiver geometry stamp. Without the flag, output is byte-identical
  to before (tested) + a one-line ADR-152 warning.

Design finding (recorded for ADR-152): a single planar checkerboard's
corner grid is centrosymmetric — the reversed corner ordering fits a
ghost camera pose with IDENTICAL reprojection error, so per-board flip
disambiguation is mathematically ill-posed. solve_two_board_extrinsics
solves the joint wall+floor set over all 4 flip combinations, where the
minimum is unique — an independent reason the TWO-checkerboard method is
required, beyond what PerceptAlign states.

15 headless pytest tests green (synthetic corners: extrinsics recovery
incl. ghost resolution, bundle round-trip + hash stability, ray
transforms w/ distortion + cross-resolution, no-calibration byte
identity).

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

* feat(benchmarks): WiFlow-STD reproduction harness + measurement (a) results (ADR-152 §2.2)

Shipped checkpoint REFUTED (0.08% PCK@20, wrong keypoint normalization);
6 reproducibility defects documented (broken imports, corrupted dataset
tail with float32-max garbage that NaN-poisons fp16 BatchNorm, unreachable
test phase). After repairs, retraining with upstream defaults reproduces
96.09% PCK@20 full-test / 96.61% corruption-free (published 97.25%) on
RTX 5080. Claims graded MEASURED-EQUIVALENT; 2.23M params + ~0.055 GFLOPs
verified. Third-party code/weights/data stay out of tree (gitignored).

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

* feat: ADR-152 Rust integrations + ADR-153 802.11bf protocol model

- calibration: GeometryEmbedding — 32-slot permutation-invariant NodeGeometry
  featurization for future LoRA-head conditioning (ADR-152 §2.1.2); derived
  SpecialistBank::geometry_embedding() accessor; 59 tests
- train: MaePretrainConfig + patchify/random-mask with UNSW measured recipe
  (80% masking, (30,3) patches; ADR-152 §2.3, arXiv 2511.18792); strict
  no-truncate/no-NaN policy; proptest properties
- train: WiFlowStdModel — tch-gated port of the verified ~96%-PCK@20
  WiFlow-STD architecture (ADR-152 §2.2 beyond-SOTA); ungated param formula
  pinned to 2,225,042; 15/17-keypoint support; 239 crate tests
- hardware: ieee80211bf forward-compatibility protocol model (ADR-153):
  SpecProfile gates, SensingCapabilities negotiation, required ConsentMode,
  session FSM, SensingTransport + SimTransport + OpportunisticCsiBridge;
  full acceptance checklist covered; 156+4 tests
- deps: ruvector bumps per ADR-152 §2.6 survey (mincut/solver 2.0.6,
  attention 2.1.0, gnn 2.2.0); vendor/ruvector synced to a083bd77f
- docs: ADR-153 accepted; ADR-152 §2.2 status, §2.4 amendment, §2.6 added

Workspace: 162 test suites green (--no-default-features); Python proof PASS.
Known pre-existing flake: homecore-api env_empty_falls_back_to_defaults
(unserialized env-var mutation) — untouched, follow-up.

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

* docs: CHANGELOG + CLAUDE.md entries for ADR-152 integrations and ADR-153

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

* fix(train): repair tch-backend bit-rot — gated path compiles and tests run again

Mechanical API refresh against current tch: Vec::from(Tensor) -> try_from
(+ explicit flatten), numel() usize cast, Rem/div ops -> remainder() /
divide_scalar_mode(floor) — the latter fixed a silent true-division bug in
heatmap argmax decoding; clamp(1.0, f64::MAX) -> clamp_min (torch 2.x scalar
overflow panic); petgraph EdgeRef import; missing EvalMetrics and
verify_checkpoint_dir APIs that tests documented. wiflow_std roundtrip test
uses safetensors (.pt _save_parameters roundtrip broken in torch 2.11
Windows). Gated: 349 passed (incl. all 20 wiflow_std); ungated: unchanged.
Known pre-existing: gaussian-heatmap convention mismatch (2 tests), proof
seed race under parallel threads — documented, deliberate follow-ups.

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

* feat(train): WiFlow-STD PyTorch->tch weight import + numerical parity proof

export_to_safetensors.py maps the retrained checkpoint (295 tensors -> 248
mapped, param sum exactly 2,225,042; num_batches_tracked dropped) into a
tch-loadable safetensors plus a deterministic parity fixture. Gated #[ignore]
integration test loads it strictly and asserts forward-pass agreement:
max abs diff 1.192e-7 on the seed-42 fixture. dump_variable_names test makes
the tch name layout authoritative. Zero architecture discrepancies found.

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

* fix: workflow-review findings — BN gamma init, ThresholdParams serde, init docs

Concurrent validation workflow (2 review lanes + adversarial verification,
13 agents): 5 confirmed findings, 3 refuted. Fixes:
- wiflow_std: pin BatchNorm gamma to 1.0 (tch default draws Uniform(0,1) —
  silently halves activations in from-scratch training; loaded checkpoints
  unaffected, parity re-verified after the change)
- wiflow_std: document the conv-init divergences vs the reference's
  effective kaiming_normal(fan_out) re-init (from-scratch dynamics only)
- ieee80211bf: ThresholdParams deserialization validates via try_from so
  the <=100 invariant holds for untrusted payloads (+ rejection test)

Benchmarks (release, ruvzen): GeometryEmbedding 1.84us/call (542k/s),
MAE tokenization 7.38us/window (135k/s), 802.11bf FSM 8.9M events/s —
nothing suspicious.

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

* docs(adr): ADR-152 §2.1.4 gate resolved — PerceptAlign repo MIT, dataset on HF

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

* feat(benchmarks): edge optimization measured + measurement (b) blocked + 92.9% retraction

Edge optimization (ADR-152 optimize track): ONNX Runtime fp32 is the CPU
latency win (3.2 ms/window, ~3.4x faster than torch, parity 2.4e-7); ORT
dynamic int8 reaches 2.44 MB (paper's ~2.2 MB claim plausible only via
conv-capable toolchains; -0.16pt PCK@20, +18% MPJPE, 2x slower); torch
dynamic quant converts 0% of this conv-only model; fp16 halves storage free
but is slower on CPU.

Measurement (b) BLOCKED-ON-DATA: only 1,077 paired ESP32 windows exist
(stop rule <2k). Forensic recheck of the surviving April holdout RETRACTS
the ADR-079 '92.9% PCK@20' figure: constant-output model, absolute (not
torso) threshold, 69 near-static frames — mean predictor scores 100% under
that protocol; torso-PCK@20 is 19.1%. Corroborates PR #535. Stale citations
removed from user-guide, readme-details, ADR-152 §2.1.3; no-citation rule
extended to ADR-079 accuracy claims. Unblock: >=2k-window multi-pose paired
session + torso-PCK re-baseline.

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

* docs(user-guide): corrected camera-supervised collection tutorial

Step 0 CSI-rate check + session-length math (window yield = frames/20 —
the May session's 8x under-delivery was a ~12 Hz CSI rate, not an aligner
bug); two-checkerboard calibration step (ADR-152 §2.1.3); pose-variety and
confidence guidance; torso-normalized PCK + temporal-split + pred-variance
eval protocol (lessons from the 92.9% retraction); scale presets re-keyed
to realistic window counts.

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

* feat(benchmarks): static PTQ int8 (calibrated) results + overnight capture script

Conv-only static QDQ beats dynamic int8 on accuracy (PCK@20 96.61-96.63%
vs 96.52%, MPJPE +10% vs +18% over fp32) at ~equal size/latency; all-ops
QDQ strictly worse (int8 activations through attention glue). Entropy
calibration verified bit-identical to MinMax on this data. Deployment:
ONNX fp32 for speed (3.2ms), static conv-only QDQ for smallest (2.53MB).

Also: scripts/overnight-empty-capture.py — segmented UDP CSI recorder for
empty-room baselines (no glob collisions, detach-safe).

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

* feat(benchmarks): measurement (b) MEASURED — optimization transfer only, mean-pose baseline wins

WiFlow-STD fine-tuned on 2,046 fresh single-room ESP32 paired windows
(temporal 70/15/15, 70->540 adapter, K=17): pretrained-init 65% PCK@20 vs
scratch 0% (optimization transfer) but frozen-trunk ~0% (no feature
transfer), and NOTHING beats the mean-pose baseline (95.9% PCK@20 —
single subject, near-static normalized coords). Honesty gates held: pred
std 0.0113 (non-constant model) but mean-baseline dominance means no
citable CSI->pose capability from this data. ADR-152 open question 1
answered partially; definitive answer needs multi-subject/position data.

Two new aligner findings: heterogeneous csi_shape with silent zero-padding
(~20%), and extractCsiMatrix's transposed shape label (frame-major data,
[nSc, nFrames] label) — fixes pending.

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

* feat(benchmarks): efficiency sweep MEASURED — half model dominates full reference

Compact WiFlow-STD variants on the same data/split/protocol: half (843,834
params, 0.38x) strictly dominates the 2.23M reference (PCK@20 96.62 vs
96.61, PCK@50 99.47 vs 99.11, MPJPE 0.00898 vs 0.0094) — the published
architecture is over-parameterized for its own benchmark. quarter (338k)
96.05%; tiny (56,290 params, 1/39.5) holds 94.11% — a ~220KB fp32 edge
candidate. In-domain caveats recorded; cross-domain untested.

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

* feat(train): compact WiFlow-STD presets in Rust + tiny edge artifact (ADR-152)

WiFlowStdConfig gains half()/quarter()/tiny() mirroring the overnight sweep
exactly: TcnGroupsMode (Fixed/Gcd/Depthwise), input_pw_groups, derived
stride schedule and decoder-mid (all default to upstream behavior; legacy
serde JSON unaffected). Param formulas pin to trained ground truth first
try: 843,834 / 338,600 / 56,290; default 2,225,042 pin and 1.192e-7 parity
unchanged. 248 tests green.

Tiny edge artifact (tiny_edge_bench.py): ONNX fp32 = 295 KB, 0.66 ms/win
(~1,500/s CPU), 94.11% PCK@20 (matches sweep clean-test exactly; parity
1.49e-7). Static int8 is a bad trade at this scale (-1.43pt, +19% MPJPE,
-16% size, slower) — recorded as negative result. Export note: width-16
breaks AdaptiveAvgPool((15,1)) TorchScript export; replaced by exact
mean+matmul equivalent, proven by parity.

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

* fix: resolve all 10 confirmed code-review findings (7-angle review, 20/20 verified)

wiflow_std: min_feature_width (default 15) replaces the keypoints->stride
coupling — for_keypoints(17) now provably builds the trained [2,2,2,2]
graph and pools 15->17, matching the validated Python protocol (pinned by
tests); param_count() total on invalid configs; random_mask returns Result
and rejects non-finite/out-of-range ratios; trainer checkpoints switched
to safetensors (.pt VarStore roundtrip broken on Windows torch 2.11).

ieee80211bf: SBP proxy now re-triggers instances and relays reports via
Action::RelaySbpReport -> SensingFrame::SbpReport (clients consume via
their existing path); missed_instances reset on success = consecutive
semantics; SessionTable gains a guarded SBP entry point + unknown-id drop
counter; initiator-role sessions reject inbound setup/SBP requests
(RejectedNotSupported) closing the idle hijack; StartSetup/StartSbp
outside Idle return InvalidStateForCommand; SBP validation unified
through evaluate_setup with a 1:1 SetupStatus->SbpStatus mapping.
events.rs split out to honor the 500-line cap.

calibration/cli: enrollment geometry now actually reaches trained banks —
both production call sites attach .with_geometry; --geometry flag on
train-room and POST /enroll/geometry + train-body geometry on
calibrate-serve give production a recording surface; geometry-free banks
log the ADR-152 §2.1.2 note.

benchmarks: corruption masks committed as ground truth (unregenerable
after in-place cleaning; verified bit-identical regeneration from the
pristine copy) + generate_corruption_masks.py producer; _bench_common.py
dedups the 5x-copied shim/evaluate/seed/remap (post-refactor PCK@20
re-verified equal to the last digit); remote scripts get the mmap patch;
tiny_edge --calib validated multiple-of-64; onnx_bench --help no longer
executes (and overwrote) the export — artifact restored byte-exact.

Workspace: 2,963 tests passed, 0 failed; Python proof PASS.

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

* ci: build workspace tests without debuginfo — runner disk exhaustion

The combined 38-crate debug target exceeds the GitHub runner's disk
('final link failed: No space left on device'); the same tree measured
151GB locally with full debuginfo. CARGO_PROFILE_{DEV,TEST}_DEBUG=0
shrinks the target ~5-10x; debuginfo serves no purpose in CI test runs.

Co-Authored-By: claude-flow <ruv@ruv.net>
2026-06-11 17:02:23 -04:00

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//! Enrollment protocol — per-anchor capture with an adaptive quality gate
//! (ADR-151 Stage 2).
//!
//! Bad anchors poison small calibrated models far more than large ones, so an
//! anchor is only *accepted* when its captured statistics match what the anchor
//! is supposed to teach: a person present (or absent for `empty`), and the
//! expected stillness/motion. Failed anchors are re-prompted, not silently kept.
//!
//! Quality is measured against the ADR-135 empty-room baseline via
//! [`wifi_densepose_signal::BaselineCalibration::deviation`], whose
//! `CalibrationDeviationScore` gives a per-frame amplitude z-score (presence
//! strength).
//!
//! **Motion is NOT taken from the score's `motion_flagged`** (ADR-152 finding,
//! "z-band squeeze"): that flag fires on `amplitude_z_median > 2.0` — deviation
//! from the *empty* baseline — which conflates presence strength with motion. A
//! strongly-reflecting person standing perfectly still (z > 2 on every frame)
//! would be rejected as "too much motion". Instead the recorder derives motion
//! from the frame-to-frame *change* in the deviation series (|Δz| and |Δφ|),
//! which is presence-independent: a still strong reflector has high z but a
//! flat z-series; a moving person has a jittery one.
use wifi_densepose_core::types::CsiFrame;
use wifi_densepose_signal::{BaselineCalibration, CalibrationDeviationScore};
use crate::anchor::{Anchor, AnchorLabel, AnchorQuality};
/// Thresholds for accepting an anchor.
#[derive(Debug, Clone, Copy)]
pub struct AnchorQualityGate {
/// Minimum mean amplitude z-score to consider a person present.
pub min_presence_z: f32,
/// For `empty`: maximum mean z-score to consider the room truly empty.
pub empty_max_z: f32,
/// For "still" anchors: maximum motion-flag rate tolerated.
pub max_still_motion: f32,
/// For the "move" anchor: minimum motion-flag rate required.
pub min_move_motion: f32,
/// Minimum frames required to evaluate an anchor.
pub min_frames: u32,
}
impl Default for AnchorQualityGate {
fn default() -> Self {
Self {
min_presence_z: 1.5,
empty_max_z: 1.0,
max_still_motion: 0.6,
min_move_motion: 0.3,
min_frames: 60,
}
}
}
impl AnchorQualityGate {
/// Evaluate accumulated stats for `label`, returning the quality verdict
/// and (on rejection) a human-readable reason.
pub fn evaluate(
&self,
label: AnchorLabel,
presence_z: f32,
motion_rate: f32,
frames: u32,
) -> (AnchorQuality, Option<String>) {
let mut reason: Option<String> = None;
if frames < self.min_frames {
reason = Some(format!(
"only {frames} frames (need ≥{}); is the ESP32 streaming?",
self.min_frames
));
} else if label.expects_presence() {
if presence_z < self.min_presence_z {
reason = Some(format!(
"no person detected (presence_z {presence_z:.2} < {:.2}) — move closer / face the sensor",
self.min_presence_z
));
} else if label.expects_still() && motion_rate > self.max_still_motion {
reason = Some(format!(
"too much motion ({:.0}% > {:.0}%) for a still anchor — hold still",
motion_rate * 100.0,
self.max_still_motion * 100.0
));
} else if !label.expects_still() && motion_rate < self.min_move_motion {
reason = Some(format!(
"not enough motion ({:.0}% < {:.0}%) — move a bit more",
motion_rate * 100.0,
self.min_move_motion * 100.0
));
}
} else {
// `empty` anchor: the room must actually be empty.
if presence_z > self.empty_max_z {
reason = Some(format!(
"room not empty (presence_z {presence_z:.2} > {:.2}) — clear the room",
self.empty_max_z
));
}
}
let quality = AnchorQuality {
presence_z,
motion_rate,
frames,
accepted: reason.is_none(),
};
(quality, reason)
}
}
/// Frame-to-frame amplitude-z change above which a frame counts as motion.
///
/// Presence-independent by construction: a still person shifts the z *level*
/// but not its frame-to-frame delta (only noise-scale jitter survives), while
/// body movement modulates the reflected paths every frame. Sized well above
/// the delta the baseline's own noise floor produces (≲0.3σ) and well below
/// the delta even small limb movements produce (≳1σ). See ADR-152.
pub const Z_DELTA_MOTION: f32 = 0.5;
/// Frame-to-frame phase-drift change above which a frame counts as motion.
/// Same constant family as the absolute π/6 drift bound in
/// `CalibrationDeviationScore`, applied to the delta (static body phase shift
/// cancels out).
pub const PHASE_DELTA_MOTION: f32 = std::f32::consts::PI / 6.0;
/// Accumulates per-frame deviation statistics for a single anchor capture.
pub struct AnchorRecorder {
label: AnchorLabel,
z_sum: f64,
motion_count: u32,
frames: u32,
/// Previous frame's (amplitude_z_median, phase_drift_median) for the
/// delta-based motion measure (ADR-152 z-band-squeeze fix).
prev: Option<(f32, f32)>,
}
impl AnchorRecorder {
/// Start recording the given anchor.
pub fn new(label: AnchorLabel) -> Self {
Self {
label,
z_sum: 0.0,
motion_count: 0,
frames: 0,
prev: None,
}
}
/// The anchor being recorded.
pub fn label(&self) -> AnchorLabel {
self.label
}
/// Frames recorded so far.
pub fn frames(&self) -> u32 {
self.frames
}
/// Record a pre-computed deviation score (caller runs `baseline.deviation`).
///
/// Motion is derived from the frame-to-frame change of the deviation
/// series, NOT from `score.motion_flagged` — the flag conflates presence
/// strength with motion (z-band squeeze, see module docs / ADR-152). The
/// first frame of a capture is never motion (no predecessor).
pub fn record_score(&mut self, score: &CalibrationDeviationScore) {
let z = score.amplitude_z_median;
let phase = score.phase_drift_median;
if let Some((pz, pp)) = self.prev {
if (z - pz).abs() > Z_DELTA_MOTION || (phase - pp).abs() > PHASE_DELTA_MOTION {
self.motion_count += 1;
}
}
self.prev = Some((z, phase));
self.z_sum += z as f64;
self.frames += 1;
}
/// Convenience: record a CSI frame directly against a baseline.
/// Frames that fail baseline geometry checks are skipped (not counted).
pub fn record_frame(&mut self, baseline: &BaselineCalibration, frame: &CsiFrame) {
if let Ok(score) = baseline.deviation(frame) {
self.record_score(&score);
}
}
/// Mean presence z-score over the capture.
pub fn presence_z(&self) -> f32 {
if self.frames == 0 {
0.0
} else {
(self.z_sum / self.frames as f64) as f32
}
}
/// Fraction of frames flagged as motion.
pub fn motion_rate(&self) -> f32 {
if self.frames == 0 {
0.0
} else {
self.motion_count as f32 / self.frames as f32
}
}
/// Evaluate the capture against the gate and produce an `Anchor` (accepted
/// or not) plus a rejection reason.
pub fn finalize(&self, gate: &AnchorQualityGate, at_unix_s: i64) -> (Anchor, Option<String>) {
let (quality, reason) = gate.evaluate(
self.label,
self.presence_z(),
self.motion_rate(),
self.frames,
);
(
Anchor {
label: self.label,
captured_at_unix_s: at_unix_s,
quality,
},
reason,
)
}
}
#[cfg(test)]
mod tests {
use super::*;
/// Build a score the way `BaselineCalibration::deviation` actually would:
/// `motion_flagged` is DERIVED from z (z > 2.0 ⇒ flagged), never free.
/// The old tests mocked `(z=3.0, motion=false)` — a combination the real
/// producer can never emit, which is exactly how the z-band squeeze hid.
fn score(z: f32) -> CalibrationDeviationScore {
CalibrationDeviationScore {
amplitude_z_median: z,
amplitude_z_max: z + 1.0,
phase_drift_median: 0.05,
motion_flagged: z > 2.0,
}
}
/// Record a z-series and finalize against the default gate.
fn run_series(label: AnchorLabel, zs: &[f32]) -> (Anchor, Option<String>) {
let mut r = AnchorRecorder::new(label);
for &z in zs {
r.record_score(&score(z));
}
r.finalize(&AnchorQualityGate::default(), 100)
}
/// Constant z (a perfectly still capture at the given presence strength).
fn run_still(label: AnchorLabel, z: f32, n: usize) -> (Anchor, Option<String>) {
run_series(label, &vec![z; n])
}
/// Alternating z (every frame's |Δz| exceeds Z_DELTA_MOTION ⇒ all motion).
fn run_jittery(label: AnchorLabel, z: f32, n: usize) -> (Anchor, Option<String>) {
let zs: Vec<f32> = (0..n)
.map(|i| {
if i % 2 == 0 {
z
} else {
z + 2.0 * Z_DELTA_MOTION
}
})
.collect();
run_series(label, &zs)
}
/// ADR-152 z-band-squeeze regression: a STRONGLY-reflecting still person
/// (z = 3.0, so every frame is motion_flagged by the baseline heuristic)
/// must still pass a still anchor — presence strength is not motion.
#[test]
fn still_anchor_with_strong_still_person_accepts() {
let (a, reason) = run_still(AnchorLabel::StandStill, 3.0, 400);
assert!(a.quality.accepted, "z-band squeeze is back: {reason:?}");
assert!(reason.is_none());
assert!(
a.quality.motion_rate < 0.05,
"flat z-series must read still"
);
}
#[test]
fn still_anchor_rejects_when_no_presence() {
let (a, reason) = run_still(AnchorLabel::Sit, 0.4, 400);
assert!(!a.quality.accepted);
assert!(reason.unwrap().contains("no person"));
}
#[test]
fn still_anchor_rejects_on_motion() {
let (a, reason) = run_jittery(AnchorLabel::LieDown, 3.0, 400);
assert!(!a.quality.accepted);
assert!(reason.unwrap().contains("motion"));
}
#[test]
fn move_anchor_requires_motion() {
let (still, r1) = run_still(AnchorLabel::SmallMove, 3.0, 400);
assert!(!still.quality.accepted);
assert!(r1.unwrap().contains("not enough motion"));
let (moving, r2) = run_jittery(AnchorLabel::SmallMove, 3.0, 400);
assert!(moving.quality.accepted, "reason: {r2:?}");
}
#[test]
fn phase_delta_also_counts_as_motion() {
// Constant z but a phase-drift series that swings past PHASE_DELTA_MOTION
// every frame — motion must be detected from the phase channel alone.
let mut r = AnchorRecorder::new(AnchorLabel::LieDown);
for i in 0..400 {
let mut s = score(1.8);
s.phase_drift_median = if i % 2 == 0 {
0.0
} else {
PHASE_DELTA_MOTION * 1.5
};
r.record_score(&s);
}
let (a, reason) = r.finalize(&AnchorQualityGate::default(), 100);
assert!(!a.quality.accepted);
assert!(reason.unwrap().contains("motion"));
}
#[test]
fn empty_anchor_rejects_when_occupied() {
let (occupied, reason) = run_still(AnchorLabel::Empty, 3.0, 400);
assert!(!occupied.quality.accepted);
assert!(reason.unwrap().contains("not empty"));
let (empty, _) = run_still(AnchorLabel::Empty, 0.3, 400);
assert!(empty.quality.accepted);
}
#[test]
fn too_few_frames_rejected() {
let (a, reason) = run_still(AnchorLabel::Sit, 3.0, 10);
assert!(!a.quality.accepted);
assert!(reason.unwrap().contains("frames"));
}
}