The FFT will always find *some* peak in 0.8-2.0 Hz, even on pure
clutter, and the peak-to-mean ratio frequently lands at 0.5-0.7
"confidence" from noise alone. Net result: the HR pill showed 75-97
BPM with 60%+ confidence while the operator was across the room
with their back to the radar.
Add a presence gate based on the target gate's micromotion energy:
empty room peak_micro_mid 1k-3k
person nearby peak_micro_mid 10k-20k
person in beam peak_micro_mid 40k-80k
Threshold at 20k. Below it we null both BR and HR (the breathing
detector's internal buffer is still fed so it stays warm for instant
re-acquisition).
New diagnostic endpoint GET /api/v1/mmwave/gates dumps current
motion/micro arrays + the target gate so we can re-calibrate the
threshold on new firmware.
UI: pill now shows "· нет цели" (no target) when presence=false, so
the operator can tell "buffer warming up" from "nobody in beam"
from "module fell back to Normal Mode".
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
ADR-121 (Normal Mode) gave us distance and a passable breathing
estimate but couldn't see the heartbeat — cardiac chest displacement
(~0.5 mm) is well below the cm quantisation of `distance:NNN`.
Engineering Mode streams per-range-gate energy at the same 6 Hz
cadence (15 motion + 15 micromotion gates, u32 LE each). The
micromotion bin at the target's distance carries enough cardiac
modulation for FFT peak-detection in the 0.8-2.0 Hz band.
Live result, seated operator ~1.5 m from the radar:
🫁📡 13.0 BPM · 37% норма 12-20
💓📡 76 BPM · 63% норма 60-100
Implementation:
- Send enable-config → set-mode(0x04) → disable-config on startup;
fall back to Normal-Mode ASCII parsing if the sequence fails.
- Binary frame parser: F4 F3 F2 F1 | len(2) | 0x01 | dist(2) | 8z |
motion[15]×u32 LE | micro[15]×u32 LE | F8 F7 F6 F5. Gate the ASCII
line-drain on the engineering_mode flag — first cut ran both
unconditionally and destroyed 80% of partial frames mid-buffer.
- Target-gate selection: distance-bracketed gate first, mid-range
micro-peak fallback, gate 1 default. Per-gate ring buffer of
log-energies feeds a Hann + radix-2 FFT.
- /api/v1/mmwave/vitals now returns real `heart_rate_bpm`.
- raw.html: 💓📡 pill now shows real values (no more "n/a"
placeholder).
- New probe script v2/scripts/probe_ld2402_engineering.py used to
reverse-engineer the wire format; kept in tree for next time.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Previously only WiFi-CSI produced breathing/HR estimates. With the
HLK-LD2402 radar wired up we can compute a second, physically
independent breathing estimate from chest-induced cm flicker in the
distance time-series — a useful cross-check that catches the case
when one modality is blind (e.g. WiFi-CSI when nodes are offline,
or mmWave when nothing's in the radar's field of view).
mmwave.rs:
- Plumb a per-reading VitalSignDetector tuned for the module's 6 Hz
Normal-Mode cadence (Nyquist 3 Hz comfortably covers the 0.1-0.5
Hz breathing band).
- Distance (cm) feeds the detector as the "amplitude" channel;
phase is empty so heartbeat falls back to amplitude residual.
- Gate `current_vitals()` on data freshness so a disconnected radar
doesn't return stale cached BPMs.
main.rs:
- New GET /api/v1/mmwave/vitals returning the same shape as
/api/v1/vital-signs plus buffer status for UI warm-up feedback.
ui/raw.html:
- Each vital pill now shows both 📶 (WiFi-CSI) and 📡 (mmWave)
values side-by-side, separated by `|`. mmWave HR is labelled
"n/a" — cm precision at 6 Hz puts heartbeat below the noise
floor. Buffer fill (e.g. "120/180") shown while detector is
warming up so the operator knows BPM is on the way.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The breathing/HR pills carried raw BPM with no context. An operator
glancing at "94 BPM" can't tell if that's normal or tachycardia
without external reference.
Add inline "норма 12–20" / "норма 60–100" hints (dimmed so they
don't compete with the live value), and tint the number amber when
it falls outside the adult-at-rest range. Tooltip carries the
medical terminology (bradypnea/tachypnea, bradycardia/tachycardia).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
ADR-021 already publishes `vital_signs` inside SensingUpdate but the
raw calibration console had no readout — the operator had to curl
/api/v1/vital-signs to see breathing/HR. Add two pills (🫁 + 💓)
next to the mmWave one and update them on every WS tick.
Confidence < 20 % dims the pill so noise-floor estimates don't read
as real values. Missing/zero rates fall back to "— BPM".
Mirrored ui/raw.html → static/raw.html so both deployment paths
serve the same console.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds a hidden-by-default 📡 mmWave pill next to the global badge + CV
stat. Polls /api/v1/mmwave/latest at 5 Hz (~200 ms) — well above the
HLK-LD2402's 6 Hz native cadence so no information is lost. Pill shows:
📡 mmWave 152 cm · 60 ms
Distance + age (ms since last reading). Fades to 50% opacity when age
>1.5 s, hides entirely when the server reports `available: false`
(port absent or stale >2 s).
Synced both copies — ui/raw.html (deploy mirror) + static/raw.html
(canonical source referenced by ADR-104 / ADR-107).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds a dedicated blocking serial-reader thread that opens the
HLK-LD2402 over a CP2102 USB-UART bridge (default 115200 8N1),
parses ASCII `distance:<cm>\r\n` lines @ ~6 Hz, stores the latest
reading in a static OnceLock<Mutex<…>>, and exposes it via:
GET /api/v1/mmwave/latest →
{ "available": true, "distance_cm": 152, "age_ms": 90 }
{ "available": false } (port absent, stale > 2 s)
UI (Sensing tab) polls the endpoint every visible WS tick and
shows a new blue card "mmWave Radar (24 GHz)" with distance +
age bar. Card hides when unavailable.
CLI:
--mmwave-port /dev/cu.usbserial-1140
--mmwave-baud 115200 (default)
Both optional — server runs as before if the module is absent.
Open failure: single WARN log, reader thread exits, server keeps
serving WiFi sensing.
Verified live: distance 149-153 cm at ~6 Hz, REST returns fresh
readings with age_ms 55-127.
Out of scope (logged in ADR-121): Engineering Mode binary frames,
vitals cross-check vs ADR-021, W-MLP feature fusion, auto-reconnect.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- CHECKLIST.md: refresh head sha (12e1cf9d), date (2026-05-18),
count (47 → 50 Done), explicit Done entries for ADR-118/119/120
with the full session accuracy trajectory (40.4% → 90.40%).
- .gitignore: stop tracking deployment-specific training artifacts:
v2/data/recordings/ (175 MB each), v2/data/adaptive_model.json
(regenerated on each retrain), v2/data/baseline.json (regenerated
on /api/v1/baseline/calibrate).
- ui/style.css: ship the .sensing-class-label color rules for
present_moving (yellow), waving (purple), transition (orange) —
written during ADR-117 conversation but missed by that commit.
- git rm --cached v2/data/adaptive_model.json (stays on disk; untracked).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds diagnostic endpoint returning the last 30 RAW model labels,
their distribution, the smoother's internal buffer, committed +
candidate labels, and consecutive count. Lets the operator
distinguish "smoothing is sticky" from "model genuinely keeps
outputting the same class" — without that signal, tuning smoothing
parameters is shooting in the dark.
Also relaxes smoothing back to 15/2 (Layer-1 1.5s majority +
Layer-2 200ms confirm). The earlier 30/5 setting was over-damped
because the actual problem was model overfitting, not flicker.
Diagnostic finding on current live data:
transition raw count: 25/30 (83%)
present_still: 2
absent: 2
present_moving: 1
Model believes user is performing sit/stand transitions even when
they're typing at the keyboard. Likely cause: `train_transition`
recording captured ~3s pauses between sit-stand cycles, so the
class signature is broad enough to grab typing/mouse motion. Fix
is data-side (re-record cleaner transition class or add a desk_work
class), not algorithm-side. ADR-120 follow-up notes.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Previous smoothing covered only the adaptive_override path. The 5 other
classification.motion_level writes (amp_presence_override and
amp_classify_from_latest in 3 different tick handlers) wrote raw
values that bypassed the smoother entirely — explaining the lingering
"переключается со скоростью света" complaint after the two-layer fix.
New finalize_motion_label(&mut classification) runs at end-of-tick AFTER
all overrides have settled, applies the same two-layer (30-tick mode +
5-tick confirm) smoothing uniformly to whatever label survived the
priority cascade. Called from 3 sites:
- multi-BSSID tick handler
- feature_state tick handler
- per-node loop in broadcast tick task
adaptive_override now emits raw model label (no double-smoothing).
Verified: 30-second sample, user actively performing transitions,
ZERO flips. Label persisted as `transition` all 30 samples.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Previous 15-tick majority window still flickered visibly in the live
UI ("переключается со скоростью света"). Bump to a two-stage filter:
Layer 1: ADAPTIVE_SMOOTH_WIN = 30 (was 15)
Majority vote over last 3 seconds @ 10 Hz tick rate. Doubles the
window — sustained signal dominates, brief glitches lose.
Layer 2: ADAPTIVE_CONFIRM_TICKS = 5 (new)
Even when Layer-1 mode flips, the committed displayed label only
updates after the new mode persists for 5 consecutive mode-results
(~500ms). Stops rapid bouncing between near-tied classes.
Effective dwell time: ≥3 seconds before any visible label change.
Live test (30s sample, user actively waving): label locked to
`waving` for 20 consecutive samples after a 10s warmup. No flicker.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
After hybrid priority fix (442c03da) the W-MLP labels reach the live UI
but at ~10 Hz tick rate they flip between adjacent classes (transition /
present_still / present_moving) too fast to read. Adds majority-vote
smoothing over last 7 ticks (~700ms window) — snappy enough for real-
time feedback, stable enough that the displayed label persists long
enough to be readable.
Implementation: static ADAPTIVE_LABEL_HISTORY VecDeque + helper
adaptive_label_smooth() called at end of adaptive_override after the
model emits its raw decision. Mode of last 7 raw labels wins; ties
break sticky to the previous committed label.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
W-MLP claimed 90.4% training accuracy in ADR-120 but live UI kept
showing only the 4 baseline classes (absent/still/moving/active).
Root cause: 3 amp_presence_override / amp_classify_from_latest call
sites ALWAYS overwrite classification.motion_level after
adaptive_override runs, regardless of what the model decided. The
rule-based path only knows 4 classes; the 2 new ones (waving,
transition) emitted by the adaptive W-MLP were silently clobbered
every tick.
Hybrid priority:
rule-based wins → absent / present_still / present_moving / active
(ESPectre-style F1>96%, battle-tested)
adaptive wins → waving / transition (exclusive to ADR-120 W-MLP)
Implementation: new helper adaptive_owns_class() + ADAPTIVE_EXCLUSIVE_CLASSES
constant. Each of the 3 rule-based override blocks (multi-BSSID tick,
feature_state path, per-node loop) now guards on `if !adaptive_owns_class(
classification.motion_level)`. Skips the overwrite when the adaptive
model has just emitted a new class.
Live verification (30s sample):
transition: 14/30 (47%) — visible in live UI for the first time
present_still: 10/30 (33%)
present_moving: 1/30
absent: 1/30
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Audit on 6-node training data (151,329 frames) found 21 multicollinear
pairs (|r|>0.85), one dead feature (amp_min constant 0), and only node[0]
used in 8 of 15 features. Top per-feature F-stat = 15,497 but accuracy
stuck at 44.4% — classifier couldn't extract the signal that physical
sensors were already capturing.
Refactor:
- Drop 8 dead/redundant features (amp_min, amp_range, breath_bp,
spec_pow, motion_bp, amp_mean, amp_max, amp_iqr, amp_kurt).
- Keep 4 globals: variance, mean_rssi, dom_hz, change_pts.
- Add per-node features × all 6 nodes: amp_std, amp_skew, amp_entropy.
- New N_FEATURES = 22 (was 15). Z-score normalisation kept.
API change: features_from_runtime now takes &[(u8, &[f64])] — caller
must supply per-node amplitudes. New helper current_per_node_amps()
reads AMP_HIST.nbvi_history.back() for all live nodes.
Old data/adaptive_model.json removed (incompatible 15-feature schema).
Retrain result on same 151k frames:
44.4% → 49.58% accuracy (+5.2 pts)
Total improvement vs 2-node baseline (40.4%): +9.2 pts.
Live confidence distribution now meaningful (0.30-0.85) vs pre-fix
near-uniform 0.04-0.10. Sensor placement matters: n6 (near door, far
from AP) sep_ratio=0.60 best; n1/n5 (near AP) ~0.01-0.06 nearly dead.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Audit fix bundle (10 areas; details in ADR-117 + commit body below).
Server (main.rs / wiflow_v1.rs):
- UDP receiver filters loopback/multicast/unspecified before NODE_ADDRS
registration. Defends against `cargo test` cross-talk that spawned
250+ ping zombies on the production server's :5005 port.
- csi_keepalive_task pre-reaps `/sbin/ping -i 0.040` orphans at task
entry. macOS doesn't propagate parent death, so killed servers used
to leave init-parented pings running indefinitely.
- run_wiflow_inference stamps real classifier confidence onto every
keypoint (was hardcoded 1.0) — reads 0.037 on live data, honest.
- run_wiflow_inference clones only the tail-20 frames inside the lock,
not the full 600-deep VecDeque (~270 KB → ~9 KB per tick).
- wiflow_v1::build_input_from_history: zero-pad dead channel slots
instead of duplicating subcarrier 0 across all of them. Comment said
"zero the rest", prior code did the opposite.
- GET / now 308-redirects to /ui/index.html; API index moved to /api.
UI (ui/index.html, ui/components/LiveDemoTab.js):
- <section id="sensing"> gets a <div id="sensing-container"> child so
app.js::SensingTab.mount has its mount point. Sensing tab was
permanently blank.
- LiveDemoTab.fetchModels: only inject WiFlow into the dropdown if no
RVF model is already active. Prevents silent flip back to WiFlow
after every poll.
Tests (multi_node_test.rs):
- test_multi_node_udp_send probes 127.0.0.1:5005 first; if bind fails
(e.g. a dev server is running), skip the send. Two-layer defense
with the server-side filter above.
Docs (CHECKLIST.md, ADR-115, espectre-gap-analysis.md, ota-pipeline.md):
- CHECKLIST head sha + count refreshed (43→47 Done, head 0ec1e4b0,
ADR range to 001-117 with ADR-111 noted as intentionally absent).
- ADR-115 typo fixes: "ADR-100" → "ADR-110" (TP-Link WISP),
"ADR-111" → "ADR-109" (AP-MAC tracking actually lives there).
- gap-analysis "Still open" table: 8 shipped items annotated with
commit hashes; remainder reclassified Deferred with reason.
- ota-pipeline.md: new "Operator REST endpoints" section listing
/ota/recalibrate (ADR-109) and /ota/set-target (ADR-115) with
unauthed + bearer-token curl examples.
Verified post-restart:
- exactly 2 ping children, both parented to current PID, one per real
sensor IP, no 127.0.0.1.
- GET / → 308 → /ui/index.html.
- /api/v1/info: pose_estimation=true, version 0.3.0.
- /api/v1/pose/current: 17 COCO keypoints, confidence 0.037 (real).
- cargo test --workspace: 13 passed / 0 failed / 5 ignored.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
1000 idle + 1000 motion synthetic-but-parameter-matched CSI
frames live under tests/fixtures/replay_*.jsonl; the cargo test
`replay_2000_packets_f1_above_threshold` replays each through
amp_presence_override and asserts F1 ≥ 0.85.
Fixtures generated by scripts/generate-replay-fixtures.py (seeded
42/43). Parameters mirror data/baseline.json: per-node baseline
mean from live recording, idle σ=1.8 % per-frame noise, motion
±40 % envelope at 0.15 Hz (long enough to swing the classifier's
4.5 s rolling CV) plus 5 % per-frame noise.
Current run: F1 = 1.000 (tp=822, fp=0, tn=822, fn=0; 178 warmup
frames per fixture excluded). 0.85 threshold leaves headroom for
classifier evolution.
Test resets per-node history + per-sub baseline between fixtures
so each run is hermetic; keeps the per-node baseline-CV so the
ADR-103 universal-threshold path stays exercised.
Co-Authored-By: claude-flow <ruv@ruv.net>
--baseline-profile {single,auto,day,night} (default single).
* single — legacy data/baseline.json path, unchanged.
* auto — picks data/baseline.{day,night}.json by local hour
(day=07:00-20:59), hot-swaps every 5 min on transitions.
* day/night — force one of the profile files, no switching.
Missing profile files fall back to data/baseline.json with a
warning, so migration is incremental — operator can record one
profile at a time without breaking the deployment.
Watch task is a no-op outside `auto` (no log noise, no tokio slot).
Smoke: --baseline-profile auto with no day.json → "falling back
to data/baseline.json" warning then normal startup; watch task
enabled.
Co-Authored-By: claude-flow <ruv@ruv.net>
scripts/record-baseline.py and capture_baseline_to_disk now
compute per-subcarrier circular mean + variance of phases when the
WS stream carries them (ADR-106). Saved as per_subcarrier_phase_mean
+ per_subcarrier_phase_var in baseline.json.
Server loads them into PHASE_BASELINE_PER_SUB; phase_drift_update
computes a per-tick score (mean circular distance / π over
subcarriers with baseline variance < 0.30) and stores it in
PHASE_DRIFT. Surfaces as PerNodeFeatureInfo.phase_drift_score
(skip-if-none). Honesty contract: emits None below
PHASE_DRIFT_MIN_USABLE = 16 usable subcarriers.
Legacy baselines without phase fields fall back to amplitude-only
behaviour with no change.
Co-Authored-By: claude-flow <ruv@ruv.net>
Replaces the text-pill status with a 140×14 px progress bar that
fills from 0 → 99% over CALIB_DURATION_SEC (90s default). On
complete it flashes to 100% with "done" label, then hides itself
after 3s; on error it surfaces a text pill so failure modes stay
visible.
Closes the last Open Item in ADR-107.
Co-Authored-By: claude-flow <ruv@ruv.net>
signal_field_from_multistatic renders a 20×20 floor-plan heatmap by
overlaying isotropic Gaussians at each ESP32 node's configured 3D
position, scaled by cv²(fused_amplitude) × cross_node_coherence.
Replaces ADR-105 D6's zero grid only when ≥2 nodes are active AND
positions are configured (--node-positions); else preserves the zero
grid (ADR-105 honesty contract).
Honestly framed as a coverage × activity map, not a target-position
estimate — commodity ESP32s have no phase-coherent ranging.
Verified end-to-end: 320/400 cells non-zero with two live sensors
at (1.5,2,1) and (-1.5,2,-1), all-zero on single sensor / no-position
deployments. cargo test --workspace passes (313 tests).
Co-Authored-By: claude-flow <ruv@ruv.net>
Three related ADR-104 follow-ups:
1. Expose per-node drift_score on PerNodeFeatureInfo (skip-if-none
so legacy v1 baseline.json — no per_subcarrier_mean — emits
nothing instead of misleading 0.0).
2. raw.html drift sparkline below the RSSI/broadband trace, fixed
Y range [0, 0.30] with dashed presence (0.10) + warning (0.15)
thresholds so operators can read off-axis presence across nodes
without re-scaling. Stat pill "drift" shows the live numeric.
3. baseline_staleness_watch background task: when the on-disk
baseline is older than --baseline-stale-age-sec (default 4 h)
AND drift > 1.5× presence threshold for ≥3 consecutive 5-min
ticks while the classifier reports `absent`, logs a warning
suggesting recalibration. Rate-limited via
--baseline-stale-warn-cooldown-sec (default 1 h). Independent
from auto-recalibrate: that one needs a quiet room; this one
fires when the operator is *in* the room while the channel
itself has physically shifted (AP moved, furniture, etc.).
Co-Authored-By: claude-flow <ruv@ruv.net>
Uniform u8 field on both enhanced_* JSON objects so downstream
consumers can decide whether to trust a multi-AP enhancement
that, on a single sensor, may have run with only 1 AP. Mirrors
the existing contributing_bssids / bssid_count counts under a
single name across motion and breathing.
Co-Authored-By: claude-flow <ruv@ruv.net>
ADR-102 Step 3 (FP-rate validation) — `nbvi_select_top_k` no longer
takes the literal top-K. Evaluates candidate K ∈ {6,8,10,12,16,20}
over the quiet window: for each, computes per-subset broadband CV
on a sliding sub-window and counts how many sub-windows cross the
moving threshold (0.10). Picks smallest K with fewest "false
positives" (ties broken by smallest total-NBVI). Defends against
the rare case where the literal top-12 happens to include a
subcarrier overlapping a noise source — the FP count surfaces it
and a tighter K wins.
ADR-104 (off-axis presence via per-subcarrier drift) — when
baseline.json carries `per_subcarrier_mean` for a node, server
loads the vector into AMP_BASELINE_PER_SUB. Each classifier tick
computes `drift = mean |Δ amp / baseline|` over the recent
AMP_SHORT_WIN frames vs that baseline. Drift ≥ 10 % → trigger
`present_still` even if broadband mean barely shifted. Catches the
case where the operator is in the room but off the AP→sensor line,
so individual subcarriers are perturbed without a global drop.
amp_node_level / amp_node_snapshot — per-node drift trigger
amp_classify_from_latest — cross-node MAX drift trigger
Drift channel is opportunistic: if baseline.json predates ADR-104
(no per_subcarrier_mean field), drift = 0 and classifier behaves
exactly as before. Re-record baseline via the calibrate-empty button
to populate the field and activate the channel.
Closes ADR-106 open item #1: server now receives the real WiFi RX
timestamp from the sensor's hardware controller instead of stamping
on receipt with SystemTime.
FW (csi_collector.c csi_serialize_frame):
Append uint32_t = info->rx_ctrl.timestamp (µs since FW boot,
monotonic per ESP-IDF docs) as 4 trailing bytes after I/Q data.
Header layout unchanged → old server parsers still work (they
ignore tail bytes per existing `if buf.len() >= expected` check).
Server (parse_esp32_frame):
Opportunistically read trailing 4 bytes as u32 LE into
Esp32Frame.sensor_timestamp_us. Old FW → None, new FW → Some(µs).
udp_receiver_task uses sensor timestamp when present, falls back
to server SystemTime if not. Result published as NodeInfo.timestamp_us.
Flashed both sensors via OTA (no USB dance):
192.168.0.101: ota_0 → ota_1 ✓
192.168.0.100: ota_1 → ota_0 ✓
Live verify: WS timestamps now sub-1e12 (sensor monotonic, ~39s
after FW boot), Δ between successive frames = 43.3 ms ≈ 23 fps
sampling jitter, sub-ms precision. Cross-node skew = sensor boot
time delta (here ~292 ms). For sync the host can subtract per-node
boot offset learned from the first packet pair.
UI side of ADR-107: green "calibrate empty" button in raw.html next
to the existing reset/log-y controls. Click → confirm dialog tells
the operator to step out → POST /api/v1/baseline/calibrate with
90 s capture window → polls GET /api/v1/baseline every 2 s, surfaces
"recording… N/90 s" then "baseline updated ✓".
ADR-107 documents:
D1 in-process capture_baseline_to_disk (port of record-baseline.py)
D2 BASELINE_BUS broadcast forwarder so capture stays decoupled from
WS clients
D3 POST /api/v1/baseline/calibrate (immediate ack, background work)
D4 GET /api/v1/baseline (current state + cooldown + status)
D5 auto_recalibrate_task — 30-min absent+low-CV trigger, 1-h cooldown
D6 raw.html button + polling
Eliminates the manual `scripts/record-baseline.py` ritual:
REST endpoints
GET /api/v1/baseline — current per-node baseline +
last_written_sec_ago + calibration_status
POST /api/v1/baseline/calibrate — start a background capture, optional
JSON body { duration_sec, trim_sec,
clean_window_sec, out }. Returns
immediately; status transitions
idle → running → complete | error: ...
Auto-recalibrate background task
Watches the live classifier. When motion_level=="absent" and CV<0.08 for
--auto-recalibrate-quiet-sec (default 1800 = 30 min) AND the last write
is older than --auto-recalibrate-min-age-sec (default 3600 = 1h),
silently re-runs the capture and live-reloads the override map. No
operator action needed.
Implementation
capture_baseline_to_disk() — in-process port of record-baseline.py:
trim head/tail, scan windows for lowest-
CV chunk, compute full-broadband stats,
write baseline.json, hot-reload override.
BASELINE_BUS — broadcast bus carrying every sensing_update
JSON so the capture can read live frames
without re-binding any sockets.
BASELINE_LAST_WRITTEN — SystemTime tracker for the cool-down.
BASELINE_CALIBRATION_STATUS — status string for the REST endpoint.
Verified live: POST /api/v1/baseline/calibrate (5 s test window) ->
capture wrote `/tmp/test_baseline.json` with n_samples=86 per node,
override hot-reloaded (visible via GET /api/v1/baseline). Real baseline
restored on next server restart from data/baseline.json.
Closes the first ADR-106 open item without an FW change. On every
raw-CSI frame we now stamp `ns.latest_timestamp_us` with
SystemTime::now() in µs since UNIX epoch. NodeInfo.timestamp_us
surfaces it on WS via the already-wired skip_serializing_if guard.
Accuracy is wall-clock + Mac monotonic + LAN jitter ≈ ~1 ms. Verified
cross-node skew ts(node1) - ts(node2) = 1556 µs in a single test, well
within the 5-10 ms tolerance needed for FFT-based vital-signs
correlation across sensors.
Sensor-side ESP-IDF rx_ctrl.timestamp (true RX-time µs) is still
better and remains on the open list for a future FW header bump
(reserved bytes [18..19] are only 2 of the 4 we'd need — header
extension required, opt-in via new magic).
Continuation of ADR-106 (max raw signal off sensors).
Operator was running `ping -i 0.05 192.168.0.101 &` by hand to keep CSI
callbacks firing on the sensors. Server now does this itself:
* Track per-node source addresses in NODE_ADDRS, populated on every
recv_from via a cheap magic-byte peek (works for 0xC5110001 raw,
0xC5110002 vitals, 0xC5110006 feature_state).
* csi_keepalive_task spawns one `ping -i <interval> <ip>` child per
discovered sensor, re-spawns if the child dies or the sensor IP
changes. Default 25 pkt/s via --csi-keepalive-pps; 0 disables.
Why ICMP, not UDP: tried a UDP-based keepalive (send tiny UDP packet
to sensor's known src port). Sensor's closed-port UDP rejected before
the CSI callback fired on its side. ICMP echo gets handled in the
WiFi stack regardless of any user-space listener so CSI fires reliably.
Verified live, no external `ping` running:
keepalive: ping -i 0.040 192.168.0.101 for node 1
node 1: 55.6 Hz raw CSI (amp+phase populated)
node 2: 55.6 Hz raw CSI (amp+phase populated)
Combined with ADR-106 NodeInfo fields (phases, noise_floor_dbm,
n_antennas, timestamp_us) this gives downstream consumers — UI,
classifier, future ML model — the full complex CSI signal at high
rate without any operator-side ritual.
Operator asked for maximum raw signal off the sensors so a future
trained pose / fine-motion model has everything it needs, instead of
only the amplitude scalar we surfaced before. Adds four fields to
NodeInfo:
phases: Vec<f64> per-subcarrier atan2(Q,I), radians
n_antennas: u8 RX antenna count from WiFi driver
noise_floor_dbm: i8 noise floor reported by ESP-IDF
timestamp_us: u64 per-frame µs timestamp from the sensor
Each is `skip_serializing_if = zero-or-empty` so feature_state ticks
(which carry no raw CSI) stay slim in the WS payload — only real raw
CSI frames populate them.
NodeState gains: latest_phases / latest_noise_floor /
latest_n_antennas / latest_timestamp_us (per-node stash, replaces
having to keep a parallel phase_history). The raw-CSI ingest path
populates these on every frame.
Verified live: WS now emits 185 messages over 4 s (~46 fps) with
both amplitude[56] and phases[56] populated; noise_floor reports -91
dBm; n_antennas reports 1 (ESP32-S3 single antenna).
Continuation of ADR-105 (no synthetic outputs in production runtime).
The 20×20 SignalField heatmap was generated by mapping subcarrier
index k to angle 2π·k/N and dropping a Gaussian hotspot — a totally
fabricated spatial layout. A single sensor has no directional info
so the resulting heatmap had no correspondence to where anything
actually was in the room; UI showed believable-looking but
physically meaningless hotspots. Operator asked for boots-on-the-
ground honesty.
`generate_signal_field` now returns a zero-filled 20×1×20 grid. UI
renders blank, which is the truthful state until a real multistatic
localizer is wired (multi-AP attention from ADR-008 or the
`MultistaticFuser` already in code).
Audit of remaining fields confirmed they are either:
- already gated on real data (vital_signs returns None when br < 1 BPM,
persons/pose_keypoints/posture/signal_quality_score all None without
model loaded),
- or processed from real CSI (classification, features.mean_rssi,
features.variance, enhanced_motion when multi-AP pipeline active).
`--source simulate` was already disabled by an earlier change
(exit code 2). `--pretrain` and `--train` synthetic fallbacks remain
in code as developer tools but never touch the runtime sensing path.
Operator inspected the rich Docker UI tied to our backend and noticed
the dashboard showed a 17-keypoint skeleton even with no DensePose
model loaded. Tracing it: `derive_pose_from_sensing` synthesized
geometric placeholders, `pose_stats.average_confidence` was hard-coded
0.87, `pose_zones_summary` invented zones 2/3/4 as "clear", and
`/api/v1/info.features.pose_estimation` claimed `true` regardless.
All cosmetic noise that hid the real capability gap.
Changes:
* `derive_pose_from_sensing` is now an inert `Vec::new()` stub.
Heuristic logic kept in `derive_single_person_pose` (dead-code-warned
out by the rustc unused-fn lint) for the day someone wires a real
trained pose model in.
* `pose_current` returns persons only when `model_loaded == true`; the
endpoint always includes `model_loaded` so the UI can decide what
to render.
* `pose_stats` drops the fake `average_confidence: 0.87`.
* `pose_zones_summary` reports `zones_configured: 0` and an empty
`zones {}` instead of fabricating four zones.
* `api_info.features.pose_estimation` now mirrors `s.model_loaded`.
Sensing endpoints (`/api/v1/sensing/latest`, `/ws/sensing`) are
unchanged — they always carried real ESP32-derived data per ADR-101.
Pace's Problem #3 ("threshold=1.0 means different things on different
devices") solved by normalizing the runtime CV against the empty-room
baseline CV measured during calibration.
norm_cv = current_cv / baseline_cv
gates: norm_cv ≥ 3.0 → present_moving
norm_cv ≥ 6.0 → active
Baseline CV loaded per-node from data/baseline.json (full_broadband_cv_pct).
When no calibration loaded, falls back to absolute gates (0.10 / 0.22)
that were deployment-tuned earlier — keeps backwards compatibility.
Both per-node `amp_node_level` and global `amp_classify_from_latest` use
the same normalization. On the operator's deployment with baseline CV
~4 %, the universal 3×/6× gates map to ~12 %/24 % absolute — same numbers
the hard-coded thresholds had, but now any-room-portable.
Problem from ADR-103 v1: persisted NBVI-subset mean (19.86 in operator's
recording) drifted out of comparability after server restart because
NBVI re-selected a different top-12 subset, yielding a different mean
from the same channel. classifier saw current/baseline ratio > 1 even
in clearly empty room.
Fix:
1. Separate FULL-broadband mean (all non-zero subcarriers) from
NBVI-subset mean in amp_presence_override. NBVI subset still drives
CV / motion sensitivity. FULL is what gets compared to the
persistent baseline — stable across NBVI re-selection.
2. baseline.json schema v2: full_broadband_{mean,p50,p95,std,cv_pct}
replaces NBVI-only p95_amp/mean_amp. Loader prefers full_*; falls
back to legacy fields for backward compat.
3. NBVI Step 1 quiet-window finder (ESPectre): nbvi_select_top_k now
slides a window across the calibration history, picks the lowest-CV
sub-window, and ranks subcarriers using only that. Robust to brief
motion during the calibration buffer.
4. scripts/record-baseline.py v2: emits v2 schema, computes
full-broadband stats per node, trims head/tail transients, picks
cleanest 30-s sub-window, also saves per_subcarrier_mean for future
subcarrier-level comparison.
Operator workflow now: step out → run script → restart server →
forget about the empty-room ritual forever.
* docs/references/espectre-techniques.md — catalogues every Pace
technique from Part-2 against what RuView has implemented, doesn't
have, or has differently. Includes ranked open-items list.
* sensing-server: revert feature_state path to vec![] amplitudes.
The previous fix made bars LOOK live by reissuing the last raw-CSI
vector on every feature_state tick — operator reported this made
the bars misleading (visually busy but unresponsive to movement).
raw.html already skips empty-amp updates so bars now refresh only
on actual fresh CSI, which is honest.
* raw.html: comment on the skip-empty branch for future-me.
Operator request: only one UI page open. raw.html (ADR-099 console,
extended in ADR-101 with per-node classification badges) covers all
live-debug use cases. mobile.html / spectrum.html / calibrate.html
were either superseded or never adopted in the field — removing them
reduces the surface that has to track ADR-101/102 contract changes.
raw.html stays at /static/raw.html on the existing :8080 listener.
After 3393c1e8 made FW emit ~80 % feature_state packets and ~20 % raw
CSI, the server's feature_state path was overwriting NodeInfo.amplitude
with vec![] on every feature_state tick. raw.html's per-node bar chart
ended up freezing for hundreds of milliseconds between rare raw-CSI
packets, and /api/v1/sensing/latest mostly snapshotted an empty amps
vector even though raw CSI was flowing.
Fix: in the feature_state SensingUpdate builder, hand out
ns.frame_history.back() (the last raw amps vector that the raw-CSI
path pushed) instead of an empty Vec. Bars now refresh on every WS
update (verified: 100/100 updates carry amps in a 4-s sample, was
~20/100 before the patch).
Classifier behaviour unchanged — amp_presence_override still runs only
when actual raw CSI arrives; this only affects what the UI displays.
Ports Pace's NBVI = α·(σ/μ²) + (1-α)·(σ/μ) (α=0.5) into the
amp_presence_override classifier. Per node, accumulates a 30-second
ring of full amplitude vectors, every ~5 s ranks the subcarriers,
picks top-12 by lowest NBVI, then computes broadband mean and CV ONLY
on that subset instead of all 56 subcarriers.
Live impact on the operator's deployment (idle room, 2 pps ping):
node 1 CV: 5% -> 3.1% (-38 %)
node 2 CV: 7% -> 3.9% (-44 %)
Thresholds tightened proportionally to match the new baseline:
active: 30 % -> 22 %
present_moving: 15 % -> 10 %
This lets the detector catch subtler motion (e.g. waving while seated)
without raising the false-positive rate above what we had before.
Implemented entirely server-side — no firmware change, no second
flash cycle. Algorithm parameters in const block for easy retuning.
* nodes[].rssi_dbm of 0 used to display literally as "0.0 dBm",
misleading the operator when rssi_history was empty on the first
few ticks. Now coerce to "--" and skip pushing zeros to the trace.
* per-node fps was 1/dt instantaneous, blown up to 235 by multiple
SensingUpdate emit paths firing back-to-back. Replaced with a
1-second windowed counter — now matches the real ~38 fps per node.
scripts/ota-deploy.sh
Python 3 helper (the earlier bash version tripped over macOS bash 3.2's
missing associative arrays). One invocation with no arguments:
1. discovers nodes in the local /24 via ARP + /ota/status:8032 probe;
2. POSTs the firmware blob to every node in parallel;
3. waits for reboot, polls /ota/status until running_partition flips,
and fails-loud if any node stays on the old partition (typical
symptom of a panic on first boot from the new slot).
Supports `--build` (idf.py build first), `--no-verify`, explicit IP
list, and OTA_PSK=<token> for the ADR-050 Bearer auth path.
Measured cycle: ~25 s end-to-end for both room01 + room02.
static/mobile.html
Mobile-first sibling of static/raw.html. The desktop page is unreadable
on a 360-420 px screen — bars chart fights the narrow viewport, 11-12 px
font, controls overlap the badge. The mobile page:
- sticky global badge (30 px) + connection pill + reset (44 px tap);
- per-node card with 22 px node badge, 18 px stat tiles, 90 px trace;
- drops the bars chart (useless under 600 px wide);
- viewport-fit=cover, theme-color, apple-mobile-web-app meta tags;
- high-contrast palette tuned for outdoor light;
- reuses the /ws/sensing contract verbatim — anything that lights up
raw.html lights this up too.
main.rs ServeDir route
Adds `.nest_service("/static", ServeDir::new(.../static))` so
raw.html / mobile.html / calibrate.html / spectrum.html are served on
the main 8080 port. Previously they needed a separate
`python -m http.server :8091`, which the operator had to remember to
start by hand on every deploy. Now there's exactly one URL per device.
Reachable from a phone on the LAN:
http://<mac>:8080/static/mobile.html
http://<mac>:8080/static/raw.html
Co-Authored-By: claude-flow <ruv@ruv.net>
Two server-side parsers (csi.rs::parse_esp32_frame and the duplicate in
main.rs) read every field after `n_antennas` from offsets shifted by 2
bytes — n_subcarriers as u8 instead of u16, sequence at 10..14 instead of
12..16, rssi at 14 instead of 16. The saturating_neg() workaround hid the
bug by always forcing a negative dBm value, so the trace looked plausible
but was actually a slice of mid-sequence number. ADR-100 D3 documented
this as an open item; this commit closes it.
Adds two regression tests in csi.rs (header-offset round-trip with
distinctive values per field, plus 20-byte boundary case) so the layout
contract can't drift again without CI catching it.
Even with both parsers correct, RSSI never reached the UI because the
firmware now ships only rv_feature_state_t (0xC5110006) — raw CSI
(0xC5110001) is no longer hot. rv_feature_state had no RSSI field;
both parsers fell back to rssi: -50 hardcode.
To fix without a protocol bump: repurpose the first byte of the trailing
`reserved` field (offset 54) as `int8_t rssi_dbm`. Firmware fills it from
radio_ops::get_health()::rssi_median_dbm in emit_feature_state. Server
reads buf[54] as i8; 0 means "not measured yet" → keeps the historical
-50 fallback for backward compat with pre-update nodes.
Verified live on TP-Link WISP (192.168.0.100/101):
node 1: -54 dBm node 2: -63 dBm (was plateau -50.0 fallback)
Co-Authored-By: claude-flow <ruv@ruv.net>
Surfaces the raw-amplitude classifier's per-node decision in
node_features[].classification so the UI can show which sensor is
actually seeing motion at any moment. Lets the operator visually find
the best sensor placement without physically moving things — just walk
around and watch which badge lights up.
Server side: adds amp_node_level() pure helper + amp_node_snapshot()
that reads AMP_LATEST, then plugs it into build_node_features so the
existing PerNodeFeatureInfo.classification carries the new labels.
UI: adds a global badge in the top bar and a per-node badge inline in
each h2, color-coded (grey/absent, blue/present_still, green/moving,
red/active) plus the live per-node CV %.
After ADR-100 gain-lock reveals a clean baseline, the broadband CV of
mean amplitude separates EMPTY/STILL/WALK by 3-6× on the operator's
deployment where RSSI MAD-Δ overlapped within noise. Adds:
amp_presence_override(node_id, amps) — per-frame: rolling 4.5 s
short window for CV, 60 s long window for 95th-percentile baseline,
cross-node fusion (MAX CV gate, ANY baseline-drop → still),
3 s motion hysteresis to bridge step pauses.
amp_classify_from_latest() — readonly fusion for feature_state
(0xC5110006) and adaptive-model paths that don't carry raw amps.
Wired into the three SensingUpdate-producing paths (raw CSI,
feature_state, adaptive model). Marks rssi_presence_override as
dead_code, kept for reference.
Live test (10 samples @ 3 s):
walk: present_moving, CV 41-53 %, sustained through pauses
stop: absent (CV 4-8 %) after 3 s hold expires
Ports Francesco Pace's ESPectre gain-lock (GPLv3) to RuView FW: medians
AGC and FFT scale over the first 300 packets after boot, then freezes
them via phy_force_rx_gain / phy_fft_scale_force. With both sensors
locked and proper AP→body→sensor geometry, a 30-s × 3-state capture
(empty / still / walk) now separates by ×3.4–×5.9 instead of ±0.02
within ±0.10 noise as in ADR-099.
Adds static/raw.html — per-node 56-subcarrier amplitude bars + RSSI/
broadband traces, no DSP, for live calibration.
ADR-100 documents the technique, boot calibration values for the
operator's deployment (AGC=42/44, both APPLIED), and the verified
three-state separation table.
Operator's household environment showed CSI-variance presence detection
failing — empty room produced HIGHER variance than an occupied room because
ambient WiFi noise (neighbour APs, retransmits, BT-coex) dominated the
broadband-variance signal at multi-meter range.
Deployed a TP-Link TL-WR841N in WISP mode as a dedicated isolated AP for
the sensors:
* Sensors associate only with TP-Link_8340 (clean channel)
* TP-Link bridges to the household AP, NAT-forwards sensor UDP to the Mac
* Mac keeps its primary household-AP association — no LAN reconfig needed
* Empty-room variance dropped 50.7 → 35.8 (-30%)
Replaced presence classification with RSSI MAD-Δ override:
* Per-node rolling 120-sample (~10 s @ 12 Hz) window of frame RSSI
* Metric: mean(|Δrssi|) between consecutive frames — robust to int8
quantisation jitter
* Thresholds tuned for the operator's geometry:
d < 0.20 → absent
< 0.55 → present_still
< 1.10 → present_moving
>= 1.10 → active
* Confidence field temporarily carries raw d for in-field threshold tuning
* CSI-based features (variance, motion_band_power, spectral_power) remain
in features.* for vital-sign signal-quality and multi-node fusion paths
UI / tooling:
* New static/spectrum.html — live signal console: combined classification,
all host-computed features (variance, motion_band, spectral, breathing
band, RSSI, dominant_freq, change_points), per-node FW signals, and a
60-second variance trace. Served via `python -m http.server 8091`.
* static/calibrate.html — simpler per-node motion/presence/RSSI bars
with peak-hold.
Desktop UI / discovery hardening (rolled in here because they came up
during this debug session):
* commands/discovery.rs: HTTP sweep limited to 2..=60 hosts (was 1..=254),
mDNS + UDP-broadcast paths disabled (current RuView FW doesn't advertise
them and they were burning CPU every poll cycle). Per-request timeout
set to 1500 ms with overall budget enforced via tokio::time::timeout +
futures::join_all (replaces the previous sequential select loop that
blocked on slow IPs).
* ui/hooks/useNodes.ts: poll interval 10 s → 30 s.
* ui/pages/Dashboard.tsx + NetworkDiscovery.tsx: merge new scan results
into existing list instead of replacing — discovery races sometimes miss
a node that was found a moment ago.
Firmware tuning:
* edge_processing.c: broadband-variance divisor /3.0 → /30.0 → /5.0
iterated; final /5.0 chosen for multi-meter geometry (sensor 1-3 m
from activity zone). DEBUG_MOTION_DSP scaffolding removed.
* csi_collector.c: CSI_MIN_SEND_INTERVAL_US 20 ms → 4 ms so the host can
see every available frame (real ceiling is the WiFi CSI callback rate).
Documentation:
* docs/adr/ADR-099 — full forensic write-up: measurement tables for sit/
walk/empty, the RSSI-Δ rationale, the WISP setup procedure, calibration
protocol for new deployments, and open items.
Verified end-to-end on hardware (sensors at 192.168.1.17/.19 → TP-Link at
192.168.1.14 → Mac at 192.168.1.21):
* UDP/5006 packets arrive ~12 Hz combined from both nodes
* Empty-room baseline d ≈ 0.49 measured (next: capture sit + walk to
finalize thresholds)
* Vital signs continue to populate (breathing 9–11 BPM stable)
* Two consecutive OTA round-trips remain functional after the change
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
End-to-end deployment fixes that took the two ESP32-S3 sensor boards
(room01, room02) from "boots but DSP frozen, OTA always rolls back" to
"motion/presence/breathing all live, two consecutive OTA round-trips
succeed". Full forensic write-up in docs/adr/ADR-098.
Firmware (firmware/esp32-csi-node/main/):
* csi_collector.c — remove esp_wifi_set_promiscuous(true): this call
silenced the CSI RX callback entirely on this silicon revision
(yield=0pps). Without it, callbacks resume at ~5-10 pps.
* edge_processing.c — root cause: incoming CSI frames carry 192
subcarriers but EDGE_MAX_SUBCARRIERS=128, so the size check
early-returned every frame and Step 8 (motion) never ran. Truncate
to 128 + warn once instead of returning.
* edge_processing.c — replace per-bin unwrapped-phase variance with
temporal variance of per-frame broadband mean amplitude. Empirical
separation on deployed hardware: empty 0.07-0.10, walking 3.5-14
(~44x). Scaled by /3.0 and clamped to [0,1].
* edge_processing.c — biquad fs 20.0 -> 10.0, matching the actual
callback rate (was halving the breathing passband).
* ota_update.c — OTA_WITH_SEQUENTIAL_WRITES -> OTA_SIZE_UNKNOWN to
erase the full target partition (stale tail of the previous larger
image was crashing the new image on boot, looking like rollback).
* ota_update.c — httpd_config_t.stack_size = 8192 (default 4 KB
overflowed in OTA verify path).
* main.c — log esp_reset_reason() and running_partition->label once
at app_main start, so OTA outcomes are visible without guesswork.
* sdkconfig.defaults — local deployment defaults: tier=2, display
disabled (no expander on these boards), 8192 timer stack.
Sensing server (v2/crates/wifi-densepose-sensing-server/):
* src/main.rs — parse_rv_feature_state() for the 0xC5110006
feature_state packet that RuView FW emits by default; this format
was previously unhandled. Wire ahead of parse_esp32_vitals.
* src/main.rs — BaselineTracker with hysteretic motion gating on top
of FW-reported scores, so UI sees clean boolean presence transitions.
* src/main.rs — refuse --source simulate; remove auto-fallback to
synthetic data. Production builds never run on fake signals.
* src/main.rs/csi.rs — parse_csi_lean() for legacy FW 5.47 CSV
packets; defence-in-depth for mistakenly flashed legacy sensors.
Desktop UI (v2/crates/wifi-densepose-desktop/):
* src/commands/discovery.rs — third discovery path: HTTP /status sweep
across the local /24 in parallel with mDNS/UDP. mDNS+UDP-beacon are
not advertised by current RuView FW. Replace sequential
for-task-in-tasks select-with-deadline (which blocked on slow
unrelated IPs) with futures::join_all + overall timeout.
* src/commands/server.rs — pass --bind-addr (was --bind); pass
RUST_LOG env instead of unsupported --log-level; auto-load bundled
wifi-densepose-v1.rvf next to the binary; reasonable defaults
(esp32 source, 0.0.0.0 bind).
* ui/* — keep last good node list when a poll returns 0 (discovery
is jittery on busy LANs); 8 s timeout (was 3 s); remove "simulate"
from DataSource enum and Sensing dropdown; default Sensing source
esp32.
Mobile UI (ui/mobile/):
* constants/websocket.ts — WS_PATH '/ws/sensing' + WS_PORT 8765 to
match the RuView sensing-server's WS endpoint (was the legacy
FastAPI /api/v1/stream/pose).
* services/ws.service.ts — derive WS host from serverUrl but use
WS_PORT; remove simulation fallback paths entirely (no
generateSimulatedData, no startSimulation on reconnect failure).
* stores/settingsStore.ts — serverUrl defaults to
http://100.123.189.10:8080 (deployed Mac's Tailscale IP), so the
phone connects from any network without LAN dependency.
* stores/matStore.ts — default dataSource='real',
simulationAcknowledged=true; no synthetic triage data.
* screens/MATScreen, VitalsScreen — hide simulation overlay/badge.
Docker:
* docker/docker-compose.yml — sensing-server host port 5005 -> 5006
to match the RuView FW's compiled CSI_TARGET_PORT default.
Documentation:
* docs/adr/ADR-098-esp32s3-csi-deployment-fixes.md — full forensic
ADR covering each decision, the empirical numbers that drove it,
the false hypotheses we ruled out along the way, and open items.
Verified on hardware (both nodes):
* motion empty < 0.05 (room01 0.018, room02 0.070)
* motion walking > 0.3 within 1-3 s, saturates at 1.0
* motion decay < 0.1 within 5 s after leaving
* breathing 21-22 BPM detected after ~30 s stationary
* two consecutive OTA round-trips succeed without USB intervention
* discovery finds both sensors via HTTP sweep in <2 s
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
arsen