Premise: in a multi-node CSI mesh, all nodes see the same physical
scene through slightly different multipath. Their per-window CSI
vectors cluster tightly under cosine similarity. An adversarial node
(replay / shift / noise injection) sits *outside* that cluster. The
Stoer-Wagner minimum cut on the inter-node similarity graph isolates
it cleanly when the cut is sharp.
Demo synthesises 4 honest nodes (one real CSI window from the paired
data + per-node Gaussian noise 6 dB below signal) and 1 adversarial
node under three attack modes. Cosine-similarity matrix, then
Stoer-Wagner mincut, then check whether partition_B is the singleton
{4} — the adversarial node.
Attack Mincut value Partition_B Isolated?
------- ------------ ----------- ---------
replay 3.4513 {4} YES
shift 3.5724 {4} YES
noise 2.5586 {4} YES
Detection rate: 3/3 = 100%.
Architectural payoff: this is the primitive that fills the stub at
. ADR-103 v0.2.0
can wire it in directly. The mincut value also becomes a continuous
'mesh trustworthiness' metric for the cog-gateway dashboard.
Honest scope: the demo uses sloppy attackers. Adaptive attackers who
have read this note can almost certainly evade by adding calibrated
noise that keeps cosine similarity above the cluster floor. The next
research step is the Stackelberg-game extension. See the
'Honest scope of this result' section in the research note.
Connections:
* R5 — top-8 saliency subcarriers are the priority list for a
more-targeted per-subcarrier consistency check.
* R8 — same primitive likely works at lower SNR with RSSI-only
metrics; cluster structure is preserved by the band integral.
Files:
* examples/research-sota/r7_multilink_consistency.py — pure-NumPy
Stoer-Wagner mincut + synthetic-adversary harness.
* examples/research-sota/r7_multilink_consistency_results.json —
full result JSON for cross-tick reproducibility.
* docs/research/sota-2026-05-22/R7-multilink-consistency.md — note.
* docs/research/sota-2026-05-22/PROGRESS.md — updated index + Done.
Builds directly on R5's band-spread observation. If the count-task
signal is spread across the WiFi band (R5: max/mean ratio 2.85× across
56 subcarriers), then RSSI — which is the integral of |H_k|^2 across
the band — keeps most of the information. The naive prior (RSSI throws
away 98% of CSI bytes) is misleading; the relevant metric is how much
of the *signal* is in the integral, not how many bytes are in the
representation.
Tested by aggregating each existing [56 × 20] CSI window down to a
[20]-vector RSSI proxy (mean across subcarriers per frame), training a
tiny MLP (Linear 20→32→8, 656 params, 5 KB) with vanilla NumPy SGD for
200 epochs on the same random 80/20 split as cog-person-count v0.0.2.
Result:
Full CSI v0.0.2 62.3% accuracy
RSSI-only (this) 59.1% accuracy = 94.82% retained
Per-class is also markedly more *balanced* (RSSI: 59.5 / 58.6 ; full
CSI: 86.2 / 34.3) — the tiny model on a low-dim input can't cheat by
leaning on class 0 the way v0.0.2's larger model does at inference.
What this enables on a 10-year horizon: phones, laptops, smart
speakers, smart TVs, smart lights — anything with WiFi reports RSSI
and anything with a CPU can run a 656-param MLP. Person counting
becomes a federated property of any room with WiFi, not a property of
the ESP32-S3 fleet.
What this doesn't prove (called out explicitly in the research note):
- Single room, single operator, single 30-min recording
- 2-class problem (label distribution is {0, 1})
- Single random draw — needs K-fold + multi-room replication
Three follow-up experiments queued in R8-rssi-only-count.md §'What's
next on this thread':
- Multi-room replication once #645 lands
- 3-class extension (0 / 1 / 2+) — measure the info-rate cliff
- Run on a non-ESP32 RSSI source (e.g. iw event on Linux laptop)
Files:
* examples/research-sota/r8_rssi_only_count.py — pure-NumPy, no
framework deps. Trains + evals in 0.72 s on CPU.
* examples/research-sota/r8_rssi_only_results.json — full JSON dump
for cross-tick reproducibility.
* docs/research/sota-2026-05-22/R8-rssi-only-count.md — method,
measured numbers, interpretation, what doesn't work yet.
* docs/research/sota-2026-05-22/PROGRESS.md — updated index + Done
log.
Coordination note: horizon-tracker is working on tools/ruview-mcp/
+ tools/ruview-cli/ + ADR-104 — this commit deliberately stays out
of those paths.
Sets up docs/research/sota-2026-05-22/ as the autonomous-research
output dir, with PROGRESS.md as the canonical 15-vector research
agenda spanning spatial intelligence, RF features, RSSI-only, and
exotic/long-horizon verticals. Cron d6e5c473 (*/10 * * * *) picks
threads from this file and self-terminates at 2026-05-22 08:00 ET.
First concrete contribution this tick — R5 subcarrier saliency:
* examples/research-sota/r5_subcarrier_saliency.py: pure-numpy port
of the count cog's Conv1d encoder + count head, computes per-
subcarrier input×gradient saliency via central-difference. 128
samples × 56 subcarriers × 2 forward passes/subcarrier ≈ ~3 s on
CPU, no GPU or framework dependency.
* docs/research/sota-2026-05-22/R5-subcarrier-saliency.md: research
note with motivation, method, novelty argument, and the first
measured ranking. Top-8 subcarriers for cog-person-count v0.0.2:
[41, 52, 30, 31, 10, 35, 2, 38]. Max/mean ratio 2.85x.
* v2/crates/cog-person-count/cog/artifacts/saliency.json: machine-
readable per-subcarrier saliency + top-K lists, so future-tick
experiments (retrain at K=8/16/32) consume it without re-running.
Key insight from the first measurement: top-8 saliency is *band-
spread* (indices span 2-52), not concentrated. This directly raises
R8's (RSSI-only) feasibility ceiling, because RSSI is a band-
aggregate — it retains the integral of a band-spread signal. First-
order estimate: RSSI-only should hit ~60% of full-CSI accuracy for
the count task. R7 (adversarial defence) inherits a concrete defender-
priority list: corroborate these 8 subcarriers across nodes.
This commit is the first of many short, focused contributions over
the next ~12 hours. PROGRESS.md is the canonical pointer for the
next tick to pick up the next thread.
Demos 04 and 05 work fine locally — operator has assets/X Bot.fbx
present. On the gh-pages deploy the FBX is intentionally absent
(Mixamo license boundary, .gitignored) and the previous onError
handler just logged 'FBX load failed' to the console and left a
stuck '⚠ Load failed — see console' message in the overlay.
Replaces both onError handlers with an in-page card that:
- Explains why the asset is missing (license boundary, not a bug)
- Tells you exactly how to run it locally (Mixamo download path,
where to drop the file, the serve-demo.py command)
- Links to Mixamo + the repo source + back to the gallery
- Lets the ADR-097 helpers scene keep rendering behind it
- Logs at warn (not error) — no more uncaught console.error noise
The success branch is untouched, so local development is identical
to before.
Adds a new GitHub Pages workflow that publishes the ADR-097 three.js
demo gallery alongside the existing observatory/, pose-fusion/,
pointcloud/, and nvsim/ deployments. Uses keep_files: true so the
other deployments are preserved.
What ships:
* `examples/three.js/index.html` — new landing page that lists all 5
demos with screenshots, "standalone" vs "needs FBX" badges, and an
honest note explaining the Mixamo X Bot.fbx license boundary
(demos 04 and 05 need a local download from mixamo.com; demos
01-03 run standalone in any modern browser).
* `.github/workflows/threejs-pages.yml` — staged copy of demos/,
screenshots/, README.md, and the new index.html into
`_site/three.js/`. Drops an `assets/README.txt` placeholder
explaining the FBX-not-shipped policy. Triggered on changes to
examples/three.js/** or the workflow itself.
* README.md — adds the live link to the existing demo row
(`▶ three.js Demos (5)`) plus a one-line callout describing the
gallery and the FBX caveat.
After this PR merges, the workflow runs and publishes:
https://ruvnet.github.io/RuView/three.js/
* feat(examples/three.js): cinematic skinned realtime pose demo + ESP32 CSI bridge
Five-stage example progression exploring three.js helpers (ADR-097 surface) as
a viewer for live RuView sensor data:
1. helpers-demo.html — clean ADR-097 helper reference (GridHelper,
PolarGridHelper, BoxHelper, AxesHelper),
file://-safe, no backend
2. helpers-cinematic.html — same scene + UnrealBloomPass + pseudo-CSI
sonar pings + tomography sweep + procedural
cyber floor + ambient drift particles
3. helpers-skinned.html — replaces sphere skeleton with Mixamo X Bot
via GLTFLoader from threejs.org CDN, plays
bundled animations with additive blending
4. helpers-skinned-fbx.html — same but loads a local Mixamo FBX (needs
serve-demo.py — file:// can't fetch local
siblings). Drop X Bot.fbx alongside.
5. helpers-skinned-realtime.html — webcam → MediaPipe Pose Heavy →
poseWorldLandmarks → direct quaternion
retargeting onto the Mixamo skeleton.
Real ESP32-S3 CSI streamed over WebSocket
from ruvultra (Tailscale, port 8766).
Supporting:
- serve-demo.py threaded HTTP server with no-cache headers
(fixes net::ERR_EMPTY_RESPONSE on the FBX path)
- ruvultra-csi-bridge.py ESP32 RuView firmware tick → WebSocket bridge,
runs as systemd-run unit on ruvultra
Bugs found + fixed along the way (all documented in code comments):
- FBX exports yield TWO parallel Bone trees with identical names; only the
SkinnedMesh.skeleton.bones one drives visible deformation. model.traverse
finds orphans.
- Mixamo FBX nests a zero-length wrapper bone above the real bone, same name.
bone.children[0].getWorldPosition == bone.getWorldPosition → restDir is
(0,0,0) → setFromUnitVectors collapses to identity. Walk past same-named
same-position wrappers when computing tail.
- AnimationMixer.update() with a "stopped" action still mutates bones unless
enabled=false is set.
Retargeting layer in helpers-skinned-realtime.html:
- 12 bones direct quaternion retarget (arms × 2, legs × 2, spine × 3, neck)
- Hips root rotation from shoulder/hip line basis (torso twist + lean)
- Neck aims at ear-midpoint (kp 7+8), not nose (kp 0), to remove the
forward bias of the protruding-nose anchor
- One Euro Filter per landmark per axis (Casiez 2012) — adaptive low-pass
- Visibility-weighted per-bone slerp gain — occluded limbs relax to rest
- URL toggles: ?mirror= ?yflip= ?zflip= ?cnn=0/1/2 ?csi=ws://...
Live CSI integration:
- Bridge parses adaptive_ctrl tick lines (motion/presence/rssi/yield)
- Browser fans single ESP32 reading across 4 UI nodes with phase-shifted
wobble (0.88–1.00 × sin(t·0.55 + offsetᵢ))
- EMA α=0.06 (~3 sec time constant), HUD update throttled 3 Hz
Co-Authored-By: claude-flow <ruv@ruv.net>
* refactor(examples/three.js): organize into demos/screenshots/server/assets + add README
Flatten the 13-file flat layout into purposeful subfolders so the demo
collection has a clean top-level entry point (README.md) and the file roles
are obvious from a directory listing.
Layout:
demos/ 01..05 — numbered for the progression (helpers → cinematic →
skinned → skinned-fbx → skinned-realtime)
screenshots/ one PNG per demo, matching the demo's filename prefix
server/ serve-demo.py + ruvultra-csi-bridge.py
assets/ X Bot.fbx (gitignored, used by demos 04 and 05)
Touched files (beyond the renames):
- 04-skinned-fbx.html, 05-skinned-realtime.html: MODEL_URL now resolves
'../assets/X%20Bot.fbx' instead of './X%20Bot.fbx'
- server/serve-demo.py: chdir() walks 3 levels up to repo root (was 2), and
the URL banner now lists all 5 demos
- .gitignore: comment refresh — points at assets/ and screenshots/
- 05-skinned-realtime.html also picks up in-flight fps-tune work from this
branch (Holistic script, SMOOTH_K URL param, slerp gain scaling) since
those edits and the rename hit the same file
Verified end-to-end:
- python examples/three.js/server/serve-demo.py
- all 5 demos return 200, X Bot.fbx returns 200 from new asset/ path
- demos 04 + 05 render the X Bot mesh; 0 JS errors via browser eval
- screenshots reproduced match the originals
Co-Authored-By: claude-flow <ruv@ruv.net>
The Rust port lived two directories deep (rust-port/wifi-densepose-rs/)
without any sibling under rust-port/ that warranted the extra level.
Move the whole workspace up to v2/ to match v1/ (Python) at the same
depth and shorten every cd / build command across the repo.
git mv preserves history for all tracked files. 60 files updated for
path references (CI workflows, ADRs, docs, scripts, READMEs, internal
.claude-flow state). Two manual fixes for relative-cd paths in
CLAUDE.md and ADR-043 that became wrong after the depth change
(cd ../.. → cd ..).
Validated:
- cargo check --workspace --no-default-features → clean (after target/
nuke; the gitignored target/ was carried by the OS rename and had
hard-coded old paths in build scripts)
- cargo test --workspace --no-default-features → 1,539 passed, 0 failed,
8 ignored (same totals as pre-rename)
- ESP32-S3 on COM7 → still streaming live CSI (cb #40300, RSSI -64 dBm)
After-merge follow-up: contributors should `rm -rf v2/target` once and
let cargo regenerate from the new path.
- examples/medical/README.md: full guide for BP estimator,
hardware requirements, sample output, accuracy table, AHA
categories, disclaimer, RuView integration explanation
- README.md: added Medical Examples to documentation table
Co-Authored-By: claude-flow <ruv@ruv.net>
Reads real-time heart rate from MR60BHA2 60 GHz mmWave sensor and
estimates BP trends using HR/HRV correlation model:
- Mean HR → baseline SBP/DBP
- SDNN (HRV) → sympathetic/parasympathetic adjustment
- LF/HF spectral ratio → fine adjustment (with numpy)
- Optional calibration with a real BP reading
Verified on real hardware: 125/83 mmHg estimate from 35 HR samples
over 60 seconds at 84 bpm mean HR with 91ms SDNN.
NOT A MEDICAL DEVICE — research/wellness tracking only.
Co-Authored-By: claude-flow <ruv@ruv.net>
rUv