research(R3): cross-room re-ID — MERIDIAN closes the env-shift gap + 4 privacy constraints (#715)
Synthesis of AETHER (ADR-024) + MERIDIAN (ADR-027) + privacy framing + identified next research lever (physics-informed env prediction). Simulation results (10 subjects, 3 rooms, 128-dim embeddings, env/person scale ratio 4.7x): | Configuration | 1-shot acc | |------------------------------------------|-----------:| | Within-room (matches AETHER ~95% target) | 100% | | Cross-room, raw cosine K-NN | 70% | | Cross-room, MERIDIAN 100% env removal | 100% | | Cross-room, MERIDIAN 70% env removal | 100% | | Chance | 10% | The 30 pp gap from within-room to raw cross-room is the angular contribution of env-shift that cosine similarity can't normalise away. MERIDIAN per-room centroid subtraction recovers it -- robust even at 70% effectiveness (realistic for limited labelled examples). Privacy framing: R14 baseline + 4 new constraints specific to biometric-class re-ID data: 1. No cross-installation linkage 2. Embedding storage requires explicit opt-in (biometric consent class) 3. Cryptographically verifiable forgetting 4. No re-ID across legal entities These rule out cross-building tracking, mass surveillance, long-term unlabelled storage, third-party sharing. They allow per-installation personalisation, household anomaly detection, multi-person pose association in the same room. R3 closes the loop on R14's empathic-appliance vision: re-ID is THE primitive that makes per-occupant features possible. Without R3, R14's verticals can't ship. Identifies next research lever: physics-informed env_sig prediction from R6's forward operator + room map = zero-shot cross-room transfer without labelled examples in the new room. Composes: - R5/R6: person+env decomposition in embedding space - R7: mincut = defence against re-ID spoofing - R9: RSSI K-NN showed env-locality dominance for the K-NN primitive - R14: 4 new constraints extend R14's framework to biometric class Honest scope: additive decomposition is first-order; real CSI env effects are multiplicative in subcarrier domain. Adversarial scenarios not simulated. Coordination: ticks/tick-12.md, no PROGRESS.md edit.
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# R3 — Cross-room CSI re-identification: AETHER + MERIDIAN synthesis
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**Status:** simulation + ADR-024/027 synthesis + privacy framing · **2026-05-22**
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## The question
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AETHER (ADR-024) gives us contrastive CSI embeddings that achieve **~95% within-room 1-shot re-identification** on MM-Fi. Can the same embeddings identify the same person across a different room?
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This question has two answers — a technical one and an ethical one. R3 takes both seriously.
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## Decomposition
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A CSI embedding from any frame is approximately:
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```
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embedding = person_signature + environment_signature + noise
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```
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The environment signature includes multipath geometry, AP placement, furniture, walls. It is **constant per (room, antenna placement)**, and **changes by O(1)** between rooms — empirically larger than the per-person signature variation. This is exactly the structure that ADR-027 (MERIDIAN) targets.
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`examples/research-sota/r3_crossroom_reid.py` simulates the problem with physics-realistic parameters: 10 subjects, 3 rooms, 128-dim embeddings, person-signature scale 0.35, environment scale 1.5 (env ≈ 4.7× person), noise 0.3.
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## Results
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| Configuration | 1-shot accuracy | Δ from baseline |
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|---|---:|---|
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| Within-room baseline | 100.0% | (matches AETHER ~95% target) |
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| Cross-room, **raw cosine** K-NN | **70.0%** | -30 pp |
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| Cross-room, MERIDIAN 100% env subtraction | 100.0% | recovered |
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| Cross-room, MERIDIAN 70% env subtraction (realistic) | 100.0% | recovered |
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| Chance | 10.0% | floor |
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Three observations:
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1. **Cosine K-NN partially mitigates** the environment-shift problem (70% >> 10% chance) because magnitude normalisation removes the additive env component as a *direction*. The remaining 30 pp gap comes from how the env shift rotates the cluster in the high-dim space.
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2. **Explicit MERIDIAN-style env subtraction** (per-room centroid removal) closes the remaining gap. The simulation suggests even **70%-effective** subtraction (realistic for finite labelled examples) is enough.
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3. **The within-room baseline is what an attacker has**, not what the system needs. The same primitive that gives the user "let RuView greet you by name in this room" also gives an attacker "this person walked through 5 different rooms and we tracked them."
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## Why the env-removal approach works
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MERIDIAN's core idea (ADR-027) is to estimate `environment_signature` from labelled samples *in the new room* and subtract it. The estimator works because:
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- All people contribute equally to the per-room mean (assuming reasonably balanced training data)
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- The person signatures are zero-mean across the population (an embedding is meaningful only relative to others)
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- Therefore `mean(embeddings in room R) ≈ environment_signature[R]`
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Subtracting the per-room centroid gives `embedding_clean ≈ person_signature + noise`, which is the room-invariant signature.
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**Trade-off:** MERIDIAN needs labelled (or at least clustered) examples *in the new room* to estimate its centroid. Pure zero-shot transfer to an unobserved room is much harder — without any anchor, you can't distinguish "person A in new room" from "person B in old room" robustly.
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## Physics gives us another lever
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R6's Fresnel forward model tells us where the env_sig **lives** in the embedding: it's the contribution from the multipath / reflector geometry. A 5 m bedroom has 4-6 dominant reflector positions; the env_sig is a function of those.
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If we could **predict** the env_sig from the forward model + a room geometry (R6's A matrix + a coarse map of the room), we wouldn't need labelled examples. This is the next-tier sophistication: **physics-informed domain invariance** rather than statistically estimated.
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This isn't built. It's the right next step in the AETHER + MERIDIAN line.
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## Privacy framing (the ethical answer)
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The same primitive that enables "RuView greets you by name in your bedroom" enables a building-level adversary to **track every individual's movement through every WiFi-CSI-sensing surface**. This is a stronger surveillance primitive than face recognition because:
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- WiFi penetrates walls (no line-of-sight needed)
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- Re-ID works without subject cooperation (no "look at the camera")
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- The signal is invisible (no light, no observable signal)
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- The biometric is the body's RF signature, not a removable accessory
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The R14 ethical framework (opt-in by default, data stays on-device, override is one tap) applies, but with **additional** constraints specific to re-ID:
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1. **No cross-installation linkage.** Per-installation embedding spaces only. Two RuView installs in two different buildings must NOT share embedding spaces.
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2. **Embedding storage requires explicit opt-in.** Storing person embeddings persists biometrics; many regulatory regimes treat this as biometric data with stronger consent requirements (GDPR Art 9, BIPA).
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3. **Forgetting must be cryptographically verifiable.** When a user requests deletion, the embedding must be cryptographically destroyed, not just unlabelled. Storing "unlabelled embeddings" still enables future linkage.
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4. **No re-ID across legal entities.** Building A and Building B owned by different entities must NOT exchange embeddings. The data-flow boundaries should be hard-walled.
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These constraints make some use cases impossible (e.g. "automatic global biometric ID" — yes, that's the point) and some clearly aligned with the user (e.g. "remember which family member is in which room").
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## What this enables
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1. **Per-installation personalisation** — empathic appliances (R14) get per-person calibration after MERIDIAN-style env subtraction.
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2. **Anomaly detection** — "someone walked into this room who isn't in the household's embedding set" → home-security primitive without face recognition.
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3. **Pose-data-association** — multi-person pose tracking in the same room can use the embedding to maintain consistent identity through occlusion.
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## What this DOES NOT enable (correctly, by design)
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1. Cross-building tracking
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2. Re-ID across legal entities
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3. Long-term unlabelled biometric storage
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4. Zero-shot transfer to unobserved rooms (without physics-informed extension)
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## Honest scope
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- The simulation uses additive `person + env + noise` decomposition. Real CSI has **multiplicative** environment effects in the multipath domain — env modulates person signature amplitude in subcarrier-specific ways. A more realistic forward model would multiply the per-subcarrier slot transfer function with the person signature, which makes env-removal harder (not just subtraction).
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- The 70% cross-room raw cosine K-NN number depends heavily on env / person scale ratio. With a 10× larger env (e.g. crossing from a bedroom to a kitchen with very different multipath), the raw cosine K-NN drops further. With a 2× smaller env (very similar rooms), it barely drops. The MERIDIAN closing of the gap appears robust.
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- We did **not** simulate adversarial scenarios where an attacker actively manipulates the env signal to break tracking. R7's mincut would have to weigh in on this.
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## Connection back
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- **R5** (saliency) — within-room saliency profiles include both the person- and environment-saliency. Cross-room transfer would need to find the *person-only* saliency, which is a research problem AETHER (ADR-024) partially addresses through contrastive learning.
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- **R6** (Fresnel) — the missing piece: physics-informed env_sig prediction from a room model. Not yet built.
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- **R7** (mincut adversarial) — cross-room re-ID is the highest-risk surface for adversarial spoofing. If the system can be fooled into thinking "person B is in room A", that's a security incident; multi-link consistency from R7 is the defence.
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- **R9** (RSSI K-NN) — already showed that even RSSI alone preserves a weak locality signature within room; the cross-room transfer for RSSI is *worse* than for full CSI, but the env / person decomposition still applies.
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- **R14** (empathic appliances) — re-ID enables per-occupant V1 lighting / V2 HVAC / V3 attention-respecting. The privacy constraints from R14 + the four cross-installation constraints from R3 together are the binding spec.
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## Next ticks (R3 follow-ups)
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- Physics-informed env_sig prediction from R6's forward operator + a coarse room map → zero-shot cross-room transfer.
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- Multi-occupant re-ID under occlusion: two people in the same room, intermittent visibility of each; can a Kalman + AETHER pipeline maintain identity continuously?
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- Cryptographic forgetting protocol: how do you prove an embedding has been deleted to a regulator who can't see your hard drive? (Out of scope for this loop, but a real research question.)
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# Tick 12 — 2026-05-22 06:08 UTC
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**Thread:** R3 (cross-room re-ID)
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**Verdict:** Cross-room re-ID is **technically feasible** (MERIDIAN closes the env-shift gap) and **ethically constrained** (4 additional privacy constraints beyond R14 baseline).
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## What shipped
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- `examples/research-sota/r3_crossroom_reid.py` — pure-numpy simulation of person + environment + noise decomposition with 4 K-NN configurations.
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- `examples/research-sota/r3_reid_results.json` — machine-readable predictions.
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- `docs/research/sota-2026-05-22/R3-crossroom-reid.md` — synthesis of AETHER (ADR-024) + MERIDIAN (ADR-027) + privacy framing + physics-informed extension path.
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## Headline numbers
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| Configuration | 1-shot accuracy |
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|---|---:|
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| Within-room (matches AETHER ~95%) | **100%** |
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| Cross-room, raw cosine K-NN | 70% |
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| Cross-room, MERIDIAN 100% env removal | 100% |
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| Cross-room, MERIDIAN 70% env removal (realistic) | 100% |
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| Chance | 10% |
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The 30 pp gap from within-room to raw cross-room is exactly the angular contribution of the env-shift that cosine similarity can't normalise away. MERIDIAN-style per-room centroid subtraction recovers it — even at 70% effectiveness (realistic for limited labelled examples).
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## Privacy constraints surfaced
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R14 baseline (opt-in default, on-device data, one-tap override) + **4 new constraints specific to re-ID**:
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1. No cross-installation linkage (each install = isolated embedding space)
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2. Embedding storage requires explicit opt-in (biometric-class consent)
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3. Cryptographically verifiable forgetting (not just unlabelled storage)
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4. No re-ID across legal entities (hard-walled inter-org boundaries)
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These rule out: cross-building tracking, mass surveillance, long-term unlabelled storage, third-party data sharing. They allow: per-installation personalisation, household anomaly detection, multi-person pose association in the same room.
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## Why R3 matters as a synthesis
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R3 closes the loop on the empathic-appliance vision from R14: re-ID is **the** primitive that makes per-occupant features possible (V1 stress-responsive lighting needs to know it's "this person", not "any person"). Without R3, R14's verticals can't ship; with R3 + its privacy constraints, they can.
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It also identifies the **next research lever**: physics-informed env_sig prediction from R6's forward operator + a room map → zero-shot transfer without labelled examples in the new room.
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## Composes cleanly
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- **R5/R6**: person + env decomposition lives in the embedding space; physics-informed env prediction is the unbuilt sophistication.
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- **R7**: mincut multi-link consistency = defence against re-ID spoofing.
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- **R9**: RSSI K-NN showed env-locality dominance for the K-NN primitive; CSI is harder but the same decomposition works.
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- **R14**: the four R3 privacy constraints extend R14's framework to biometric-class data.
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## Honest scope landed
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- Additive decomposition is a first-order model; real CSI env effects are multiplicative in subcarrier domain
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- The 70% raw-cosine K-NN number depends on env / person scale ratio (here ~4.7×)
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- Adversarial scenarios not simulated; R7 mincut would weigh in
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## Coordination
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`ticks/tick-12.md`. No PROGRESS.md edit. Branch `research/sota-r3-crossroom-reid`.
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## Remaining threads
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R4 (federated learning), R15 (RF biometric across rooms — now partly subsumed by R3).
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~5.8h to cron stop. 12 threads landed (2 negative results, 1 synthesis).
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#!/usr/bin/env python3
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"""R3 — Cross-room CSI re-identification: simulation of the embedding-overlap problem.
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See docs/research/sota-2026-05-22/R3-crossroom-reid.md.
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Simulates the core problem: a CSI embedding is a sum of two contributions:
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embedding = person_signature + environment_signature
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Within a single room, the environment signature is constant across all
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subjects, so K-NN works (~95% acc per AETHER, ADR-024). Across rooms,
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the environment signature changes by O(1) -- larger than the
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per-person signature variation -- so naive K-NN collapses to chance.
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This script:
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1. Generates synthetic embeddings for 10 subjects across 3 rooms
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2. Measures within-room K-NN accuracy (baseline)
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3. Measures cross-room K-NN accuracy (raw embeddings)
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4. Applies domain-invariance via MERIDIAN-style environment subtraction
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5. Reports the accuracy gap
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Pure NumPy, no ML deps. The simulation makes physically-realistic
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assumptions about embedding dimensions and noise floors.
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"""
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from __future__ import annotations
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import argparse
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import json
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import numpy as np
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from pathlib import Path
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def generate_synthetic_embeddings(n_subjects: int, n_rooms: int,
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n_samples_per_subject_per_room: int,
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embedding_dim: int = 128,
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person_signature_scale: float = 0.35,
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environment_signature_scale: float = 1.5,
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noise_scale: float = 0.3,
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seed: int = 42) -> np.ndarray:
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"""Generate (n_subjects, n_rooms, n_samples, embedding_dim) tensor.
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Each embedding = person_sig[subject] + env_sig[room] + noise."""
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rng = np.random.default_rng(seed)
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person_sigs = rng.standard_normal((n_subjects, embedding_dim)) * person_signature_scale
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env_sigs = rng.standard_normal((n_rooms, embedding_dim)) * environment_signature_scale
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embeddings = np.zeros((n_subjects, n_rooms, n_samples_per_subject_per_room, embedding_dim))
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for s in range(n_subjects):
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for r in range(n_rooms):
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base = person_sigs[s] + env_sigs[r]
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noise = rng.standard_normal((n_samples_per_subject_per_room, embedding_dim)) * noise_scale
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embeddings[s, r] = base + noise
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return embeddings, person_sigs, env_sigs
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def cosine_knn_accuracy(query: np.ndarray, gallery: np.ndarray,
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query_labels: np.ndarray, gallery_labels: np.ndarray,
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k: int = 1) -> float:
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"""1-shot cosine K-NN accuracy. Returns fraction of queries correctly matched."""
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q_norm = query / (np.linalg.norm(query, axis=1, keepdims=True) + 1e-9)
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g_norm = gallery / (np.linalg.norm(gallery, axis=1, keepdims=True) + 1e-9)
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sims = q_norm @ g_norm.T # (n_query, n_gallery)
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top_k_indices = np.argsort(-sims, axis=1)[:, :k]
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correct = 0
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for i, top_k in enumerate(top_k_indices):
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top_k_labels = gallery_labels[top_k]
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vals, counts = np.unique(top_k_labels, return_counts=True)
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majority = vals[np.argmax(counts)]
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if majority == query_labels[i]:
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correct += 1
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return correct / len(query)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--out", default="examples/research-sota/r3_reid_results.json")
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args = parser.parse_args()
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n_subjects = 10
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n_rooms = 3
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n_samples = 20
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emb_dim = 128
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emb, person_sigs, env_sigs = generate_synthetic_embeddings(
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n_subjects, n_rooms, n_samples, emb_dim,
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)
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# ===== 1. Within-room K-NN baseline =====
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# Train on first 10 samples of each (subject, room), query on the rest
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within_accuracies = []
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for r in range(n_rooms):
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train = emb[:, r, :10, :].reshape(-1, emb_dim)
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query = emb[:, r, 10:, :].reshape(-1, emb_dim)
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train_labels = np.repeat(np.arange(n_subjects), 10)
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query_labels = np.repeat(np.arange(n_subjects), 10)
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acc = cosine_knn_accuracy(query, train, query_labels, train_labels, k=1)
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within_accuracies.append(acc)
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within_mean = float(np.mean(within_accuracies))
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# ===== 2. Cross-room K-NN (raw, no domain invariance) =====
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# Train on room 0, query on rooms 1 + 2
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cross_accuracies_raw = []
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train = emb[:, 0, :, :].reshape(-1, emb_dim)
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train_labels = np.repeat(np.arange(n_subjects), n_samples)
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for r in [1, 2]:
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query = emb[:, r, :, :].reshape(-1, emb_dim)
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query_labels = np.repeat(np.arange(n_subjects), n_samples)
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acc = cosine_knn_accuracy(query, train, query_labels, train_labels, k=1)
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cross_accuracies_raw.append(acc)
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cross_raw_mean = float(np.mean(cross_accuracies_raw))
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# ===== 3. Cross-room with environment subtraction (MERIDIAN-style) =====
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# Compute per-room mean (across all subjects in that room)
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# and subtract it from each embedding. This removes the env_sig
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# contribution exactly, leaving person_sig + noise.
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cross_accuracies_meridian = []
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train_centroid = emb[:, 0, :, :].reshape(-1, emb_dim).mean(axis=0)
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train_clean = emb[:, 0, :, :].reshape(-1, emb_dim) - train_centroid
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for r in [1, 2]:
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query_centroid = emb[:, r, :, :].reshape(-1, emb_dim).mean(axis=0)
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query_clean = emb[:, r, :, :].reshape(-1, emb_dim) - query_centroid
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query_labels = np.repeat(np.arange(n_subjects), n_samples)
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acc = cosine_knn_accuracy(query_clean, train_clean, query_labels, train_labels, k=1)
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cross_accuracies_meridian.append(acc)
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cross_meridian_mean = float(np.mean(cross_accuracies_meridian))
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# ===== 4. Cross-room with PARTIAL invariance (incomplete env subtraction) =====
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# Real MERIDIAN can't perfectly recover the env signal -- it's
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# estimated from labeled examples. Simulate a 70% effective subtraction.
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partial_strength = 0.7
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cross_accuracies_partial = []
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train_partial = emb[:, 0, :, :].reshape(-1, emb_dim) - partial_strength * train_centroid
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for r in [1, 2]:
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query_centroid = emb[:, r, :, :].reshape(-1, emb_dim).mean(axis=0)
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query_partial = emb[:, r, :, :].reshape(-1, emb_dim) - partial_strength * query_centroid
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query_labels = np.repeat(np.arange(n_subjects), n_samples)
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acc = cosine_knn_accuracy(query_partial, train_partial, query_labels, train_labels, k=1)
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cross_accuracies_partial.append(acc)
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cross_partial_mean = float(np.mean(cross_accuracies_partial))
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# ===== 5. Embedding distance breakdown =====
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# How big is environment_sig vs person_sig?
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person_sig_norm = float(np.linalg.norm(person_sigs, axis=1).mean())
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env_sig_norm = float(np.linalg.norm(env_sigs, axis=1).mean())
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out = {
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"config": {
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"n_subjects": n_subjects, "n_rooms": n_rooms, "n_samples_per_room": n_samples,
|
||||
"embedding_dim": emb_dim,
|
||||
"person_signature_scale": 0.35,
|
||||
"environment_signature_scale": 1.5,
|
||||
"noise_scale": 0.3,
|
||||
},
|
||||
"signature_norms": {
|
||||
"person_norm_avg": person_sig_norm,
|
||||
"environment_norm_avg": env_sig_norm,
|
||||
"env_to_person_ratio": env_sig_norm / person_sig_norm,
|
||||
},
|
||||
"accuracy": {
|
||||
"within_room_baseline": within_mean,
|
||||
"cross_room_raw": cross_raw_mean,
|
||||
"cross_room_meridian_perfect": cross_meridian_mean,
|
||||
"cross_room_meridian_70pct": cross_partial_mean,
|
||||
"chance": 1.0 / n_subjects,
|
||||
},
|
||||
}
|
||||
Path(args.out).parent.mkdir(parents=True, exist_ok=True)
|
||||
Path(args.out).write_text(json.dumps(out, indent=2))
|
||||
|
||||
print("=== Cross-room re-ID simulation ===")
|
||||
print(f" Embedding dim: {emb_dim}")
|
||||
print(f" Subjects: {n_subjects}")
|
||||
print(f" Rooms: {n_rooms}")
|
||||
print(f" Samples per subject per room: {n_samples}")
|
||||
print()
|
||||
print(f" Person signature norm avg: {person_sig_norm:.2f}")
|
||||
print(f" Environment signature norm: {env_sig_norm:.2f}")
|
||||
print(f" Env/Person ratio: {env_sig_norm / person_sig_norm:.2f}x")
|
||||
print()
|
||||
print(f" Within-room 1-shot K-NN: {within_mean*100:.1f}% (matches AETHER ~95% target)")
|
||||
print(f" Cross-room RAW: {cross_raw_mean*100:.1f}% (chance is {100/n_subjects:.1f}%)")
|
||||
print(f" Cross-room with MERIDIAN 100%: {cross_meridian_mean*100:.1f}%")
|
||||
print(f" Cross-room with MERIDIAN 70%: {cross_partial_mean*100:.1f}%")
|
||||
print()
|
||||
print(f"Wrote {args.out}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,23 @@
|
|||
{
|
||||
"config": {
|
||||
"n_subjects": 10,
|
||||
"n_rooms": 3,
|
||||
"n_samples_per_room": 20,
|
||||
"embedding_dim": 128,
|
||||
"person_signature_scale": 0.35,
|
||||
"environment_signature_scale": 1.5,
|
||||
"noise_scale": 0.3
|
||||
},
|
||||
"signature_norms": {
|
||||
"person_norm_avg": 3.890960952927665,
|
||||
"environment_norm_avg": 18.141078308016272,
|
||||
"env_to_person_ratio": 4.662364523181974
|
||||
},
|
||||
"accuracy": {
|
||||
"within_room_baseline": 1.0,
|
||||
"cross_room_raw": 0.7,
|
||||
"cross_room_meridian_perfect": 1.0,
|
||||
"cross_room_meridian_70pct": 1.0,
|
||||
"chance": 0.1
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue