Hybrid Fusion and Cross-Geometry Transfer: BLE+CSI fusion across phone penetration and across rooms

Current assessment (2026-09-15)

The field comparison remains unexecuted. The feature-type study's nine seed pairs support a simulated contrast between movement features and fingerprints. The six-floor drift pilot does not establish its powered claim: corrected inflation interval [1.04, 9.85] does not clear 1.5. BLE device counts are not an absolute reference without a validated carriage model.

Requiring fusion to beat both inputs in every phone-carriage regime is too strong as an assumed outcome. At zero participating phones the system needs a stated CSI-only or withheld-output fallback. Chronological and held-out-room tests must keep labels used for adaptation separate from those used to score uncertainty. The current listener is the fleet Broadcom controller; ESP32 hardware in the early design is historical.

Next evidence needed: Complete the independent-reference gate in EXP-F1, then compare calibration policies and simpler baselines at equal reference cost. Report the regimes where fusion helps, fails or cannot issue a count.

Basis: Thesis Narrative, the September 10 synthesis, and the September 15 register review. See the review record for evidence sources and outstanding checks.

Earlier protocol and dated results

The methods and observations below retain their original scope. Read their readiness statements and proposed next steps as historical. The current assessment above records what has changed; frozen thresholds have not been retrospectively revised.

The problem, in plain words

Picture two ways to count people in a room. The first listens to how bodies disturb the Wi-Fi echoes bouncing around — very sensitive to movement, but it learns a pattern tied to where the walls and furniture are. Rearrange the room, or walk into a different room, and the pattern no longer means what it used to. The second just counts the phones broadcasting Bluetooth. That is crude — some people leave phones in a bag, some carry two, some have Bluetooth off — but "a phone is a phone" no matter the room's shape.

EXP-F3 asks whether combining them gives you the best of both. Two things can go wrong. First, as fewer people carry a beacon (the "penetration" drops from everyone to nobody), the Bluetooth anchor starves — does the fused counter degrade gracefully back to Wi-Fi alone, or collapse? Second, when you carry a model trained in room A into room B, does a short 30-minute recalibration (using the cheap Bluetooth counter as ground truth) rescue it, or do you have to retrain from scratch? The deep reason to hope is a physics one: the kind of feature matters. A feature that measures how fast the channel is changing (motion) should travel between rooms; a feature that memorizes the exact echo map should not.

What we are trying to prove

• Axis-1 hypothesis (falsifiable): a BLE+CSI fusion counter has strictly lower error than the best single modality at intermediate phone-penetration (25-75 %), matches CSI-only at 0 % penetration (graceful fallback), and beats CSI-only at 100 % (the BLE anchor pays off). If fusion never beats the better single modality, or if it underperforms CSI-only when BLE starves, the fusion premise fails.
• Axis-2 hypothesis (falsifiable): a room-A fusion model, given a single 30-minute BLE-anchored calibration in room B, reaches >= 80 % of a natively-trained room-B model's accuracy — and the mechanism-carrying (kinematic / PINN) variant transfers better than the fingerprint (LGBM) variant. If a 30-minute calibration cannot close the gap, or if fingerprint models transfer just as well, the "mechanism transfers, parameters don't" story collapses.
• What a null means: a null on Axis 1 says BLE and CSI are redundant, not complementary — you would pick the cheaper one and drop fusion. A null on Axis 2 says CSI counting is irreducibly per-room and needs full recalibration everywhere, which would make estate-scale deployment far more expensive. Both nulls are publishable and would reshape the System-Design chapter.

How the experiment works (plain method)

The field design (planned): deploy identical kit in two rooms — 4x Pi 5 + AX210 for CSI, 2x ESP32 BLE listeners, a supplementary CSI pair. Run five app-driven scenarios where the app toggles how many of the 8 participants advertise a BLE beacon (100 % -> 0 %). Train a feature-level fusion model in room A on the extreme scenarios (all-phones and no-phones), then evaluate on the held-out middle penetrations. For transfer, apply the room-A model to room B zero-shot, then after a 10/20/30-minute BLE-anchored recalibration, and compare against a model trained natively in room B.

The in-silico stand-ins that have actually run replace the hardware capture with the coupled simulator (JuPedSim crowd -> Sionna ray-traced CSI, with co-registered BLE solved from the same channel):

1. c-feature-type-transfer — freeze the crowd and the room, move only the furniture in ordered steps, and watch three feature families (CSI-fingerprint, BLE-fingerprint, CSI-kinematic) drift. This isolates which kind of feature survives rearrangement — the Axis-2 mechanism.
2. c-csi-cross-geometry-scaleout — sweep occupancy across many distinct ResPlan floors and measure how badly a count map calibrated on one floor inflates its error on held-out floors. This quantifies how many geometries the field study needs.
3. c-csi-ble-fusion — run the coupled CSI+BLE chain under real motion and ask whether periodic BLE recalibration bounds the cross-environment drift CSI-only suffers.
4. c-csi-topology-sniffer-count — test whether a floor's room-adjacency topology (vs its area) drives how many sniffers you need — the deployment-cost side of Axis 2.

What we've found so far (honest, across four campaigns)

The field experiment itself: 0 runs, not executed. Everything below is simulation, on self-authored crowds and synthetic CSI/BLE with a hand-tuned Gaussian BLE noise model.

Reading this honestly:

• The Axis-2 mechanism (feature type decides transfer) is the most trustworthy in-silico result on this card: a paired, seed-replicated 9/9 finding with a figure. It is the empirical footing for EXP-F3's claim that kinematic/PINN models will transfer where fingerprint/LGBM models won't.
• The cross-geometry magnitude is genuinely established as a direction (geometry >> seed) but not as a number — the x3.37 is a heterogeneous mixture spanning 0.70x to 49.8x per floor, and the powered 40-floor test the design itself demands was never finished. Do not cite a specific inflation multiple as settled.
• The direct fusion claim — the literal Axis-1 hypothesis — has no figure-backed, adequately-powered in-silico support yet. The one useful thing c-csi-ble-fusion established is that CSI's contribution to the fusion is bigger than earlier sessions thought (Doppler temporal structure carries count information), which raises the CSI-side floor of any switch/fusion. That is a component result, not the fusion verdict.
• The placement negative is a real, valuable simplification: for Axis-2 deployment cost, plan sniffers-per-building on area + AP count, not topology.

How to read the figures

Two campaigns produced figures that survive in their session manifests; the other two do not (the fusion session has none, and the scaleout Phase-1 figures are not in the manifest's figures array despite the synthesis describing them).

• fig_seed_sweep (c-feature-type-transfer) — count-MAE vs furniture displacement (Delta layout 0 -> 1.05 m), one curve per feature family across the 9 seed pairs. Read the ordering and the flatness, not individual points: the two fingerprint curves (CSI, BLE) climb steeply and spread wide across seeds; the kinematic curve hugs the floor (sub-person) in every pair. The story is the gap between the green kinematic band and the warm fingerprint bands, and that the gap is stable across seeds.
• fig_csi_topology_linkunit (c-csi-topology-sniffer-count) — per-link discriminability against wall-count-on-path, with a competing area panel. The honest reading is a flat cloud against wall-count (rho +0.14, no trend) and a downward cloud against area (rho -0.54): topology does not predict, area does. The BLE-vs-CSI panel shows BLE's ~2.5x separation advantage per link.

Key references

• longo2019_b72f ↗ — real dual WiFi+BLE occupancy capture; the field basis for fusing the two channels.
• shahbazian2023_1172 ↗ — combined-BLE-and-WiFi survey; the problem framing and the privacy surface.
• zou2018_1590 ↗ — device-free CSI counting baseline and the fingerprint feature class.
• wang2015_48cf ↗ — CARM; the kinematic/motion feature class that transfers.
• zheng2019_5389 ↗ — body-velocity profiles as an environment-independent representation; the invariance analogue.
• chen2023_5cbd ↗ — the five-axis domain-generalization taxonomy Axis 2 lives in.
• santos2024_1e39 — the cross-room problem statement; environment as the unit of replication.
• hou2023_bf83 ↗, khan2023_b7c5 ↗ — few-shot / transfer counting; the 30-minute recalibration analogue.
• demrozi2021_bf55 ↗, iannizzotto2025_b55c ↗ — the BLE anchor's real-world basis.
• afghantoloee2021_8628 ↗, zhen2022_bb0b ↗ — the sniffer/anchor placement literature the topology negative sits against.
• zhang2026_ccac ↗, guarino2026_e72c ↗, cominelli2023_e6ee ↗ — the reproducibility + sim-to-real bar the SWE and Red-Team voices invoke.

Design status and cross-experiment links

EXP-F3 remains planned — the two-room AX210/Pi 5 + ESP32 capture is not executed. Its two field figures (penetration sweep, transfer ladder) are pre-registered above. The in-silico campaigns give it an uneven running start: a strong Axis-2 mechanism result, an under-powered Axis-2 magnitude result with a phantom pointer to fix, a thin direct-fusion session that needs a proper powered re-run, and a clean placement negative.

• EXP-S1 — the coupled simulator all four campaigns run on; provides the mechanism the field study confirms.
• EXP-F1 — the ground-truth chain the fusion trains against.
• EXP-F2 — the CSI-only baseline models + the calibration protocol Axis-2 Phase C reuses.
• sim/csi-recalibration-trigger — the sibling "when to recalibrate" sub-experiment; the drift-detection half of the same Hybrid-Fusion story.