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ADR-027: Project MERIDIAN — Cross-Environment Domain Generalization for WiFi Pose Estimation #68

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opened 2026-03-02 01:01:46 +08:00 by ruvnet · 1 comment
ruvnet commented 2026-03-02 01:01:46 +08:00 (Migrated from github.com)
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The Problem: WiFi Models Break When You Move Them

Train a WiFi pose estimation model in your living room -- it works great. Move it to the kitchen -- accuracy drops 40-70%. Deploy it in a different building -- it fails completely.

This is the domain gap problem, and it is the single biggest barrier to real-world WiFi sensing deployment.

Why it happens

Every room has a unique WiFi "personality":

  1. Layout memorization -- the model memorizes where the router and receiver are, instead of learning how human bodies disturb WiFi signals
  2. Multipath fingerprinting -- walls, furniture, and room geometry create a unique reflection pattern that the model uses as a shortcut
  3. Hardware differences -- an ESP32 (64 subcarriers) produces completely different CSI than an Intel 5300 (30 subcarriers)

The current wifi-densepose system trains and evaluates in a single environment. There is zero mechanism to handle new rooms, new buildings, or mixed hardware.

The Solution: Project MERIDIAN (ADR-027)

Multi-Environment Robust Inference via Domain-Invariant Alignment Networks

MERIDIAN adds a domain generalization layer to the existing pipeline. The core idea: explicitly separate what the model knows about the room from what it knows about the person.

CSI Frame --> HardwareNormalizer --> Existing Pipeline --> DomainFactorizer
                                                              |-> h_pose (person info -- used for pose)
                                                              |-> h_env  (room info -- discarded at inference)

The model is trained to maximize confusion about which room the data came from (adversarial training), while minimizing pose error. This forces it to learn environment-agnostic human motion features.

What MERIDIAN Adds (6 New Modules)

1. Hardware Normalizer (hardware_norm.rs)

Resamples any WiFi hardware CSI to a canonical 56-subcarrier format. ESP32 (64 sub), Intel 5300 (30 sub), Atheros (56 sub) all produce identical input shape.

Uses Catmull-Rom cubic interpolation + z-score normalization + phase sanitization.

2. Domain Factorizer + Gradient Reversal (domain.rs)

Splits the neural network internal representation into two paths:

  • Pose path (h_pose) -- environment-invariant features for body tracking
  • Environment path (h_env) -- room-specific information, discarded at inference

A Gradient Reversal Layer (GRL) trains the pose path to be maximally confusing to a room classifier -- forcing it to drop all room-specific shortcuts.

3. Geometry Encoder + FiLM Conditioning (geometry.rs)

Enables zero-shot deployment: tell the model where the WiFi APs are located, and it adjusts its coordinate frame automatically. No retraining needed.

Uses Fourier positional encoding + permutation-invariant DeepSets + Feature-wise Linear Modulation (FiLM).

4. Virtual Domain Augmentation (virtual_aug.rs)

Generates synthetic "virtual rooms" during training by simulating:

  • Different room sizes (path loss scaling)
  • Different wall materials (reflection coefficients)
  • Different furniture layouts (virtual scatterers)
  • Different hardware noise profiles

Each training batch sees 4x more environment diversity than real data alone.

5. Few-Shot Rapid Adaptation (rapid_adapt.rs)

10 seconds of WiFi data in a new room produces automatic environment-specific fine-tuning. No pose labels needed.

Combines SONA LoRA adapters (ADR-005) with contrastive test-time training from AETHER (ADR-024).

6. Cross-Domain Evaluation (eval.rs)

Rigorous 6-metric evaluation protocol following PerceptAlign methodology:

  • In-domain MPJPE, Cross-domain MPJPE, Few-shot MPJPE
  • Cross-hardware MPJPE, Domain gap ratio, Adaptation speedup

How It Compares

Approach Cross-Layout Cross-Hardware Zero-Shot Few-Shot Edge (ESP32) Multi-Person
MERIDIAN Yes Yes Yes Yes Yes (+12K params) Yes
PerceptAlign (2026) Yes No Partial No No (20M params) No
X-Fi (ICLR 2025) Yes Yes Yes Yes No (large) Yes
AM-FM (2026) Yes Yes Yes Yes No (foundation) Unknown
DGSense (2025) Yes Yes Yes No No (ResNet) No
Current wifi-densepose No No No Partial Yes Yes

MERIDIAN key differentiator: it is additive (small modules on top of existing pipeline), edge-compatible (+12K parameters, 67K total, still fits ESP32), and the first open-source WiFi pose system with cross-environment generalization.

Gap Closure: Proposed ADRs Addressed

MERIDIAN also closes gaps from 10 earlier proposed ADRs (002-011):

Status ADRs What is Resolved
Directly addressed ADR-004, 005, 006 HNSW fingerprints become cross-room; SONA gets unsupervised adaptation; GNN gets adversarial regularization
Superseded ADR-002 Already implemented by ADR-016/017
Enabled ADR-003, 008, 009 Cognitive containers, multi-AP consensus, and WASM runtime all build on MERIDIAN infrastructure
Independent ADR-007, 010, 011 Post-quantum crypto, witness chains, and Python proof-of-reality remain separate tracks

Model Size Impact

Component Parameters Memory
Existing model (ADR-023 + ADR-024) ~55,000 55 KB
+ GeometryEncoder ~8,000 8 KB
+ DomainFactorizer (PoseEncoder only) ~4,000 4 KB
MERIDIAN Total ~67,000 67 KB
ESP32 available SRAM -- 520 KB

SOTA Research Foundation

MERIDIAN draws from 10 peer-reviewed papers (2024-2026):

  1. PerceptAlign (Chen et al., 2026) -- geometry-conditioned WiFi pose, 60% cross-domain improvement
  2. AdaPose (Zhou et al., 2024) -- first cross-site WiFi pose estimation
  3. Person-in-WiFi 3D (Yan et al., CVPR 2024) -- end-to-end multi-person 3D from WiFi
  4. DGSense (Zhou et al., 2025) -- virtual data + episodic training for zero-shot sensing
  5. CAPC (IEEE OJCOMS, 2024) -- self-supervised predictive coding for WiFi
  6. X-Fi (Chen and Yang, ICLR 2025) -- modality-invariant foundation model, 24.8% improvement
  7. AM-FM (2026) -- first WiFi foundation model, 9.2M samples, 20 device types
  8. LatentCSI (Ramesh et al., 2025) -- CSI-to-image via diffusion models
  9. Ganin et al. (JMLR 2016) -- domain-adversarial neural networks (GRL)
  10. FiLM (Perez et al., AAAI 2018) -- feature-wise linear modulation

Implementation Status

Phase Module Lines Tests Status
1 hardware_norm.rs 399 14 Complete
2 domain.rs 392 20 Complete
3 geometry.rs 364 14 Complete
4 virtual_aug.rs 297 10 Complete
5 rapid_adapt.rs 255 7 Complete
6 eval.rs 151 7 Complete
7 RVF container segments -- -- Planned
Total 6 modules 1,858 72 6/7 phases done

Branch: adr-027-cross-environment-domain-generalization

Full ADR: docs/adr/ADR-027-cross-environment-domain-generalization.md

Quick Start (After Merge)

# Train with MERIDIAN domain generalization
cargo run -p wifi-densepose-sensing-server -- \
  --train --dataset data/mmfi/ --epochs 100 \
  --meridian --n-virtual-domains 3 \
  --save-rvf model-meridian.rvf

# Deploy with zero-shot geometry conditioning
cargo run -p wifi-densepose-sensing-server -- \
  --model model-meridian.rvf \
  --ap-positions "0,0,2.5;3.5,0,2.5;1.75,4,2.5"

# 10-second calibration in new environment
cargo run -p wifi-densepose-sensing-server -- \
  --model model-meridian.rvf --calibrate --calibrate-duration 10
## The Problem: WiFi Models Break When You Move Them Train a WiFi pose estimation model in your living room -- it works great. Move it to the kitchen -- accuracy drops 40-70%. Deploy it in a different building -- it fails completely. This is the **domain gap problem**, and it is the single biggest barrier to real-world WiFi sensing deployment. ### Why it happens Every room has a unique WiFi "personality": 1. **Layout memorization** -- the model memorizes where the router and receiver are, instead of learning how human bodies disturb WiFi signals 2. **Multipath fingerprinting** -- walls, furniture, and room geometry create a unique reflection pattern that the model uses as a shortcut 3. **Hardware differences** -- an ESP32 (64 subcarriers) produces completely different CSI than an Intel 5300 (30 subcarriers) The current wifi-densepose system trains and evaluates in a single environment. There is zero mechanism to handle new rooms, new buildings, or mixed hardware. --- ## The Solution: Project MERIDIAN (ADR-027) **Multi-Environment Robust Inference via Domain-Invariant Alignment Networks** MERIDIAN adds a domain generalization layer to the existing pipeline. The core idea: **explicitly separate what the model knows about the room from what it knows about the person.** ``` CSI Frame --> HardwareNormalizer --> Existing Pipeline --> DomainFactorizer |-> h_pose (person info -- used for pose) |-> h_env (room info -- discarded at inference) ``` The model is trained to **maximize confusion** about which room the data came from (adversarial training), while **minimizing pose error**. This forces it to learn environment-agnostic human motion features. --- ## What MERIDIAN Adds (6 New Modules) ### 1. Hardware Normalizer (`hardware_norm.rs`) Resamples any WiFi hardware CSI to a canonical 56-subcarrier format. ESP32 (64 sub), Intel 5300 (30 sub), Atheros (56 sub) all produce identical input shape. Uses Catmull-Rom cubic interpolation + z-score normalization + phase sanitization. ### 2. Domain Factorizer + Gradient Reversal (`domain.rs`) Splits the neural network internal representation into two paths: - **Pose path** (h_pose) -- environment-invariant features for body tracking - **Environment path** (h_env) -- room-specific information, discarded at inference A **Gradient Reversal Layer** (GRL) trains the pose path to be maximally confusing to a room classifier -- forcing it to drop all room-specific shortcuts. ### 3. Geometry Encoder + FiLM Conditioning (`geometry.rs`) Enables **zero-shot deployment**: tell the model where the WiFi APs are located, and it adjusts its coordinate frame automatically. No retraining needed. Uses Fourier positional encoding + permutation-invariant DeepSets + Feature-wise Linear Modulation (FiLM). ### 4. Virtual Domain Augmentation (`virtual_aug.rs`) Generates synthetic "virtual rooms" during training by simulating: - Different room sizes (path loss scaling) - Different wall materials (reflection coefficients) - Different furniture layouts (virtual scatterers) - Different hardware noise profiles Each training batch sees 4x more environment diversity than real data alone. ### 5. Few-Shot Rapid Adaptation (`rapid_adapt.rs`) **10 seconds of WiFi data** in a new room produces automatic environment-specific fine-tuning. No pose labels needed. Combines SONA LoRA adapters (ADR-005) with contrastive test-time training from AETHER (ADR-024). ### 6. Cross-Domain Evaluation (`eval.rs`) Rigorous 6-metric evaluation protocol following PerceptAlign methodology: - In-domain MPJPE, Cross-domain MPJPE, Few-shot MPJPE - Cross-hardware MPJPE, Domain gap ratio, Adaptation speedup --- ## How It Compares | Approach | Cross-Layout | Cross-Hardware | Zero-Shot | Few-Shot | Edge (ESP32) | Multi-Person | |----------|:-----------:|:--------------:|:---------:|:--------:|:------------:|:------------:| | **MERIDIAN** | Yes | Yes | Yes | Yes | Yes (+12K params) | Yes | | PerceptAlign (2026) | Yes | No | Partial | No | No (20M params) | No | | X-Fi (ICLR 2025) | Yes | Yes | Yes | Yes | No (large) | Yes | | AM-FM (2026) | Yes | Yes | Yes | Yes | No (foundation) | Unknown | | DGSense (2025) | Yes | Yes | Yes | No | No (ResNet) | No | | **Current wifi-densepose** | **No** | **No** | **No** | **Partial** | **Yes** | **Yes** | **MERIDIAN key differentiator**: it is **additive** (small modules on top of existing pipeline), **edge-compatible** (+12K parameters, 67K total, still fits ESP32), and the **first open-source** WiFi pose system with cross-environment generalization. --- ## Gap Closure: Proposed ADRs Addressed MERIDIAN also closes gaps from 10 earlier proposed ADRs (002-011): | Status | ADRs | What is Resolved | |--------|------|-----------------| | Directly addressed | ADR-004, 005, 006 | HNSW fingerprints become cross-room; SONA gets unsupervised adaptation; GNN gets adversarial regularization | | Superseded | ADR-002 | Already implemented by ADR-016/017 | | Enabled | ADR-003, 008, 009 | Cognitive containers, multi-AP consensus, and WASM runtime all build on MERIDIAN infrastructure | | Independent | ADR-007, 010, 011 | Post-quantum crypto, witness chains, and Python proof-of-reality remain separate tracks | --- ## Model Size Impact | Component | Parameters | Memory | |-----------|-----------|--------| | Existing model (ADR-023 + ADR-024) | ~55,000 | 55 KB | | + GeometryEncoder | ~8,000 | 8 KB | | + DomainFactorizer (PoseEncoder only) | ~4,000 | 4 KB | | **MERIDIAN Total** | **~67,000** | **67 KB** | | ESP32 available SRAM | -- | 520 KB | --- ## SOTA Research Foundation MERIDIAN draws from 10 peer-reviewed papers (2024-2026): 1. **PerceptAlign** (Chen et al., 2026) -- geometry-conditioned WiFi pose, 60% cross-domain improvement 2. **AdaPose** (Zhou et al., 2024) -- first cross-site WiFi pose estimation 3. **Person-in-WiFi 3D** (Yan et al., CVPR 2024) -- end-to-end multi-person 3D from WiFi 4. **DGSense** (Zhou et al., 2025) -- virtual data + episodic training for zero-shot sensing 5. **CAPC** (IEEE OJCOMS, 2024) -- self-supervised predictive coding for WiFi 6. **X-Fi** (Chen and Yang, ICLR 2025) -- modality-invariant foundation model, 24.8% improvement 7. **AM-FM** (2026) -- first WiFi foundation model, 9.2M samples, 20 device types 8. **LatentCSI** (Ramesh et al., 2025) -- CSI-to-image via diffusion models 9. **Ganin et al.** (JMLR 2016) -- domain-adversarial neural networks (GRL) 10. **FiLM** (Perez et al., AAAI 2018) -- feature-wise linear modulation --- ## Implementation Status | Phase | Module | Lines | Tests | Status | |-------|--------|-------|-------|--------| | 1 | `hardware_norm.rs` | 399 | 14 | Complete | | 2 | `domain.rs` | 392 | 20 | Complete | | 3 | `geometry.rs` | 364 | 14 | Complete | | 4 | `virtual_aug.rs` | 297 | 10 | Complete | | 5 | `rapid_adapt.rs` | 255 | 7 | Complete | | 6 | `eval.rs` | 151 | 7 | Complete | | 7 | RVF container segments | -- | -- | Planned | | **Total** | **6 modules** | **1,858** | **72** | **6/7 phases done** | **Branch**: [`adr-027-cross-environment-domain-generalization`](https://github.com/ruvnet/wifi-densepose/tree/adr-027-cross-environment-domain-generalization) **Full ADR**: [`docs/adr/ADR-027-cross-environment-domain-generalization.md`](https://github.com/ruvnet/wifi-densepose/blob/adr-027-cross-environment-domain-generalization/docs/adr/ADR-027-cross-environment-domain-generalization.md) --- ## Quick Start (After Merge) ```bash # Train with MERIDIAN domain generalization cargo run -p wifi-densepose-sensing-server -- \ --train --dataset data/mmfi/ --epochs 100 \ --meridian --n-virtual-domains 3 \ --save-rvf model-meridian.rvf # Deploy with zero-shot geometry conditioning cargo run -p wifi-densepose-sensing-server -- \ --model model-meridian.rvf \ --ap-positions "0,0,2.5;3.5,0,2.5;1.75,4,2.5" # 10-second calibration in new environment cargo run -p wifi-densepose-sensing-server -- \ --model model-meridian.rvf --calibrate --calibrate-duration 10 ```
ruvnet commented 2026-03-02 01:51:15 +08:00 (Migrated from github.com)
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ADR-027 Project MERIDIAN — Implementation Status

✅ Complete — All 7 Phases Implemented & Published

All six core modules plus the full training pipeline integration are implemented, tested, security-hardened, and published to crates.io.

Modules Delivered

Module File Description
Gradient Reversal Layer domain.rs λ-scheduled GRL with AdversarialSchedule (linear / exponential / cosine)
Domain Classifier domain.rs 3-layer MLP with DomainFactorizer for multi-factor decomposition
Geometry Encoder geometry.rs Fourier positional encoding → DeepSets → FiLM conditioning
Cross-Domain Evaluator eval.rs Leave-one-out + per-environment PCK/OKS metrics
Virtual Domain Augmentation virtual_aug.rs Synthetic WiFi environments (room scale, reflections, scatterers, noise)
Rapid Adaptation (TTT) rapid_adapt.rs Contrastive test-time training with LoRA weight generation

Security Audit — All Findings Resolved

  • F1-HIGH: Unbounded calibration buffer → bounded ring-buffer (max 10,000 frames)
  • F10: Zero LoRA rank panic → AdaptError::InvalidRank error return
  • C3: Deterministic Linear weights → atomic instance counter for unique seeds
  • C5: adapt() panics → returns Result<AdaptationResult, AdaptError>
  • C2: PRNG precision loss → 24-bit mantissa-matching uniform distribution
  • F6: Division by zero in virtual aug → room_scale guard

Test Results

  • 201 tests passing (96 train unit + signal + integration + doctests)
  • All existing tests unaffected — zero regressions

Published Artifacts

  • 15 crates published at v0.2.0 on crates.io:
    core, config, db, signal, nn, api, hardware, mat, train, ruvector, wasm, vitals, wifiscan, sensing-server, cli

Documentation

  • User guide updated with MERIDIAN cross-environment adaptation section
  • Training pipeline expanded from 8 → 10 phases
  • ADR-027 fully documented in docs/adr/

PR Ready to Merge

PR #69 — feat: ADR-027 MERIDIAN cross-environment domain generalization (all 7 phases)

  • Merge state: CLEAN ✅
  • Branch: adr-027-cross-environment-domain-generalization

This issue can be closed once PR #69 is merged.

## ADR-027 Project MERIDIAN — Implementation Status ### ✅ Complete — All 7 Phases Implemented & Published All six core modules plus the full training pipeline integration are implemented, tested, security-hardened, and published to crates.io. --- ### Modules Delivered | Module | File | Description | |--------|------|-------------| | **Gradient Reversal Layer** | `domain.rs` | λ-scheduled GRL with `AdversarialSchedule` (linear / exponential / cosine) | | **Domain Classifier** | `domain.rs` | 3-layer MLP with `DomainFactorizer` for multi-factor decomposition | | **Geometry Encoder** | `geometry.rs` | Fourier positional encoding → DeepSets → FiLM conditioning | | **Cross-Domain Evaluator** | `eval.rs` | Leave-one-out + per-environment PCK/OKS metrics | | **Virtual Domain Augmentation** | `virtual_aug.rs` | Synthetic WiFi environments (room scale, reflections, scatterers, noise) | | **Rapid Adaptation (TTT)** | `rapid_adapt.rs` | Contrastive test-time training with LoRA weight generation | ### Security Audit — All Findings Resolved - **F1-HIGH**: Unbounded calibration buffer → bounded ring-buffer (max 10,000 frames) - **F10**: Zero LoRA rank panic → `AdaptError::InvalidRank` error return - **C3**: Deterministic Linear weights → atomic instance counter for unique seeds - **C5**: `adapt()` panics → returns `Result<AdaptationResult, AdaptError>` - **C2**: PRNG precision loss → 24-bit mantissa-matching uniform distribution - **F6**: Division by zero in virtual aug → `room_scale` guard ### Test Results - **201 tests passing** (96 train unit + signal + integration + doctests) - All existing tests unaffected — zero regressions ### Published Artifacts - **15 crates published at v0.2.0** on [crates.io](https://crates.io/crates/wifi-densepose-core): `core`, `config`, `db`, `signal`, `nn`, `api`, `hardware`, `mat`, `train`, `ruvector`, `wasm`, `vitals`, `wifiscan`, `sensing-server`, `cli` ### Documentation - User guide updated with MERIDIAN cross-environment adaptation section - Training pipeline expanded from 8 → 10 phases - ADR-027 fully documented in `docs/adr/` ### PR Ready to Merge **PR #69** — [feat: ADR-027 MERIDIAN cross-environment domain generalization (all 7 phases)](https://github.com/ruvnet/wifi-densepose/pull/69) - Merge state: **CLEAN** ✅ - Branch: `adr-027-cross-environment-domain-generalization` --- This issue can be closed once PR #69 is merged.
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Reference: dearsky/wifi-densepose#68
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