docs: add RuvSense persistent field model, exotic tiers, and appliance categories
Expands the RuvSense architecture from pose estimation to spatial intelligence platform with persistent electromagnetic world model. Research (Part II added): - 7 exotic capability tiers: field normal modes, RF tomography, intention lead signals, longitudinal biomechanics drift, cross-room continuity, invisible interaction layer, adversarial detection - Signals-not-diagnoses framework with 3 monitoring levels - 5 appliance product categories: Invisible Guardian, Spatial Digital Twin, Collective Behavior Engine, RF Interaction Surface, Pre-Incident Drift Monitor - Regulatory classification (consumer wellness → clinical decision support) - Extended acceptance tests: 7-day autonomous, 30-day appliance validation ADR-030 (new): - Persistent field model architecture with room eigenstructure - Longitudinal drift detection via Welford statistics + HNSW memory - All 5 ruvector crates mapped across 7 exotic tiers - GOAP implementation priority: field modes → drift → tomography → intent - Invisible Guardian recommended as first hardware SKU vertical DDD model (extended): - 3 new bounded contexts: Field Model, Longitudinal Monitoring, Spatial Identity - Full aggregate roots, value objects, domain events for each context - Extended context map showing all 6 bounded contexts - Repository interfaces for field baselines, personal baselines, transitions - Invariants enforcing signals-not-diagnoses boundary https://claude.ai/code/session_01QTX772SDsGVSPnaphoNgNY
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docs/adr/ADR-030-ruvsense-persistent-field-model.md
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# ADR-030: RuvSense Persistent Field Model — Longitudinal Drift Detection and Exotic Sensing Tiers
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| Field | Value |
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|-------|-------|
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| **Status** | Proposed |
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| **Date** | 2026-03-02 |
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| **Deciders** | ruv |
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| **Codename** | **RuvSense Field** — Persistent Electromagnetic World Model |
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| **Relates to** | ADR-029 (RuvSense Multistatic), ADR-005 (SONA Self-Learning), ADR-024 (AETHER Embeddings), ADR-016 (RuVector Integration), ADR-026 (Survivor Track Lifecycle), ADR-027 (MERIDIAN Generalization) |
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---
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## 1. Context
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### 1.1 Beyond Pose Estimation
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ADR-029 establishes RuvSense as a sensing-first multistatic mesh achieving 20 Hz DensePose with <30mm jitter. That treats WiFi as a **momentary pose estimator**. The next leap: treat the electromagnetic field as a **persistent world model** that remembers, predicts, and explains.
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The most exotic capabilities come from this shift in abstraction level:
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- The room is the model, not the person
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- People are structured perturbations to a baseline
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- Changes are deltas from a known state, not raw measurements
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- Time is a first-class dimension — the system remembers days, not frames
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### 1.2 The Seven Capability Tiers
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| Tier | Capability | Foundation |
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|------|-----------|-----------|
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| 1 | **Field Normal Modes** — Room electromagnetic eigenstructure | Baseline calibration + SVD |
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| 2 | **Coarse RF Tomography** — 3D occupancy volume from link attenuations | Sparse tomographic inversion |
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| 3 | **Intention Lead Signals** — Pre-movement prediction (200-500ms lead) | Temporal embedding trajectory analysis |
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| 4 | **Longitudinal Biomechanics Drift** — Personal baseline deviation over days | Welford statistics + HNSW memory |
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| 5 | **Cross-Room Continuity** — Identity persistence across spaces without optics | Environment fingerprinting + transition graph |
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| 6 | **Invisible Interaction Layer** — Multi-user gesture control through walls/darkness | Per-person CSI perturbation classification |
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| 7 | **Adversarial Detection** — Physically impossible signal identification | Multi-link consistency + field model constraints |
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### 1.3 Signals, Not Diagnoses
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RF sensing detects **biophysical proxies**, not medical conditions:
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| Detectable Signal | Not Detectable |
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|-------------------|---------------|
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| Breathing rate variability | COPD diagnosis |
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| Gait asymmetry shift (18% over 14 days) | Parkinson's disease |
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| Posture instability increase | Neurological condition |
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| Micro-tremor onset | Specific tremor etiology |
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| Activity level decline | Depression or pain diagnosis |
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The output is: "Your movement symmetry has shifted 18 percent over 14 days." That is actionable without being diagnostic. The evidence chain (stored embeddings, drift statistics, coherence scores) is fully traceable.
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### 1.4 Acceptance Tests
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**Tier 0 (ADR-029):** Two people, 20 Hz, 10 min stable tracks, zero ID swaps, <30mm torso jitter.
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**Tier 1-4 (this ADR):** Seven-day run, no manual tuning. System flags one real environmental change and one real human drift event, produces traceable explanation using stored embeddings plus graph constraints.
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**Tier 5-7 (appliance):** Thirty-day local run, no camera. Detects meaningful drift with <5% false alarm rate.
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---
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## 2. Decision
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### 2.1 Implement Field Normal Modes as the Foundation
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Add a `field_model` module to `wifi-densepose-signal/src/ruvsense/` that learns the room's electromagnetic baseline during unoccupied periods and decomposes all subsequent observations into environmental drift + body perturbation.
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```
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wifi-densepose-signal/src/ruvsense/
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├── mod.rs // (existing, extend)
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├── field_model.rs // NEW: Field normal mode computation + perturbation extraction
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├── tomography.rs // NEW: Coarse RF tomography from link attenuations
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├── longitudinal.rs // NEW: Personal baseline + drift detection
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├── intention.rs // NEW: Pre-movement lead signal detector
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├── cross_room.rs // NEW: Cross-room identity continuity
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├── gesture.rs // NEW: Gesture classification from CSI perturbations
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├── adversarial.rs // NEW: Physically impossible signal detection
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└── (existing files...)
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```
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### 2.2 Core Architecture: The Persistent Field Model
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```
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Time
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│
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▼
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┌────────────────────────────────┐
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│ Field Normal Modes (Tier 1) │
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│ Room baseline + SVD modes │
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│ ruvector-solver │
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└────────────┬───────────────────┘
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│ Body perturbation (environmental drift removed)
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│
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┌───────┴───────┐
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│ │
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▼ ▼
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┌──────────┐ ┌──────────────┐
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│ Pose │ │ RF Tomography│
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│ (ADR-029)│ │ (Tier 2) │
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│ 20 Hz │ │ Occupancy vol│
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└────┬─────┘ └──────────────┘
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│
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▼
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┌──────────────────────────────┐
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│ AETHER Embedding (ADR-024) │
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│ 128-dim contrastive vector │
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└────────────┬─────────────────┘
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│
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┌───────┼───────┐
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│ │ │
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▼ ▼ ▼
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┌────────┐ ┌─────┐ ┌──────────┐
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│Intention│ │Track│ │Cross-Room│
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│Lead │ │Re-ID│ │Continuity│
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│(Tier 3)│ │ │ │(Tier 5) │
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└────────┘ └──┬──┘ └──────────┘
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│
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▼
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┌──────────────────────────────┐
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│ RuVector Longitudinal Memory │
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│ HNSW + graph + Welford stats│
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│ (Tier 4) │
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└──────────────┬───────────────┘
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│
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┌───────┴───────┐
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│ │
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▼ ▼
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┌──────────────┐ ┌──────────────┐
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│ Drift Reports│ │ Adversarial │
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│ (Level 1-3) │ │ Detection │
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│ │ │ (Tier 7) │
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└──────────────┘ └──────────────┘
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```
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### 2.3 Field Normal Modes (Tier 1)
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**What it is:** The room's electromagnetic eigenstructure — the stable propagation paths, reflection coefficients, and interference patterns when nobody is present.
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**How it works:**
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1. During quiet periods (empty room, overnight), collect 10 minutes of CSI across all links
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2. Compute per-link baseline (mean CSI vector)
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3. Compute environmental variation modes via SVD (temperature, humidity, time-of-day effects)
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4. Store top-K modes (K=3-5 typically captures >95% of environmental variance)
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5. At runtime: subtract baseline, project out environmental modes, keep body perturbation
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```rust
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pub struct FieldNormalMode {
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pub baseline: Vec<Vec<Complex<f32>>>, // [n_links × n_subcarriers]
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pub environmental_modes: Vec<Vec<f32>>, // [n_modes × n_subcarriers]
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pub mode_energies: Vec<f32>, // eigenvalues
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pub calibrated_at: u64,
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pub geometry_hash: u64,
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}
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```
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**RuVector integration:**
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- `ruvector-solver` → Low-rank SVD for mode extraction
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- `ruvector-temporal-tensor` → Compressed baseline history storage
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- `ruvector-attn-mincut` → Identify which subcarriers belong to which mode
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### 2.4 Longitudinal Drift Detection (Tier 4)
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**The defensible pipeline:**
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```
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RF → AETHER contrastive embedding
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→ RuVector longitudinal memory (HNSW + graph)
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→ Coherence-gated drift detection (Welford statistics)
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→ Risk flag with traceable evidence
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```
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**Three monitoring levels:**
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| Level | Signal Type | Example Output |
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|-------|------------|----------------|
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| **1: Physiological** | Raw biophysical metrics | "Breathing rate: 18.3 BPM today, 7-day avg: 16.1" |
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| **2: Drift** | Personal baseline deviation | "Gait symmetry shifted 18% over 14 days" |
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| **3: Risk correlation** | Pattern-matched concern | "Pattern consistent with increased fall risk" |
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**Storage model:**
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```rust
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pub struct PersonalBaseline {
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pub person_id: PersonId,
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pub gait_symmetry: WelfordStats,
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pub stability_index: WelfordStats,
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pub breathing_regularity: WelfordStats,
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pub micro_tremor: WelfordStats,
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pub activity_level: WelfordStats,
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pub embedding_centroid: Vec<f32>, // [128]
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pub observation_days: u32,
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pub updated_at: u64,
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}
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```
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**RuVector integration:**
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- `ruvector-temporal-tensor` → Compressed daily summaries (50-75% memory savings)
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- HNSW → Embedding similarity search across longitudinal record
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- `ruvector-attention` → Per-metric drift significance weighting
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- `ruvector-mincut` → Temporal segmentation (detect changepoints in metric series)
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### 2.5 Regulatory Classification
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| Classification | What You Claim | Regulatory Path |
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|---------------|---------------|-----------------|
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| **Consumer wellness** (recommended first) | Activity metrics, breathing rate, stability score | Self-certification, FCC Part 15 |
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| **Clinical decision support** (future) | Fall risk alert, respiratory pattern concern | FDA Class II 510(k) or De Novo |
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| **Regulated medical device** (requires clinical partner) | Diagnostic claims for specific conditions | FDA Class II/III + clinical trials |
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**Decision: Start as consumer wellness.** Build 12+ months of real-world longitudinal data. The dataset itself becomes the asset for future regulatory submissions.
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---
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## 3. Appliance Product Categories
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### 3.1 Invisible Guardian
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Wall-mounted wellness monitor for elderly care and independent living. No camera, no microphone, no reconstructable data. Stores embeddings and structural deltas only.
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| Spec | Value |
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|------|-------|
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| Nodes | 4 ESP32-S3 pucks per room |
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| Processing | Central hub (RPi 5 or x86) |
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| Power | PoE or USB-C |
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| Output | Risk flags, drift alerts, occupancy timeline |
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| BOM | $73-91 (ESP32 mesh) + $35-80 (hub) |
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| Validation | 30-day autonomous run, <5% false alarm rate |
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### 3.2 Spatial Digital Twin Node
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Live electromagnetic room model for smart buildings and workplace analytics.
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| Spec | Value |
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|------|-------|
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| Output | Occupancy heatmap, flow vectors, dwell time, anomaly events |
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| Integration | MQTT/REST API for BMS and CAFM |
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| Retention | 30-day rolling, GDPR-compliant |
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| Vertical | Smart buildings, retail, workspace optimization |
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### 3.3 RF Interaction Surface
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Multi-user gesture interface. No cameras. Works in darkness, smoke, through clothing.
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| Spec | Value |
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|------|-------|
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| Gestures | Wave, point, beckon, push, circle + custom |
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| Users | Up to 4 simultaneous |
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| Latency | <100ms gesture recognition |
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| Vertical | Smart home, hospitality, accessibility |
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### 3.4 Pre-Incident Drift Monitor
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Longitudinal biomechanics tracker for rehabilitation and occupational health.
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| Spec | Value |
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|------|-------|
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| Baseline | 7-day calibration per person |
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| Alert | Metric drift >2sigma for >3 days |
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| Evidence | Stored embedding trajectory + statistical report |
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| Vertical | Elderly care, rehab, occupational health |
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### 3.5 Vertical Recommendation for First Hardware SKU
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**Invisible Guardian** — the elderly care wellness monitor. Rationale:
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1. Largest addressable market with immediate revenue (aging population, care facility demand)
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2. Lowest regulatory bar (consumer wellness, no diagnostic claims)
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3. Privacy advantage over cameras is a selling point, not a limitation
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4. 30-day autonomous operation validates all tiers (field model, drift detection, coherence gating)
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5. $108-171 BOM allows $299-499 retail with healthy margins
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---
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## 4. RuVector Integration Map (Extended)
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All five crates are exercised across the exotic tiers:
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| Tier | Crate | API | Role |
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|------|-------|-----|------|
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| 1 (Field) | `ruvector-solver` | `NeumannSolver` + SVD | Environmental mode decomposition |
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| 1 (Field) | `ruvector-temporal-tensor` | `TemporalTensorCompressor` | Baseline history storage |
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| 1 (Field) | `ruvector-attn-mincut` | `attn_mincut` | Mode-subcarrier assignment |
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| 2 (Tomo) | `ruvector-solver` | `NeumannSolver` (L1) | Sparse tomographic inversion |
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| 3 (Intent) | `ruvector-attention` | `ScaledDotProductAttention` | Temporal trajectory weighting |
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| 3 (Intent) | `ruvector-temporal-tensor` | `CompressedCsiBuffer` | 2-second embedding history |
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| 4 (Drift) | `ruvector-temporal-tensor` | `TemporalTensorCompressor` | Daily summary compression |
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| 4 (Drift) | `ruvector-attention` | `ScaledDotProductAttention` | Metric drift significance |
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| 4 (Drift) | `ruvector-mincut` | `DynamicMinCut` | Temporal changepoint detection |
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| 5 (Cross-Room) | `ruvector-attention` | HNSW | Room and person fingerprint matching |
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| 5 (Cross-Room) | `ruvector-mincut` | `MinCutBuilder` | Transition graph partitioning |
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| 6 (Gesture) | `ruvector-attention` | `ScaledDotProductAttention` | Gesture template matching |
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| 7 (Adversarial) | `ruvector-solver` | `NeumannSolver` | Physical plausibility verification |
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| 7 (Adversarial) | `ruvector-attn-mincut` | `attn_mincut` | Multi-link consistency check |
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---
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## 5. Implementation Priority
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| Priority | Tier | Module | Weeks | Dependency |
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|----------|------|--------|-------|------------|
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| P0 | 1 | `field_model.rs` | 2 | ADR-029 multistatic mesh operational |
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| P0 | 4 | `longitudinal.rs` | 2 | Tier 1 baseline + AETHER embeddings |
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| P1 | 2 | `tomography.rs` | 1 | Tier 1 perturbation extraction |
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| P1 | 3 | `intention.rs` | 2 | Tier 1 + temporal embedding history |
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| P2 | 5 | `cross_room.rs` | 2 | Tier 4 person profiles + multi-room deployment |
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| P2 | 6 | `gesture.rs` | 1 | Tier 1 perturbation + per-person separation |
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| P3 | 7 | `adversarial.rs` | 1 | Tier 1 field model + multi-link consistency |
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**Total exotic tier: ~11 weeks after ADR-029 acceptance test passes.**
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---
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## 6. Consequences
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### 6.1 Positive
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- **Room becomes self-sensing**: Field normal modes provide a persistent baseline that explains change as structured deltas
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- **7-day autonomous operation**: Coherence gating + SONA adaptation + longitudinal memory eliminate manual tuning
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- **Privacy by design**: No images, no audio, no reconstructable data — only embeddings and statistical summaries
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- **Traceable evidence**: Every drift alert links to stored embeddings, timestamps, and graph constraints
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- **Multiple product categories**: Same software stack, different packaging — Guardian, Twin, Interaction, Drift Monitor
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- **Regulatory clarity**: Consumer wellness first, clinical decision support later with accumulated dataset
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- **Security primitive**: Coherence gating detects adversarial injection, not just quality issues
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### 6.2 Negative
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- **7-day calibration** required for personal baselines (system is less useful during initial period)
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- **Empty-room calibration** needed for field normal modes (may not always be available)
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- **Storage growth**: Longitudinal memory grows ~1 KB/person/day (manageable but non-zero)
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- **Statistical power**: Drift detection requires 14+ days of data for meaningful z-scores
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- **Multi-room**: Cross-room continuity requires hardware in all rooms (cost scales linearly)
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### 6.3 Risks
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| Risk | Probability | Impact | Mitigation |
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|------|-------------|--------|------------|
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| Field modes drift faster than expected | Medium | False perturbation detections | Reduce mode update interval from 24h to 4h |
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| Personal baselines too variable | Medium | High false alarm rate for drift | Widen sigma threshold from 2σ to 3σ; require 5+ days |
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| Cross-room matching fails for similar body types | Low | Identity confusion | Require temporal proximity (<60s) plus spatial adjacency |
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| Gesture recognition insufficient SNR | Medium | <80% accuracy | Restrict to near-field (<2m) initially |
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| Adversarial injection via coordinated WiFi injection | Very Low | Spoofed occupancy | Multi-link consistency check makes single-link spoofing detectable |
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---
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## 7. Related ADRs
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| ADR | Relationship |
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|-----|-------------|
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| ADR-029 | **Prerequisite**: Multistatic mesh is the sensing substrate for all exotic tiers |
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| ADR-005 (SONA) | **Extended**: SONA recalibration triggered by coherence gate → now also by drift events |
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| ADR-016 (RuVector) | **Extended**: All 5 crates exercised across 7 exotic tiers |
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| ADR-024 (AETHER) | **Critical dependency**: Embeddings are the representation for all longitudinal memory |
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| ADR-026 (Tracking) | **Extended**: Track lifecycle now spans days (not minutes) for drift detection |
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| ADR-027 (MERIDIAN) | **Used**: Room geometry encoding for field normal mode conditioning |
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---
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## 8. References
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1. IEEE 802.11bf-2024. "WLAN Sensing." IEEE Standards Association.
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2. FDA. "General Wellness: Policy for Low Risk Devices." Guidance Document, 2019.
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3. EU MDR 2017/745. "Medical Device Regulation." Official Journal of the European Union.
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4. Welford, B.P. (1962). "Note on a Method for Calculating Corrected Sums of Squares." Technometrics.
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5. Chen, L. et al. (2026). "PerceptAlign: Geometry-Aware WiFi Sensing." arXiv:2601.12252.
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6. AM-FM (2026). "A Foundation Model for Ambient Intelligence Through WiFi." arXiv:2602.11200.
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7. Geng, J. et al. (2023). "DensePose From WiFi." arXiv:2301.00250.
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