ADR-030: RuvSense Persistent Field Model — Longitudinal Drift Detection and Exotic Sensing Tiers

1. Context

1.1 Beyond Pose Estimation

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.

The most exotic capabilities come from this shift in abstraction level:

• The room is the model, not the person
• People are structured perturbations to a baseline
• Changes are deltas from a known state, not raw measurements
• Time is a first-class dimension — the system remembers days, not frames

1.2 The Seven Capability Tiers

1.3 Signals, Not Diagnoses

RF sensing detects biophysical proxies, not medical conditions:

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.

1.4 Acceptance Tests

Tier 0 (ADR-029): Two people, 20 Hz, 10 min stable tracks, zero ID swaps, <30mm torso jitter.

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.

Tier 5-7 (appliance): Thirty-day local run, no camera. Detects meaningful drift with <5% false alarm rate.

2. Decision

2.1 Implement Field Normal Modes as the Foundation

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.

wifi-densepose-signal/src/ruvsense/
├── mod.rs                // (existing, extend)
├── field_model.rs        // NEW: Field normal mode computation + perturbation extraction
├── tomography.rs         // NEW: Coarse RF tomography from link attenuations
├── longitudinal.rs       // NEW: Personal baseline + drift detection
├── intention.rs          // NEW: Pre-movement lead signal detector
├── cross_room.rs         // NEW: Cross-room identity continuity
├── gesture.rs            // NEW: Gesture classification from CSI perturbations
├── adversarial.rs        // NEW: Physically impossible signal detection
└── (existing files...)

2.2 Core Architecture: The Persistent Field Model

                    Time
                     │
                     ▼
    ┌────────────────────────────────┐
    │     Field Normal Modes (Tier 1) │
    │     Room baseline + SVD modes   │
    │     ruvector-solver             │
    └────────────┬───────────────────┘
                 │ Body perturbation (environmental drift removed)
                 │
         ┌───────┴───────┐
         │               │
         ▼               ▼
    ┌──────────┐   ┌──────────────┐
    │ Pose     │   │ RF Tomography│
    │ (ADR-029)│   │ (Tier 2)     │
    │ 20 Hz    │   │ Occupancy vol│
    └────┬─────┘   └──────────────┘
         │
         ▼
    ┌──────────────────────────────┐
    │  AETHER Embedding (ADR-024)  │
    │  128-dim contrastive vector  │
    └────────────┬─────────────────┘
                 │
         ┌───────┼───────┐
         │       │       │
         ▼       ▼       ▼
    ┌────────┐ ┌─────┐ ┌──────────┐
    │Intention│ │Track│ │Cross-Room│
    │Lead    │ │Re-ID│ │Continuity│
    │(Tier 3)│ │     │ │(Tier 5)  │
    └────────┘ └──┬──┘ └──────────┘
                  │
                  ▼
    ┌──────────────────────────────┐
    │  RuVector Longitudinal Memory │
    │  HNSW + graph + Welford stats│
    │  (Tier 4)                     │
    └──────────────┬───────────────┘
                   │
           ┌───────┴───────┐
           │               │
           ▼               ▼
    ┌──────────────┐ ┌──────────────┐
    │ Drift Reports│ │ Adversarial  │
    │ (Level 1-3)  │ │ Detection    │
    │              │ │ (Tier 7)     │
    └──────────────┘ └──────────────┘

2.3 Field Normal Modes (Tier 1)

What it is: The room's electromagnetic eigenstructure — the stable propagation paths, reflection coefficients, and interference patterns when nobody is present.

How it works:

1. During quiet periods (empty room, overnight), collect 10 minutes of CSI across all links
2. Compute per-link baseline (mean CSI vector)
3. Compute environmental variation modes via SVD (temperature, humidity, time-of-day effects)
4. Store top-K modes (K=3-5 typically captures >95% of environmental variance)
5. At runtime: subtract baseline, project out environmental modes, keep body perturbation

pub struct FieldNormalMode {
    pub baseline: Vec<Vec<Complex<f32>>>,      // [n_links × n_subcarriers]
    pub environmental_modes: Vec<Vec<f32>>,    // [n_modes × n_subcarriers]
    pub mode_energies: Vec<f32>,               // eigenvalues
    pub calibrated_at: u64,
    pub geometry_hash: u64,
}

RuVector integration:

• ruvector-solver → Low-rank SVD for mode extraction
• ruvector-temporal-tensor → Compressed baseline history storage
• ruvector-attn-mincut → Identify which subcarriers belong to which mode

2.4 Longitudinal Drift Detection (Tier 4)

The defensible pipeline:

RF → AETHER contrastive embedding
   → RuVector longitudinal memory (HNSW + graph)
     → Coherence-gated drift detection (Welford statistics)
       → Risk flag with traceable evidence

Three monitoring levels:

Storage model:

pub struct PersonalBaseline {
    pub person_id: PersonId,
    pub gait_symmetry: WelfordStats,
    pub stability_index: WelfordStats,
    pub breathing_regularity: WelfordStats,
    pub micro_tremor: WelfordStats,
    pub activity_level: WelfordStats,
    pub embedding_centroid: Vec<f32>,  // [128]
    pub observation_days: u32,
    pub updated_at: u64,
}

RuVector integration:

• ruvector-temporal-tensor → Compressed daily summaries (50-75% memory savings)
• HNSW → Embedding similarity search across longitudinal record
• ruvector-attention → Per-metric drift significance weighting
• ruvector-mincut → Temporal segmentation (detect changepoints in metric series)

2.5 Regulatory Classification

Decision: Start as consumer wellness. Build 12+ months of real-world longitudinal data. The dataset itself becomes the asset for future regulatory submissions.

3. Appliance Product Categories

3.1 Invisible Guardian

Wall-mounted wellness monitor for elderly care and independent living. No camera, no microphone, no reconstructable data. Stores embeddings and structural deltas only.

3.2 Spatial Digital Twin Node

Live electromagnetic room model for smart buildings and workplace analytics.

3.3 RF Interaction Surface

Multi-user gesture interface. No cameras. Works in darkness, smoke, through clothing.

3.4 Pre-Incident Drift Monitor

Longitudinal biomechanics tracker for rehabilitation and occupational health.

3.5 Vertical Recommendation for First Hardware SKU

Invisible Guardian — the elderly care wellness monitor. Rationale:

1. Largest addressable market with immediate revenue (aging population, care facility demand)
2. Lowest regulatory bar (consumer wellness, no diagnostic claims)
3. Privacy advantage over cameras is a selling point, not a limitation
4. 30-day autonomous operation validates all tiers (field model, drift detection, coherence gating)
5. $108-171 BOM allows $299-499 retail with healthy margins

4. RuVector Integration Map (Extended)

All five crates are exercised across the exotic tiers:

5. Implementation Priority

Total exotic tier: ~11 weeks after ADR-029 acceptance test passes.

6. Consequences

6.1 Positive

• Room becomes self-sensing: Field normal modes provide a persistent baseline that explains change as structured deltas
• 7-day autonomous operation: Coherence gating + SONA adaptation + longitudinal memory eliminate manual tuning
• Privacy by design: No images, no audio, no reconstructable data — only embeddings and statistical summaries
• Traceable evidence: Every drift alert links to stored embeddings, timestamps, and graph constraints
• Multiple product categories: Same software stack, different packaging — Guardian, Twin, Interaction, Drift Monitor
• Regulatory clarity: Consumer wellness first, clinical decision support later with accumulated dataset
• Security primitive: Coherence gating detects adversarial injection, not just quality issues

6.2 Negative

• 7-day calibration required for personal baselines (system is less useful during initial period)
• Empty-room calibration needed for field normal modes (may not always be available)
• Storage growth: Longitudinal memory grows ~1 KB/person/day (manageable but non-zero)
• Statistical power: Drift detection requires 14+ days of data for meaningful z-scores
• Multi-room: Cross-room continuity requires hardware in all rooms (cost scales linearly)

6.3 Risks

7. Related ADRs

8. References

1. IEEE 802.11bf-2024. "WLAN Sensing." IEEE Standards Association.
2. FDA. "General Wellness: Policy for Low Risk Devices." Guidance Document, 2019.
3. EU MDR 2017/745. "Medical Device Regulation." Official Journal of the European Union.
4. Welford, B.P. (1962). "Note on a Method for Calculating Corrected Sums of Squares." Technometrics.
5. Chen, L. et al. (2026). "PerceptAlign: Geometry-Aware WiFi Sensing." arXiv:2601.12252.
6. AM-FM (2026). "A Foundation Model for Ambient Intelligence Through WiFi." arXiv:2602.11200.
7. Geng, J. et al. (2023). "DensePose From WiFi." arXiv:2301.00250.