| Field | Value |

|-------|-------|

| **Status** | Proposed |

| **Date** | 2026-03-02 |

| **Deciders** | ruv |

| **Codename** | **RuvSense Field** — Persistent Electromagnetic World Model |

| **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) |

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

| Tier | Capability | Foundation |

|------|-----------|-----------|

| 1 | **Field Normal Modes** — Room electromagnetic eigenstructure | Baseline calibration + SVD |

| 2 | **Coarse RF Tomography** — 3D occupancy volume from link attenuations | Sparse tomographic inversion |

| 3 | **Intention Lead Signals** — Pre-movement prediction (200-500ms lead) | Temporal embedding trajectory analysis |

| 4 | **Longitudinal Biomechanics Drift** — Personal baseline deviation over days | Welford statistics + HNSW memory |

| 5 | **Cross-Room Continuity** — Identity persistence across spaces without optics | Environment fingerprinting + transition graph |

| 6 | **Invisible Interaction Layer** — Multi-user gesture control through walls/darkness | Per-person CSI perturbation classification |

| 7 | **Adversarial Detection** — Physically impossible signal identification | Multi-link consistency + field model constraints |

RF sensing detects **biophysical proxies**, not medical conditions:

| Detectable Signal | Not Detectable |

|-------------------|---------------|

| Breathing rate variability | COPD diagnosis |

| Gait asymmetry shift (18% over 14 days) | Parkinson's disease |

| Posture instability increase | Neurological condition |

| Micro-tremor onset | Specific tremor etiology |

| Activity level decline | Depression or pain diagnosis |

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.

**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.

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.

**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

**The defensible pipeline:**

**Three monitoring levels:**

| Level | Signal Type | Example Output |

|-------|------------|----------------|

| **1: Physiological** | Raw biophysical metrics | "Breathing rate: 18.3 BPM today, 7-day avg: 16.1" |

| **2: Drift** | Personal baseline deviation | "Gait symmetry shifted 18% over 14 days" |

| **3: Risk correlation** | Pattern-matched concern | "Pattern consistent with increased fall risk" |

**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)

| Classification | What You Claim | Regulatory Path |

|---------------|---------------|-----------------|

| **Consumer wellness** (recommended first) | Activity metrics, breathing rate, stability score | Self-certification, FCC Part 15 |

| **Clinical decision support** (future) | Fall risk alert, respiratory pattern concern | FDA Class II 510(k) or De Novo |

| **Regulated medical device** (requires clinical partner) | Diagnostic claims for specific conditions | FDA Class II/III + clinical trials |

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

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

| Spec | Value |

|------|-------|

| Nodes | 4 ESP32-S3 pucks per room |

| Processing | Central hub (RPi 5 or x86) |

| Power | PoE or USB-C |

| Output | Risk flags, drift alerts, occupancy timeline |

| BOM | $73-91 (ESP32 mesh) + $35-80 (hub) |

| Validation | 30-day autonomous run, <5% false alarm rate |

Live electromagnetic room model for smart buildings and workplace analytics.

| Spec | Value |

|------|-------|

| Output | Occupancy heatmap, flow vectors, dwell time, anomaly events |

| Integration | MQTT/REST API for BMS and CAFM |

| Retention | 30-day rolling, GDPR-compliant |

| Vertical | Smart buildings, retail, workspace optimization |

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

| Spec | Value |

|------|-------|

| Gestures | Wave, point, beckon, push, circle + custom |

| Users | Up to 4 simultaneous |

| Latency | <100ms gesture recognition |

| Vertical | Smart home, hospitality, accessibility |

Longitudinal biomechanics tracker for rehabilitation and occupational health.

| Spec | Value |

|------|-------|

| Baseline | 7-day calibration per person |

| Alert | Metric drift >2sigma for >3 days |

| Evidence | Stored embedding trajectory + statistical report |

| Vertical | Elderly care, rehab, occupational health |

**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

All five crates are exercised across the exotic tiers:

| Tier | Crate | API | Role |

|------|-------|-----|------|

| 1 (Field) | `ruvector-solver` | `NeumannSolver` + SVD | Environmental mode decomposition |

| 1 (Field) | `ruvector-temporal-tensor` | `TemporalTensorCompressor` | Baseline history storage |

| 1 (Field) | `ruvector-attn-mincut` | `attn_mincut` | Mode-subcarrier assignment |

| 2 (Tomo) | `ruvector-solver` | `NeumannSolver` (L1) | Sparse tomographic inversion |

| 3 (Intent) | `ruvector-attention` | `ScaledDotProductAttention` | Temporal trajectory weighting |

| 3 (Intent) | `ruvector-temporal-tensor` | `CompressedCsiBuffer` | 2-second embedding history |

| 4 (Drift) | `ruvector-temporal-tensor` | `TemporalTensorCompressor` | Daily summary compression |

| 4 (Drift) | `ruvector-attention` | `ScaledDotProductAttention` | Metric drift significance |

| 4 (Drift) | `ruvector-mincut` | `DynamicMinCut` | Temporal changepoint detection |

| 5 (Cross-Room) | `ruvector-attention` | HNSW | Room and person fingerprint matching |

| 5 (Cross-Room) | `ruvector-mincut` | `MinCutBuilder` | Transition graph partitioning |

| 6 (Gesture) | `ruvector-attention` | `ScaledDotProductAttention` | Gesture template matching |

| 7 (Adversarial) | `ruvector-solver` | `NeumannSolver` | Physical plausibility verification |

| 7 (Adversarial) | `ruvector-attn-mincut` | `attn_mincut` | Multi-link consistency check |

| Priority | Tier | Module | Weeks | Dependency |

|----------|------|--------|-------|------------|

| P0 | 1 | `field_model.rs` | 2 | ADR-029 multistatic mesh operational |

| P0 | 4 | `longitudinal.rs` | 2 | Tier 1 baseline + AETHER embeddings |

| P1 | 2 | `tomography.rs` | 1 | Tier 1 perturbation extraction |

| P1 | 3 | `intention.rs` | 2 | Tier 1 + temporal embedding history |

| P2 | 5 | `cross_room.rs` | 2 | Tier 4 person profiles + multi-room deployment |

| P2 | 6 | `gesture.rs` | 1 | Tier 1 perturbation + per-person separation |

| P3 | 7 | `adversarial.rs` | 1 | Tier 1 field model + multi-link consistency |

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

- **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

- **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)

| Risk | Probability | Impact | Mitigation |

|------|-------------|--------|------------|

| Field modes drift faster than expected | Medium | False perturbation detections | Reduce mode update interval from 24h to 4h |

| Personal baselines too variable | Medium | High false alarm rate for drift | Widen sigma threshold from 2σ to 3σ; require 5+ days |

| Cross-room matching fails for similar body types | Low | Identity confusion | Require temporal proximity (<60s) plus spatial adjacency |

| Gesture recognition insufficient SNR | Medium | <80% accuracy | Restrict to near-field (<2m) initially |

| Adversarial injection via coordinated WiFi injection | Very Low | Spoofed occupancy | Multi-link consistency check makes single-link spoofing detectable |

| ADR | Relationship |

|-----|-------------|

| ADR-029 | **Prerequisite**: Multistatic mesh is the sensing substrate for all exotic tiers |

| ADR-005 (SONA) | **Extended**: SONA recalibration triggered by coherence gate → now also by drift events |

| ADR-016 (RuVector) | **Extended**: All 5 crates exercised across 7 exotic tiers |

| ADR-024 (AETHER) | **Critical dependency**: Embeddings are the representation for all longitudinal memory |

| ADR-026 (Tracking) | **Extended**: Track lifecycle now spans days (not minutes) for drift detection |

| ADR-027 (MERIDIAN) | **Used**: Room geometry encoding for field normal mode conditioning |

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.