| 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.
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...)
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) │
└──────────────┘ └──────────────┘
What it is: The room's electromagnetic eigenstructure — the stable propagation paths, reflection coefficients, and interference patterns when nobody is present.
How it works:
- During quiet periods (empty room, overnight), collect 10 minutes of CSI across all links
- Compute per-link baseline (mean CSI vector)
- Compute environmental variation modes via SVD (temperature, humidity, time-of-day effects)
- Store top-K modes (K=3-5 typically captures >95% of environmental variance)
- 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 extractionruvector-temporal-tensor→ Compressed baseline history storageruvector-attn-mincut→ Identify which subcarriers belong to which mode
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:
| 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 weightingruvector-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:
- Largest addressable market with immediate revenue (aging population, care facility demand)
- Lowest regulatory bar (consumer wellness, no diagnostic claims)
- Privacy advantage over cameras is a selling point, not a limitation
- 30-day autonomous operation validates all tiers (field model, drift detection, coherence gating)
- $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 |
- IEEE 802.11bf-2024. "WLAN Sensing." IEEE Standards Association.
- FDA. "General Wellness: Policy for Low Risk Devices." Guidance Document, 2019.
- EU MDR 2017/745. "Medical Device Regulation." Official Journal of the European Union.
- Welford, B.P. (1962). "Note on a Method for Calculating Corrected Sums of Squares." Technometrics.
- Chen, L. et al. (2026). "PerceptAlign: Geometry-Aware WiFi Sensing." arXiv:2601.12252.
- AM-FM (2026). "A Foundation Model for Ambient Intelligence Through WiFi." arXiv:2602.11200.
- Geng, J. et al. (2023). "DensePose From WiFi." arXiv:2301.00250.