- **Status**: Proposed

- **Date**: 2026-03-02

- **Issue**: [#97](https://github.com/ruvnet/wifi-densepose/issues/97)

- **Deciders**: @ruvnet

- **Supersedes**: None

- **Related**: ADR-014 (SOTA signal processing), ADR-024 (AETHER re-ID), ADR-029 (multistatic sensing), ADR-036 (RVF training pipeline)

The current signal-derived pose estimation pipeline (`derive_pose_from_sensing()` in the sensing server) generates at most one skeleton per frame from aggregate CSI features. When multiple people are present, only a single blended skeleton is produced. Live testing with ESP32 hardware confirmed: 2 people in the room yields 1 detected person.

A single ESP32 node provides 1 TX × 1 RX × 56 subcarriers of CSI data per frame. While this is limited spatial resolution compared to camera-based systems, the signal contains composite reflections from all scatterers in the environment. The challenge is decomposing these composite signals into per-person contributions.

Implement multi-person pose detection in four phases, progressively improving accuracy from heuristic to neural approaches.

Estimate occupancy count from CSI signal statistics without decomposition.

**Approach**: Eigenvalue analysis of the CSI covariance matrix across subcarriers.

- Compute the 56×56 covariance matrix of CSI amplitudes over a sliding window (e.g., 50 frames / 5 seconds)

- Count eigenvalues above a noise threshold — each significant eigenvalue corresponds to an independent scatterer (person or static object)

- Subtract the static environment baseline (estimated during calibration or from the field model's SVD eigenstructure)

- The residual significant eigenvalue count estimates person count

**Accuracy target**: > 80% for 0-3 people with single ESP32 node.

**Integration point**: `signal/src/ruvsense/field_model.rs` already computes SVD eigenstructure. Extend with a `estimate_occupancy()` method.

Separate per-person signal contributions using blind source separation.

**Approach**: Non-negative Matrix Factorization (NMF) on the CSI spectrogram.

- Construct a time-frequency matrix from CSI amplitudes: rows = subcarriers (56), columns = time frames

- Apply NMF with k components (k = estimated person count from Phase 1)

- Each component's frequency profile maps to a person's motion pattern

- NMF is preferred over ICA because CSI amplitudes are non-negative

**Alternative**: Independent Component Analysis (ICA) on complex CSI (amplitude + phase). More powerful but requires phase calibration (see `ruvsense/phase_align.rs`).

**Integration point**: New module `signal/src/ruvsense/separation.rs`.

Generate distinct pose skeletons per decomposed component.

**Approach**: Per-component feature extraction → per-person skeleton synthesis.

- Extract motion features (dominant frequency, energy, spectral centroid) per NMF component

- Map each component to a spatial position using subcarrier phase gradient (Fresnel zone model)

- Generate 17-keypoint COCO skeleton per person with position offset

- Assign person IDs using the existing Kalman tracker (`ruvsense/pose_tracker.rs`) with AETHER re-ID embeddings (ADR-024)

**Integration point**: Modify `derive_pose_from_sensing()` in `sensing-server/src/main.rs` to return `Vec<Person>` with length > 1.

Train a dedicated multi-person model using the RVF pipeline (ADR-036).

- Use MM-Fi dataset (ADR-015) multi-person scenarios for training data

- Architecture: shared CSI encoder → person count head + per-person pose heads

- LoRA fine-tuning profile for multi-person specialization

- Inference via the model manager in the sensing server

**Accuracy target**: [email protected] > 60% for 2-person scenarios.

- Enables room occupancy counting (Phase 1 alone is useful)

- Distinct pose tracking per person enables activity recognition per individual

- Progressive approach — each phase delivers incremental value

- Reuses existing infrastructure (field model SVD, Kalman tracker, AETHER, RVF pipeline)

- Single ESP32 node has fundamental spatial resolution limits — separating 2 people standing close together (< 0.5m) will be unreliable

- NMF decomposition adds ~5-10ms latency per frame

- Person count estimation will have false positives from large moving objects (pets, fans)

- Phase 4 neural model requires multi-person training data collection

- Multi-node multistatic mesh (ADR-029) dramatically improves multi-person separation but is a separate effort

- UI already supports multi-person rendering — no frontend changes needed for the `persons[]` array

| Component | Phase | Change |

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

| `signal/src/ruvsense/field_model.rs` | 1 | Add `estimate_occupancy()` |

| `signal/src/ruvsense/separation.rs` | 2 | New module: NMF decomposition |

| `sensing-server/src/main.rs` | 3 | `derive_pose_from_sensing()` multi-person output |

| `signal/src/ruvsense/pose_tracker.rs` | 3 | Multi-target tracking |

| `nn/` | 4 | Multi-person inference head |

| `train/` | 4 | Multi-person training pipeline |

| Operation | Budget | Phase |

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

| Person count estimation | < 2ms | 1 |

| NMF decomposition (k=3) | < 10ms | 2 |

| Multi-skeleton synthesis | < 3ms | 3 |

| Neural inference (multi-person) | < 50ms | 4 |

| **Total pipeline** | **< 65ms** (15 FPS) | All |

1. **Camera fusion**: Use a camera for person detection and WiFi for pose — rejected because the project goal is camera-free sensing.

2. **Multiple single-person models**: Run N independent pose estimators — rejected because they would produce correlated outputs from the same CSI data.

3. **Spatial filtering (beamforming)**: Use antenna array beamforming to isolate directions — rejected because single ESP32 has only 1 antenna; viable with multistatic mesh (ADR-029).

4. **Skip signal-derived, go straight to neural**: Train an end-to-end multi-person model — rejected because signal-derived provides faster iteration and interpretability for the early phases.