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+# ADR-015: Public Dataset Strategy for Trained Pose Estimation Model
+
+## Status
+
+Proposed
+
+## Context
+
+The WiFi-DensePose system has a complete model architecture (`DensePoseHead`,
+`ModalityTranslationNetwork`, `WiFiDensePoseRCNN`) and signal processing pipeline,
+but no trained weights. Without a trained model, pose estimation produces random
+outputs regardless of input quality.
+
+Training requires paired data: simultaneous WiFi CSI captures alongside ground-truth
+human pose annotations. Collecting this data from scratch requires months of effort
+and specialized hardware (multiple WiFi nodes + camera + motion capture rig). Several
+public datasets exist that can bootstrap training without custom collection.
+
+### The Teacher-Student Constraint
+
+The CMU "DensePose From WiFi" paper (2023) trains using a teacher-student approach:
+a camera-based RGB pose model (e.g. Detectron2 DensePose) generates pseudo-labels
+during training, so the WiFi model learns to replicate those outputs. At inference,
+the camera is removed. This means any dataset that provides *either* ground-truth
+pose annotations *or* synchronized RGB frames (from which a teacher can generate
+labels) is sufficient for training.
+
+## Decision
+
+Use MM-Fi as the primary training dataset, supplemented by XRF55 for additional
+diversity, with a teacher-student pipeline for any dataset that lacks dense pose
+annotations but provides RGB video.
+
+### Primary Dataset: MM-Fi
+
+**Paper:** "MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless
+Sensing" (NeurIPS 2023 Datasets Track)
+**Repository:** https://github.com/ybCliff/MM-Fi
+**Size:** 40 volunteers × 27 action classes × ~320,000 frames
+**Modalities:** WiFi CSI, mmWave radar, LiDAR, RGB-D, IMU
+**CSI format:** 3 Tx × 3 Rx antennas, 114 subcarriers, 100 Hz sampling rate,
+IEEE 802.11n 5 GHz, raw amplitude + phase
+**Pose annotations:** 17-keypoint COCO skeleton (from RGB-D ground truth)
+**License:** CC BY-NC 4.0
+**Why primary:** Largest public WiFi CSI + pose dataset; raw amplitude and phase
+available (not just processed features); antenna count (3×3) is compatible with the
+existing `CSIProcessor` configuration; COCO keypoints map directly to the
+`KeypointHead` output format.
+
+### Secondary Dataset: XRF55
+
+**Paper:** "XRF55: A Radio-Frequency Dataset for Human Indoor Action Recognition"
+(ACM MM 2023)
+**Repository:** https://github.com/aiotgroup/XRF55
+**Size:** 55 action classes, multiple subjects and environments
+**CSI format:** WiFi CSI + UWB radar, 3 Tx × 3 Rx, 30 subcarriers
+**Pose annotations:** Skeleton keypoints from Kinect
+**License:** Research use
+**Why secondary:** Different environments and action vocabulary increase
+generalization; 30 subcarriers requires subcarrier interpolation to match the
+existing 56-subcarrier config.
+
+### Excluded Datasets and Reasons
+
+| Dataset | Reason for exclusion |
+|---------|---------------------|
+| RF-Pose / RF-Pose3D (MIT) | Uses 60 GHz mmWave, not 2.4/5 GHz WiFi CSI; incompatible signal physics |
+| Person-in-WiFi (CMU 2019) | Amplitude only, no phase; not publicly released |
+| Widar 3.0 | Gesture recognition only, no full-body pose |
+| NTU-Fi | Activity labels only, no pose keypoints |
+| WiPose | Limited release; superseded by MM-Fi |
+
+## Implementation Plan
+
+### Phase 1: MM-Fi Loader
+
+Implement a `PyTorch Dataset` class that:
+- Reads MM-Fi's HDF5/numpy CSI files
+- Resamples from 114 subcarriers → 56 subcarriers (linear interpolation along
+  frequency axis) to match the existing `CSIProcessor` config
+- Normalizes amplitude and unwraps phase using the existing `PhaseSanitizer`
+- Returns `(amplitude, phase, keypoints_17)` tuples
+
+### Phase 2: Teacher-Student Labels
+
+For samples where only skeleton keypoints are available (not full DensePose UV maps):
+- Run Detectron2 DensePose on the paired RGB frames to generate `(part_labels,
+  u_coords, v_coords)` pseudo-labels
+- Cache generated labels to avoid recomputation during training epochs
+- This matches the training procedure in the original CMU paper
+
+### Phase 3: Training Pipeline
+
+- **Loss:** Combined keypoint heatmap loss (MSE) + DensePose part classification
+  (cross-entropy) + UV regression (Smooth L1) + transfer loss against teacher
+  RGB backbone features
+- **Optimizer:** Adam, lr=1e-3, milestones at 48k and 96k steps (paper schedule)
+- **Hardware:** Single GPU (RTX 3090 or A100); MM-Fi fits in ~50 GB disk
+- **Checkpointing:** Save every epoch; keep best-by-validation-PCK
+
+### Phase 4: Evaluation
+
+- **Keypoints:** PCK@0.2 (Percentage of Correct Keypoints within 20% of torso size)
+- **DensePose:** GPS (Geodesic Point Similarity) and GPSM with segmentation mask
+- **Held-out split:** MM-Fi subjects 33-40 (20%) for validation; no test-set leakage
+
+## Subcarrier Mismatch: MM-Fi (114) vs System (56)
+
+MM-Fi captures 114 subcarriers at 5 GHz with 40 MHz bandwidth. The existing system
+is configured for 56 subcarriers. Resolution options in order of preference:
+
+1. **Interpolate MM-Fi → 56** (recommended for initial training): linear interpolation
+   preserves spectral envelope, fast, no architecture change needed
+2. **Reconfigure system → 114**: change `CSIProcessor` config; requires re-running
+   `verify.py --generate-hash` to update proof hash
+3. **Train at native 114, serve at 56**: separate train/inference configs; adds
+   complexity
+
+Option 1 is chosen for Phase 1 to unblock training immediately.
+
+## Consequences
+
+**Positive:**
+- Unblocks end-to-end training without hardware collection
+- MM-Fi's 3×3 antenna setup matches this system's target hardware (ESP32 mesh, ADR-012)
+- 40 subjects with 27 action classes provides reasonable diversity for a first model
+- CC BY-NC license is compatible with research and internal use
+
+**Negative:**
+- CC BY-NC prohibits commercial deployment of weights trained solely on MM-Fi;
+  custom data collection required before commercial release
+- 114→56 subcarrier interpolation loses some frequency resolution; acceptable for
+  initial training, revisit in Phase 2
+- MM-Fi was captured in controlled lab environments; expect accuracy drop in
+  complex real-world deployments until fine-tuned on domain-specific data
+
+## References
+
+- He et al., "MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset" (NeurIPS 2023)
+- Yang et al., "DensePose From WiFi" (arXiv 2301.00250, CMU 2023)
+- ADR-012: ESP32 CSI Sensor Mesh (hardware target)
+- ADR-013: Feature-Level Sensing on Commodity Gear
+- ADR-014: SOTA Signal Processing Algorithms