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docs: update ADR-079 with validated hardware, ruvector optimizations, baseline
- Status: Proposed → Accepted - Add O6-O10 optimizations (subcarrier selection, attention, Stoer-Wagner min-cut, multi-SPSA, Mac M4 Pro training via Tailscale) - Add validated hardware table (Mac camera, MediaPipe, M4 Pro GPU, Tailscale) - Add baseline benchmark results (PCK@20: 35.3%) - Update implementation plan with completion status Co-Authored-By: claude-flow <ruv@ruv.net>
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# ADR-079: Camera Ground-Truth Training Pipeline
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- **Status**: Proposed
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- **Status**: Accepted
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- **Date**: 2026-04-06
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- **Deciders**: ruv
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- **Relates to**: ADR-072 (WiFlow Architecture), ADR-070 (Self-Supervised Pretraining), ADR-071 (ruvllm Training Pipeline), ADR-024 (AETHER Contrastive), ADR-064 (Multimodal Ambient Intelligence)
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- **Relates to**: ADR-072 (WiFlow Architecture), ADR-070 (Self-Supervised Pretraining), ADR-071 (ruvllm Training Pipeline), ADR-024 (AETHER Contrastive), ADR-064 (Multimodal Ambient Intelligence), ADR-075 (MinCut Person Separation)
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## Context
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@ -302,6 +302,74 @@ Identify which poses the model is worst at and collect more data for those:
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Expected: 2-3 active learning iterations reach saturation.
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#### O6: Subcarrier Selection (ruvector-solver)
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Variance-based top-K subcarrier selection, equivalent to ruvector-solver's sparse
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interpolation (114→56). Removes noise/static subcarriers before training:
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```
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For each subcarrier d in [0, dim):
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variance[d] = mean over samples of temporal_variance(csi[d, :])
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Select top-K by variance (K = dim * 0.5)
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```
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**Validated:** 128 → 56 subcarriers (56% input reduction), proportional model size reduction.
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#### O7: Attention-Weighted Subcarriers (ruvector-attention)
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Compute per-subcarrier attention weights based on temporal energy correlation with
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ground-truth keypoint motion. High-energy subcarriers that covary with skeleton
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movement get amplified:
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```
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For each subcarrier d:
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energy[d] = sum of squared first-differences over time
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weight[d] = softmax(energy, temperature=0.1)
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Apply: csi[d, :] *= weight[d] * dim (mean weight = 1)
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```
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**Validated:** Top-5 attention subcarriers identified automatically per dataset.
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#### O8: Stoer-Wagner MinCut Person Separation (ruvector-mincut / ADR-075)
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JS implementation of the Stoer-Wagner algorithm for person separation in CSI, equivalent
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to `DynamicPersonMatcher` in `wifi-densepose-train/src/metrics.rs`. Builds a subcarrier
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correlation graph and finds the minimum cut to identify person-specific subcarrier clusters:
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```
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1. Build dim×dim Pearson correlation matrix across subcarriers
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2. Run Stoer-Wagner min-cut on correlation graph
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3. Partition subcarriers into person-specific groups
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4. Train per-partition models for multi-person scenarios
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```
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**Validated:** Stoer-Wagner executes on 56-dim graph, identifies partition boundaries.
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#### O9: Multi-SPSA Gradient Estimation
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Average over K=3 random perturbation directions per gradient step. Reduces variance
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by sqrt(K) = 1.73x compared to single SPSA, at 3x forward pass cost (net win for
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convergence quality):
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```
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For k in 1..K:
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delta_k = random ±1 per parameter
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grad_k = (loss(w + eps*delta_k) - loss(w - eps*delta_k)) / (2*eps*delta_k)
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grad = mean(grad_1, ..., grad_K)
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```
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#### O10: Mac M4 Pro Training via Tailscale
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Training runs on Mac Mini M4 Pro (16-core GPU, ARM NEON SIMD) via Tailscale SSH
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(`cohen@100.123.117.38`), using ruvllm's native Node.js SIMD ops:
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| | Windows (CPU) | Mac M4 Pro |
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|---|---|---|
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| Node.js | v24.12.0 (x86) | v25.9.0 (ARM) |
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| SIMD | SSE4/AVX2 | NEON |
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| Cores | Consumer laptop | 12P + 4E cores |
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| Training | Slow (minutes/epoch) | Fast (seconds/epoch) |
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#### O5: Cross-Environment Transfer
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Train on one room, deploy in another:
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@ -397,17 +465,43 @@ models/
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## Implementation Plan
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| Phase | Task | Effort | Dependencies |
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|-------|------|--------|-------------|
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| P1 | `collect-ground-truth.py` — camera + MediaPipe capture | 2 hrs | `pip install mediapipe opencv-python` |
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| P2 | `align-ground-truth.js` — time alignment + pairing | 1 hr | P1 output + existing CSI recordings |
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| P3 | `train-wiflow-supervised.js` — supervised training | 3 hrs | P2 output + existing ruvllm infra |
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| P4 | `eval-wiflow.js` — PCK evaluation | 1 hr | P3 output |
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| P5 | Data collection session (30 min recording) | 1 hr | P1 + running ESP32 nodes |
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| P6 | Training + evaluation run | 30 min | P2-P4 + collected data |
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| P7 | Optimizations O1-O2 (curriculum + augmentation) | 2 hrs | P6 baseline results |
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| P8 | LoRA cross-room calibration (O5) | 2 hrs | P7 |
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| **Total** | | **~12 hrs** | |
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| Phase | Task | Effort | Status |
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|-------|------|--------|--------|
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| P1 | `collect-ground-truth.py` — camera + MediaPipe capture | 2 hrs | **Done** |
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| P2 | `align-ground-truth.js` — time alignment + pairing | 1 hr | **Done** |
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| P3 | `train-wiflow-supervised.js` — supervised training | 3 hrs | **Done** |
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| P4 | `eval-wiflow.js` — PCK evaluation | 1 hr | **Done** |
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| P5 | ruvector optimizations (O6-O9) | 2 hrs | **Done** |
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| P6 | Mac M4 Pro training via Tailscale (O10) | 1 hr | **Done** |
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| P7 | Data collection session (30 min recording) | 1 hr | Pending |
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| P8 | Training + evaluation on real paired data | 30 min | Pending |
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| P9 | LoRA cross-room calibration (O5) | 2 hrs | Pending |
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## Validated Hardware
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| Component | Spec | Validated |
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|-----------|------|-----------|
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| Mac Mini camera | 1920x1080, 30fps | Yes — 14/17 keypoints, conf 0.94-1.0 |
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| MediaPipe PoseLandmarker | v0.10.33 Tasks API, lite model | Yes — via Tailscale SSH |
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| Mac M4 Pro GPU | 16-core, Metal 4, NEON SIMD | Yes — Node.js v25.9.0 |
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| Tailscale SSH | `cohen@100.123.117.38`, passwordless | Yes |
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| ESP32-S3 CSI | 128 subcarriers, 100Hz | Yes — existing recordings |
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| Sensing server recording API | `/api/v1/recording/start\|stop` | Yes — existing |
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## Baseline Benchmark
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Proxy-pose baseline (no camera supervision, standing skeleton heuristic):
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```
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PCK@10: 11.8%
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PCK@20: 35.3%
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PCK@50: 94.1%
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MPJPE: 0.067
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Latency: 0.03ms/sample
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```
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Per-joint PCK@20: upper body (nose, shoulders, wrists) at 0% — proxy has no spatial
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accuracy for these. Camera supervision targets these joints specifically.
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## References
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