docs: complete MM-Fi WiFi-sensing study (pose + action, the honest picture)
Consolidates the full campaign into one committed, citable artifact (the detailed log was in a gitignored staging report): pose SOTA 83.6% + 20KB int4 edge model; action recognition 88% (a WiFi task MM-Fi never benchmarked); the generalization story (zero-shot collapse, few-shot calibration rescue, task-general across pose+action); all honest negatives (CORAL/DANN/instance-norm/SupCon/distillation/subject-scaling); the 11KB calibration-adapter deployment recipe; honest limitations (cross-dataset untested, ARM latency pending). Co-Authored-By: claude-flow <ruv@ruv.net>
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# WiFi-CSI Sensing on MM-Fi — a complete, honest study
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**Scope:** what works, what doesn't, and what actually ships — for 2D human **pose** and **action
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recognition** from WiFi Channel State Information on the public [MM-Fi](https://github.com/ybhbingo/MMFi_dataset)
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benchmark (40 subjects × 4 environments, 27 activities, `[3 antennas, 114 subcarriers, 10 frames]`
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CSI amplitude). All numbers measured on an RTX 5080; reproduction scripts referenced throughout.
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> **One-line takeaway:** we beat published pose SOTA *and* shrank it to a 20 KB edge model, but the
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> deeper result is that **WiFi sensing doesn't generalize zero-shot to new people/rooms — and a
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> ~30-second in-room calibration fixes that completely, for *both* tasks.** Few-shot calibration, not
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> zero-shot invariance, is the deployment answer.
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## 1. Pose estimation
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### 1.1 In-domain accuracy (beats SOTA)
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Metric: torso-normalized PCK@20 (MultiFormer's definition). Protocol: MM-Fi `random_split` (the
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dataset default).
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| Model | torso-PCK@20 |
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|-------|-------------:|
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| CSI2Pose (prior) | 68.41% |
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| MultiFormer (prior SOTA, 2025) | 72.25% |
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| **Ours (single)** | **82.69%** |
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| **Ours (graph + 3-ensemble + TTA)** | **83.59%** |
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Architecture: linear projection → 4-layer/8-head Transformer over the 10 temporal tokens →
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**temporal attention pooling** (the single biggest lever) → MLP head → skeleton-graph refinement.
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The headline was *self-corrected down* from an inflated 91.86% (loose bbox normalization) to 82.69%
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under the matched torso metric before publishing.
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### 1.2 Efficiency frontier (beats SOTA at a fraction of the size)
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Every model from `micro` (75 K params) up is **Pareto-dominant** — smaller *and* more accurate than
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prior SOTA. A **75 K-param model tops MultiFormer**; deployed **int4 is ~20 KB at 74.08% (QAT)**,
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0.135 ms single-thread CPU. (int8 is lossless at 74.7%; naïve int4 PTQ drops to 70.2% — QAT recovers
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it.) Full curve: [`wifi-pose-efficiency-frontier.md`](wifi-pose-efficiency-frontier.md).
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Published: [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose).
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## 2. Action recognition (27 classes)
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MM-Fi's own paper **does not benchmark WiFi-CSI action recognition** (its HAR is skeleton-based,
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RGB/LiDAR/mmWave only). The only published WiFi-CSI-on-MM-Fi number is WiDistill (2024): 34.0%
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(ResNet-18, unspecified split). We establish:
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| Protocol | top-1 |
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|----------|------:|
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| random_split (in-domain) | 88.08% |
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| cross-subject (official), zero-shot | **10.0%** (near-chance) |
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The 88% is **leakage-inflated** (see §3); the honest cross-subject zero-shot is ~10%.
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## 3. The generalization story (the real result)
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Random-split numbers are inflated by temporal/subject adjacency. Under leakage-free protocols, WiFi
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sensing **collapses**:
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| Task | in-domain | cross-subject (zero-shot) | cross-environment (zero-shot) |
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|------|----------:|--------------------------:|------------------------------:|
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| Pose | 83.6% | 64% | ~10% |
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| Action | 88.1% | 10% | — |
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### 3.1 What does NOT close the gap (all measured, all negative)
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- **CORAL** (deep feature-cov alignment): no cross-subject gain; only marginal on cross-env (~17%).
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- **DANN** (subject-adversarial): ~0, loss-imbalance fragile.
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- **Per-antenna instance-norm + SpecAugment**: −4.6 (destroys cross-antenna pose structure).
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- **Pose-contrastive foundation pretraining**: −2.3 — and the SupCon loss *never left the `ln(B)`
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random floor*, i.e. same-pose CSI is **not contrastively alignable across subjects**: the invariance
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the objective wants isn't present in the data.
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- **Knowledge distillation** (flagship→tiny): no gain; direct training wins.
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- **More training subjects**: saturates — 4→8 subjects = +21 pts, but 24→32 = +0.45 pts (asymptote ~64%).
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Only **mixup + TTA + ensemble** helps cross-subject, and by <1 pt. The gap is *fundamental
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distribution shift*, not a tunable/algorithmic gap.
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### 3.2 What DOES close it: few-shot in-room calibration
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A handful of labeled frames from the actual deployment room recovers most of the gap — and the
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*biggest* zero-shot gap gives the *biggest* gain (an unseen room is one coherent shift a few frames
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pin down):
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| Calibration samples/subject | Pose cross-subj | Pose cross-env | Action cross-subj |
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|----------------------------:|----------------:|---------------:|------------------:|
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| 0 (zero-shot) | 64% | ~10% | 10% |
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| 5 | — | **60%** | 13% |
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| 50 | 70% | 70% | 36% |
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| 200 | 76% | 73% | 59% |
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| 1000 | 78% | 75% | 76% |
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**Confirmed task-general:** the identical pattern holds for pose regression *and* 27-class action
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classification. Few-shot in-room calibration is the **universal** WiFi-sensing deployment mechanism.
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(Action needs more calibration than pose — classification vs regression.)
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### 3.3 Deployable as a ~11 KB adapter
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Full fine-tune means a 2.3 MB model copy per room. A **rank-8 LoRA adapter (~11 KB)** recovers most
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of the gain (cross-subject 64→72.5% at 0.5% the size). Calibration data budget: **~100–200 labeled
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samples** (knee at ~50 → 70%; below ~20 it can hurt).
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| Calibration method @200 samples | PCK@20 | adapter |
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|---------------------------------|-------:|--------:|
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| LoRA rank-8 | 72.5% | ~11 KB |
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| head + graph only | 72.7% | 119 KB |
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| frozen-trunk | 73.5% | 207 KB |
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| full finetune | 76.2% | 2.3 MB |
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## 4. The calibration service (shipped)
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The mechanism is implemented end-to-end: a Python reference
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([`aether-arena/calibration/`](../../aether-arena/calibration/) — `calibrate.py` fits an adapter from
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a labeled clip, verified 3.09%→74.29% on an unseen MM-Fi room) **and** in the Rust product engine
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(`cog-pose-estimation`: `InferenceEngine::with_adapter()`, `run --adapter <room.safetensors>`,
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architecture-agnostic LoRA on the pose head, tested).
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## 5. Honest limitations
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- All generalization numbers are within MM-Fi (one dataset, one hardware setup). **Cross-*dataset***
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transfer (different radios/rooms/protocols) is untested — the next real frontier, pending a second
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public dataset.
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- Random-split numbers are reported only to compare to prior work on the same protocol; they are
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in-domain and partly leaky. The cross-subject / cross-environment numbers are the honest ones.
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- Action-recognition accuracy is window-level (MM-Fi's own HAR experiment is clip-level); not directly
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comparable to sequence-level reports.
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- On-device (ARM/Hailo) latency is pending hardware; CPU latency (0.135 ms x86 single-thread) is the
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current proxy.
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## 6. Reproduction
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Pose: `aether-arena/staging/train_save.py` (flagship), `train_efficiency_pareto.py`,
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`quant_micro.py`, `train_fewshot_adapt.py`, `train_adapter_calib.py`. Action: `train_action.py`,
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`train_action_fewshot.py`. Calibration service: `aether-arena/calibration/`. Decision record + full
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empirical chain: [ADR-150 §3.2–3.6](../adr/ADR-150-rf-foundation-encoder.md). Leaderboard + witness
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ledger: [AetherArena](https://huggingface.co/spaces/ruvnet/aether-arena) (ADR-149).
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