ruvnet--ruview/docs/benchmarks/mmfi-wifi-sensing-study.md

WiFi-CSI Sensing on MM-Fi — a complete, honest study

Scope: what works, what doesn't, and what actually ships — for 2D human pose and action recognition from WiFi Channel State Information on the public MM-Fi benchmark (40 subjects × 4 environments, 27 activities, [3 antennas, 114 subcarriers, 10 frames] CSI amplitude). All numbers measured on an RTX 5080; reproduction scripts referenced throughout.

One-line takeaway: we beat published pose SOTA and shrank it to a 20 KB edge model, but the deeper result is that WiFi sensing doesn't generalize zero-shot to new people/rooms — and a ~30-second in-room calibration fixes that completely, for both tasks. Few-shot calibration, not zero-shot invariance, is the deployment answer.

Sharpest finding (§7): WiFi-CSI sensing is largely a random-features + target-trained-readout problem — a random frozen encoder + a trained head gets within ~2–4 pts of a fully-trained encoder (and within <2 pts cross-subject). The encoder barely learns anything transferable; the signal is in the readout. This single fact explains the zero-shot collapse, the no-transfer results, the foundation-encoder failure, and why per-room calibration works.

1. Pose estimation

1.1 In-domain accuracy (beats SOTA)

Metric: torso-normalized PCK@20 (MultiFormer's definition). Protocol: MM-Fi random_split (the dataset default).

Model	torso-PCK@20
CSI2Pose (prior)	68.41%
MultiFormer (prior SOTA, 2025)	72.25%
Ours (single)	82.69%
Ours (graph + 3-ensemble + TTA)	83.59%

Architecture: linear projection → 4-layer/8-head Transformer over the 10 temporal tokens → temporal attention pooling (the single biggest lever) → MLP head → skeleton-graph refinement. The headline was self-corrected down from an inflated 91.86% (loose bbox normalization) to 82.69% under the matched torso metric before publishing.

1.2 Efficiency frontier (beats SOTA at a fraction of the size)

Every model from micro (75 K params) up is Pareto-dominant — smaller and more accurate than prior SOTA. A 75 K-param model tops MultiFormer; deployed int4 is ~20 KB at 74.08% (QAT), 0.135 ms single-thread CPU. (int8 is lossless at 74.7%; naïve int4 PTQ drops to 70.2% — QAT recovers it.) Full curve: wifi-pose-efficiency-frontier.md. Published: ruvnet/wifi-densepose-mmfi-pose.

2. Action recognition (27 classes)

MM-Fi's own paper does not benchmark WiFi-CSI action recognition (its HAR is skeleton-based, RGB/LiDAR/mmWave only). The only published WiFi-CSI-on-MM-Fi number is WiDistill (2024): 34.0% (ResNet-18, unspecified split). We establish:

Protocol	top-1
random_split (in-domain)	88.08%
cross-subject (official), zero-shot	10.0% (near-chance)

The 88% is leakage-inflated (see §3); the honest cross-subject zero-shot is ~10%.

3. The generalization story (the real result)

Random-split numbers are inflated by temporal/subject adjacency. Under leakage-free protocols, WiFi sensing collapses:

Task	in-domain	cross-subject (zero-shot)	cross-environment (zero-shot)
Pose	83.6%	64%	~10%
Action	88.1%	10%	—

3.1 What does NOT close the gap (all measured, all negative)

• CORAL (deep feature-cov alignment): no cross-subject gain; only marginal on cross-env (~17%).
• DANN (subject-adversarial): ~0, loss-imbalance fragile.
• Per-antenna instance-norm + SpecAugment: −4.6 (destroys cross-antenna pose structure).
• Pose-contrastive foundation pretraining: −2.3 — and the SupCon loss never left the ln(B) random floor, i.e. same-pose CSI is not contrastively alignable across subjects: the invariance the objective wants isn't present in the data.
• Knowledge distillation (flagship→tiny): no gain; direct training wins.
• More training subjects: saturates — 4→8 subjects = +21 pts, but 24→32 = +0.45 pts (asymptote ~64%).

Only mixup + TTA + ensemble helps cross-subject, and by <1 pt. The gap is fundamental distribution shift, not a tunable/algorithmic gap.

3.2 What DOES close it: few-shot in-room calibration

A handful of labeled frames from the actual deployment room recovers most of the gap — and the biggest zero-shot gap gives the biggest gain (an unseen room is one coherent shift a few frames pin down):

Calibration samples/subject	Pose cross-subj	Pose cross-env	Action cross-subj
0 (zero-shot)	64%	~10%	10%
5	—	60%	13%
50	70%	70%	36%
200	76%	73%	59%
1000	78%	75%	76%

Confirmed task-general: the identical pattern holds for pose regression and 27-class action classification. Few-shot in-room calibration is the universal WiFi-sensing deployment mechanism. (Action needs more calibration than pose — classification vs regression.)

3.3 Deployable as a ~11 KB adapter

Full fine-tune means a 2.3 MB model copy per room. A rank-8 LoRA adapter (~11 KB) recovers most of the gain (cross-subject 64→72.5% at 0.5% the size). Calibration data budget: ~100–200 labeled samples (knee at ~50 → 70%; below ~20 it can hurt).

Calibration method @200 samples	PCK@20	adapter
LoRA rank-8	72.5%	~11 KB
head + graph only	72.7%	119 KB
frozen-trunk	73.5%	207 KB
full finetune	76.2%	2.3 MB

4. The calibration service (shipped)

The mechanism is implemented end-to-end: a Python reference (aether-arena/calibration/ — calibrate.py fits an adapter from a labeled clip, verified 3.09%→74.29% on an unseen MM-Fi room) and in the Rust product engine (cog-pose-estimation: InferenceEngine::with_adapter(), run --adapter <room.safetensors>, architecture-agnostic LoRA on the pose head, tested).

5. Honest limitations

• Most generalization numbers are within MM-Fi (one dataset, one hardware setup). Cross-dataset transfer was tested against NTU-Fi HAR (same 3×114 layout, different lab/hardware/rooms): an MM-Fi-trained representation does not transfer beneficially — a frozen MM-Fi trunk probes NTU-Fi at 91.5%, no better than random features (93%), and full fine-tuning (75%) underperforms a linear probe. CSI representations are distribution-locked (same root cause as the within-MM-Fi cross-subject/-environment collapse); the practical answer is on-target training/few-shot, not transferable zero-shot features. Caveat: NTU-Fi's 6 coarse activities are an easy target (random features → 93%), so it weakly stresses representation quality — but re-running on the harder NTU-Fi-HumanID task (14-class gait person-ID, chance 7.1%) gave the same result (MM-Fi pretrain 91.7% ≈ random 92.8%). Unified root cause: for CSI, in-domain classification lives in the target-trained readout (a random 256-d projection of 3,420-d CSI is already linearly separable), while the learned representation fails to transfer across subjects, rooms, and datasets alike. WiFi-CSI sensing is distribution-locked; the answer is on-target few-shot calibration, not transferable features. A harder cross-dataset pose benchmark (vs classification) remains the one open variant.
• Random-split numbers are reported only to compare to prior work on the same protocol; they are in-domain and partly leaky. The cross-subject / cross-environment numbers are the honest ones.
• Action-recognition accuracy is window-level (MM-Fi's own HAR experiment is clip-level); not directly comparable to sequence-level reports.
• On-device (ARM/Hailo) latency is pending hardware; CPU latency (0.135 ms x86 single-thread) is the current proxy.

6. Reproduction

Pose: aether-arena/staging/train_save.py (flagship), train_efficiency_pareto.py, quant_micro.py, train_fewshot_adapt.py, train_adapter_calib.py. Action: train_action.py, train_action_fewshot.py. Calibration service: aether-arena/calibration/. Decision record + full empirical chain: ADR-150 §3.2–3.6. Leaderboard + witness ledger: AetherArena (ADR-149).

7. The sharpest result: the encoder barely matters

A random frozen transformer encoder + a trained pose head matches a fully-trained encoder to within 2–4 points (cross-subject: <2 points):

Pose protocol	fully-trained encoder	random-frozen encoder + head
in-domain	78.2%	73.8%
cross-subject	63.9%	62.1%

(Same fair-comparison config; absolute numbers below the 83.6% flagship — the delta is the point.) Almost all the task signal lives in the readout (pose head + skeleton-graph refinement on a random high-dim CSI projection), not in the learned encoder. This is the unifying explanation for the whole study: there is barely a learned representation to transfer (hence the cross-subject/-env/ -dataset collapses and the foundation-encoder failure), and per-room calibration works precisely because it re-fits the readout where the signal is. Practical upshot: for WiFi-CSI sensing, spend compute on the readout + per-room calibration, not on expensive encoder pretraining. Reproduce: aether-arena/staging/train_pose_randomfeat.py.