Train and evaluate models that estimate human poses from WiFi signals. Compare results against a model that always predicts the average pose, check for data leakage, and report measured improvements with clear evaluation conditions.
Train/evaluate WiFi pose models honestly — camera-supervised (MediaPipe + CSI) and camera-free (WiFlow), always checked against the mean-pose baseline before any PCK is quoted.
Review the instructions the agent will receive before you install.
Build a CSI→pose model without overstating it. The project has a retracted 92.9%/100% history — the discipline below exists so it never recurs.
A pose model that always predicts the dataset's mean pose already scores ~50% PCK. Quote PCK only as a delta over that baseline, on a held-out split with no subject or temporal leakage. Example honest result (ADR-181):
Held-out PCK@20 59.5% vs a 50% mean-pose baseline = +9.4 pp real signal — MEASURED.
npx @ruvnet/ruview train-plan --mode pose-smoke # command, cwd, outputs; runs nothing
npx @ruvnet/ruview train --mode pose-smoke --confirm # SYNTHETIC pipeline smoke (libtorch 2.11 for tch 0.24)
npx @ruvnet/ruview train --mode pose --data-dir <in-repo MM-Fi dir> --confirm
npx @ruvnet/ruview train-gate --file eval-report.json # mean-pose baseline + leakage gate
The gate returns the only acceptable claim sentence. Quote nothing it fails.
(model − baseline) in pp, with the split definition (chronological /
blocked-gap / grouped-bucket; no leakage).ruview_claim_check the writeup — it flags any untagged or 100%/perfect claim.No reviews yet. Tell others what you thought.
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