Pith Number

pith:GP5KQPX7

pith:2026:GP5KQPX75I6SZJY4OIIN7VMUOE

not attested not anchored not stored refs pending

Non-intrusive Body Composition Assessment from Full-body mmWave Scans

Benjamin D. Killeen, Miriam Senne, Nassir Navab, Tony Danjun Wang

mmWave radar scans can estimate visceral adipose tissue volume and body fat percentage from clothed individuals with mean absolute errors of 1.0 L and 3.2%.

arxiv:2605.08306 v2 · 2026-05-08 · eess.IV · cs.LG

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\usepackage{pith}
\pithnumber{GP5KQPX75I6SZJY4OIIN7VMUOE}

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Record completeness

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3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

We find that the model can predict VAT and BFP with a mean absolute error of 1.0 L and 3.2%, respectively, demonstrating the potential of mmWave scanning for routine BCA in a wide range of settings.

C2weakest assumption

That synthetic mmWave-like point clouds derived from clinical imaging and parametric human models accurately represent the signal characteristics of real mmWave scans acquired through clothing in a standing posture.

C3one line summary

A multi-task learning model trained on synthetic mmWave-like point clouds estimates VAT and BFP from real full-body mmWave scans through clothing with mean absolute errors of 1.0 L and 3.2%.

Receipt and verification

First computed	2026-05-26T02:04:12.483531Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

33faa83effea3d2ca71c7210dfd59471268adc1af68e9fd51d2ee7367b53e21d

Aliases

arxiv: 2605.08306 · arxiv_version: 2605.08306v2 · doi: 10.48550/arxiv.2605.08306 · pith_short_12: GP5KQPX75I6S · pith_short_16: GP5KQPX75I6SZJY4 · pith_short_8: GP5KQPX7

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/GP5KQPX75I6SZJY4OIIN7VMUOE \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 33faa83effea3d2ca71c7210dfd59471268adc1af68e9fd51d2ee7367b53e21d

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "011c8cc9445517d4eb091b615f1cad398b253f042173769e77860e1895e8e135",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "eess.IV",
    "submitted_at": "2026-05-08T12:54:20Z",
    "title_canon_sha256": "50eb3babd6f369fd2362ba582265d1f8d3da45a916cfa1a29e17d985306b7ae3"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.08306",
    "kind": "arxiv",
    "version": 2
  }
}