{
  "abstract": "A recogniser trained on one person rarely transfers to the next, and useful performance usually demands a fresh round of labelled calibration from the end user. A systematic review of 1077 studies quantifies where the evidence is thin: amputees appear in about one in six. Here a montage-agnostic cross-user encoder is carried to eleven transradial amputees on a protocol matched to its intact-limb training data. Zero-shot cross-population transfer fails outright: the encoder requires labeled data from the new user before it begins decoding, and it then exceeds the per-user classifier a clinic would fit by 0.190 macro F1 at three repetitions and for every subject in the cohort. Given three labelled repetitions it reaches 0.779 macro-F1 against 0.589 for the per-user pipeline. Training on forty intact subjects produces better transfers to a new amputee than training on ten other amputees, and combining the two produces better transfers than either individually. The prediction pre-registered for this study, which extends the encoder's baseline-strength account with the premise that amputee EMG is less separable, holds true only after a few repetitions become available and after enriching the source pool with additional amputees. At a single repetition, and at every budget under a source matched to the intact-limb comparison, it fails. Thus, it locates the boundary of the proposed account.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.20297",
  "author_total": 2,
  "authors": [
    {
      "name": "Jethro Odeyemi"
    },
    {
      "name": "W. J. Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/abwse56xbvqx3.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.20297",
  "fulltext_url": null,
  "id": "abwse56xbvqx3",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.20297",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "openalex",
      "2204|Muscle activation and electromyography studies"
    ]
  ],
  "openalex": "W7213613191",
  "pdf_url": "https://arxiv.org/pdf/2609.20297",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/abwse56xbvqx3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:24Z",
      "native_id": "2609.20297",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.20297"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "Intact-to-Amputee Transfer in Surface-EMG Gesture Decoding: Training Source and Calibration Budget",
  "title_text": "Intact-to-Amputee Transfer in Surface-EMG Gesture Decoding: Training Source and Calibration Budget",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:24Z",
  "url": "https://research.draconitic.com/paper/abwse56xbvqx3",
  "venue": "arXiv",
  "versions": []
}