{
  "abstract": "Background\n\nSurface electromyography (sEMG) is the dominant non-invasive route to volitional prosthetic control, but a closed-world classifier issues a command for every input, including inputs corresponding to no trained gesture. This study evaluated a low-cost, three-gesture sEMG control pipeline combining a Random Forest classifier with rejection on the maximum Random-Forest class probability and temporal majority voting, and measured the resulting rejection behaviour rather than assuming it.Methods\n\nThree data sources were analysed: modelled synthetic sEMG; Ninapro DB5 (10 subjects, 16 channels at 200 Hz, documented three-channel subset); and live three-channel sEMG from 13 healthy limb-intact adults recorded over three sessions with MyoWare sensors spaced equally around the forearm flexor group. Each of the 117 recordings lasted 120 s, comprising a 60 s continuous phase and four 10 s sustained contractions separated by 5 s of rest, yielding 21,294 windows of which 4,212 were rest. Channel interleaving gave an effective 333.3 Hz per channel. All datasets were segmented in physical time (0.6 s window, 0.2 s overlap) without resampling, and four time-domain features (MAV, RMS, WL, ZC) were classified by a 100-tree Random Forest. Evaluation used repetition-level, cross-session and leave-one-participant-out (LOPO) splits, with calibration and threshold selection confined to a participant-disjoint partition. Models were trained and tested within each domain, so the three accuracies index benchmark difficulty rather than transfer.Results\n\nLOPO accuracy was 80.2% (synthetic), 74.5% (DB5) and 80.2% (live). The pooled window-level split used originally overestimated live accuracy by 7.7 percentage points. Expected calibration error was 0.147 uncalibrated and 0.038 after Platt scaling. Open-set AUROC was 0.842 against rest, 0.784 under electrode displacement and 0.741 for held-out movements, and 7.4% of windows were confidently incorrect. The complete pipeline reduced false actuation from 19.80% to 5.00% and command switching from 0.16 to 0.02 s-1 , while rejecting 18.40% of windows and raising median latency from 0.60 to 1.95 s. The prototype completed 12 of 15 benchtop trials.Conclusion\n\nThe results support a benchtop engineering proof of concept with quantified rejection and latency behaviour, not clinical safety or deployment readiness.",
  "abstract_shown": true,
  "affiliations": [
    "School of Engineering and Science, MIT ADT Campus, MIT Art, Design and Technology University, Pune, Maharashtra, India.",
    "K. J. Somaiya School of Engineering, Somaiya Vidyavihar University, Mumbai, India."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Anagha Shinde"
    },
    {
      "affs": [
        0
      ],
      "name": "Virendra Shete"
    },
    {
      "affs": [
        1
      ],
      "name": "Ninad Mehendale"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/963m8jzvjem6e.bib",
  "cited_by": 0,
  "doi": "10.3389/fresc.2026.1864998",
  "fulltext_url": "https://europepmc.org/articles/PMC13683741",
  "id": "963m8jzvjem6e",
  "issns": [
    "2673-6861"
  ],
  "landing_url": "https://europepmc.org/article/MED/42909049",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2204|Muscle activation and electromyography studies"
    ]
  ],
  "openalex": "W7220852666",
  "pdf_url": "https://europepmc.org/articles/PMC13683741?pdf=render",
  "pmcid": "PMC13683741",
  "pmid": "42909049",
  "published": "2026-10-07",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/963m8jzvjem6e.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:14Z",
      "native_id": "MED:42909049",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42909049"
    }
  ],
  "subjects": [],
  "title": "Confidence-gated temporal voting for EMG-based prosthetic arm control: a low-cost end-to-end pipeline with quantified rejection behaviour.",
  "title_text": "Confidence-gated temporal voting for EMG-based prosthetic arm control: a low-cost end-to-end pipeline with quantified rejection behaviour.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:14Z",
  "url": "https://research.draconitic.com/paper/963m8jzvjem6e",
  "venue": "Frontiers in rehabilitation sciences",
  "versions": []
}