{
  "abstract": "Ground reaction force (GRF) trajectories capture kinetic alterations in stance-phase loading, propulsion, and mediolateral control. This study presents GaitScreen, a GRF-based framework for post-acquisition prioritisation of already recorded healthy-control and pathological-gait trials. GaitScreen converts vertical, anterior–posterior, and mediolateral stance-phase GRF trajectories into a review-prioritisation score and a threshold-dependent review flag. The framework combines a multiscale temporal backbone, a convolutional neural network component, and a logistic regression component to integrate multiscale force patterns, learned convolutional representations, and raw-waveform linear information. It was evaluated using the GaitRec dataset, comprising healthy controls and individuals with lower-limb orthopaedic conditions. Under a subject-exclusive evaluation protocol, GaitScreen achieved consistent trial-level performance across ten matched runs, with an accuracy of 0.8766±0.0025, a macro-F1 score of 0.8757±0.0024, and an AUROC of 0.9425±0.0015. A high-sensitivity operating mode reduced the false-negative rate from 0.1736 to 0.1073, with an increase in false-positive review flags. The full hybrid model had a serialized size of 1.122 MB and a mean CPU inference latency of 0.626±0.010 ms per sample after GRF preprocessing. The compact model size and low inference latency support computationally scalable post-acquisition processing of standardised GRF trials.",
  "abstract_shown": true,
  "affiliations": [
    "School of Engineering, University of Birmingham, Birmingham, United Kingdom",
    "Gait and Motion Analysis Center, Yueyang Hospital of Integrative Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Qingyao Bian"
    },
    {
      "affs": [
        0
      ],
      "name": "Qing Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Yang Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Ziyun Ding"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/be0t8mtppg9k8.bib",
  "cited_by": 0,
  "doi": "10.3389/fbioe.2026.1947887",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fbioe.2026.1947887/full",
  "id": "be0t8mtppg9k8",
  "issns": [
    "2296-4185"
  ],
  "landing_url": "https://doi.org/10.3389/fbioe.2026.1947887",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "TP248.13-248.65"
    ],
    [
      "openalex",
      "2204|Lower Extremity Biomechanics and Pathologies"
    ]
  ],
  "openalex": "W7218552843",
  "pdf_url": "https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2026.1947887/pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-26",
  "publisher": "Frontiers Media S.A.",
  "ris": "https://research.draconitic.com/paper/be0t8mtppg9k8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:47:26Z",
      "native_id": "07b367b514564c658f70ab318a6d38d9",
      "source": "doaj",
      "url": "https://doi.org/10.3389/fbioe.2026.1947887"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "lcc:TP248.13-248.65",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "GaitScreen: a ground reaction force-based framework for prescreening gait abnormalities in orthopaedic populations",
  "title_text": "GaitScreen: a ground reaction force-based framework for prescreening gait abnormalities in orthopaedic populations",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:21:09Z",
  "url": "https://research.draconitic.com/paper/be0t8mtppg9k8",
  "venue": "Frontiers in Bioengineering and Biotechnology",
  "versions": []
}