{
  "abstract": "With the development of visual sensor technology and human pose estimation algorithms, skeleton-based action recognition has received extensive attention in sports analysis and training systems. The recognition of martial-arts actions is difficult because different styles can share similar poses while differing in rhythm and coordination. To support this task, we constructed the Martial Arts Pose (MA-Pose) dataset, which contains 433 source videos across 13 martial arts categories. The 33-landmark sequences include normalized image coordinates and monocular relative depth rather than metric 3D ground truth. We further propose a Dynamic Multi-Stream Multi-Scale Graph Convolutional Network (DMMS-GCN), which integrates joint, bone, and motion features. The model combines physical, learnable, and input-adaptive graph topology with parallel temporal convolutions. With a fixed seed of 42, 346 videos are used for training and 87 for validation, with one center crop and one prediction per validation video. DMMS-GCN correctly classifies 73/87 videos (83.91% Top-1; 83.51% Macro-F1; 95% Wilson confidence interval, 74.78%–90.17%) and retrieves the correct class among the top five for 84/87 videos (96.55%). These values describe development-set performance because model selection and evaluation use the same validation subset. On a Raspberry Pi 4B, pose extraction runs at approximately 10 frames/s and classification of a prepared 150-frame tensor takes approximately 120 ms. Collecting the first 150 processed pose frames takes about 15 s, so the deployment demonstrates edge-device feasibility rather than instantaneous recognition. The source code, model checkpoint, and MA-Pose dataset are publicly available.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Physical Education, Suzhou Vocational Health College, Suzhou, Jiangsu, China",
    "Department of Physical Education, Tangshan Normal University, Tangshan, China",
    "Department of Physical Education, Woosuk University, Wanju-gun, Jeollabuk-do, Republic of Korea"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Longying Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Ruijie Zhang"
    },
    {
      "affs": [
        2
      ],
      "name": "Huiteng Xu"
    },
    {
      "affs": [
        2
      ],
      "name": "Shilin Guo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dkrf7kbmnpnys.bib",
  "cited_by": 0,
  "doi": "10.3389/fnbot.2026.1967140",
  "fulltext_url": "https://www.frontiersin.org/articles/10.3389/fnbot.2026.1967140/full",
  "id": "dkrf7kbmnpnys",
  "issns": [
    "1662-5218"
  ],
  "landing_url": "https://doi.org/10.3389/fnbot.2026.1967140",
  "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",
      "RC321-571"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-04",
  "publisher": "Frontiers Media S.A.",
  "ris": "https://research.draconitic.com/paper/dkrf7kbmnpnys.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T14:20:50Z",
      "native_id": "029fbccfcbf5400eb4fbed0d3f603f7b",
      "source": "doaj",
      "url": "https://doi.org/10.3389/fnbot.2026.1967140"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:RC321-571",
      "subfield": "general"
    }
  ],
  "title": "MA-Pose and DMMS-GCN: skeleton-based martial-arts category recognition with edge-device deployment",
  "title_text": "MA-Pose and DMMS-GCN: skeleton-based martial-arts category recognition with edge-device deployment",
  "type": "peer-reviewed",
  "updated_at": "2026-11-05T14:20:46Z",
  "url": "https://research.draconitic.com/paper/dkrf7kbmnpnys",
  "venue": "Frontiers in Neurorobotics",
  "versions": []
}