{
  "abstract": "Estimation of individual skill improvement within specific school-based physical education units remains methodologically underdeveloped, with existing approaches typically lacking external validation and interpretability. Because the full model incorporates training-load features aggregated across the same temporal window as the outcome, it is epistemologically retrospective; readers should not interpret it as a prospectively deployable prediction tool. This study aimed at developing and externally validating an explainable machine learning ensemble to retrospectively explain and estimate standardized skill gain (Δz) in Baduanjin (eight-section brocade) qigong, a mandatory traditional martial arts skill in the Chinese national PE curriculum, among students from four schools (n = 727). A stacking meta-learner composed of five candidate algorithms, including XGBoost, LightGBM, Random Forest, LASSO, and MLP, was trained using biomechanical, psycho-cognitive, and semester-aggregated training-load features obtained from the development cohort (Schools A + B, n = 486) and then validated against two geographically independent but regionally situated cohorts (Schools C + D, n = 241). Because the training-load features were aggregated across the full 16-week semester, the model functions as a retrospective semester-level estimation framework rather than a prospective early-warning tool. The model yielded an external validation R² of 0.691 (95% CI: 0.661–0.718), with robust subgroup performance across gender, initial skill quartiles, and sport types. SHAP-based interpretability analysis identified exercise self-efficacy as the highest-ranking modifiable feature in model-derived importance, with its predictive contribution predominantly associated with the training-response component rather than baseline level. A baseline-only sensitivity model excluding semester-aggregated training-load variables retained moderate external validity (R² = 0.573), indicating that prospective application at semester onset is feasible in principle but at substantially reduced precision. These findings suggest that, within this dataset and model specification, psycho-cognitive features contributed more strongly to model estimations than biomechanical features.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Jiaqi Li"
    },
    {
      "name": "Ye Zhang"
    },
    {
      "name": "Yuanhang Sun"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0ftzap1cf1h2d.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-73947-0",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-73947-0",
  "id": "0ftzap1cf1h2d",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-73947-0",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1702|Educational Technology and Pedagogy"
    ]
  ],
  "openalex": "W7213270972",
  "pdf_url": "https://www.nature.com/articles/s41598-026-73947-0.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/0ftzap1cf1h2d.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.1038/s41598-026-73947-0",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-73947-0"
    },
    {
      "first_seen": "2026-10-30T13:45:50Z",
      "native_id": "d7f8cb90ea934ae89ed2067be8a0686f",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-73947-0"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "An explainable AI framework using wearable IMU-based motion sensing for estimating and explaining motor skill improvement in school-based Baduanjin education",
  "title_text": "An explainable AI framework using wearable IMU-based motion sensing for estimating and explaining motor skill improvement in school-based Baduanjin education",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:50Z",
  "url": "https://research.draconitic.com/paper/0ftzap1cf1h2d",
  "venue": "Scientific Reports",
  "versions": []
}