An explainable AI framework using wearable IMU-based motion sensing for estimating and explaining motor skill improvement in school-based Baduanjin education
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.
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BibTeX
@article{li2026explainable,
title = {{An explainable AI framework using wearable IMU-based motion sensing for estimating and explaining motor skill improvement in school-based Baduanjin education}},
author = {Jiaqi Li and Ye Zhang and Yuanhang Sun},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-73947-0},
url = {https://www.nature.com/articles/s41598-026-73947-0},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - An explainable AI framework using wearable IMU-based motion sensing for estimating and explaining motor skill improvement in school-based Baduanjin education
AU - Jiaqi Li
AU - Ye Zhang
AU - Yuanhang Sun
PY - 2026
DA - 2026/10/13
JO - Scientific Reports
DO - 10.1038/s41598-026-73947-0
UR - https://www.nature.com/articles/s41598-026-73947-0
AB - 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.
SN - 2045-2322
ER -