Key biomechanical features of jump-landing under cognitive dual-task conditions: an XGBoost-SHAP-based explainable machine learning analysis.
- DOI
- 10.3389/fbioe.2026.1930588
- Published
- 2026
- Container
- Frontiers in bioengineering and biotechnology
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3389/fbioe.2026.1930588,
title = {Key biomechanical features of jump-landing under cognitive dual-task conditions: an XGBoost-SHAP-based explainable machine learning analysis.},
author = {Zhu Y and Zhang Y and Ma Q and Fang M and Cui R and Yu X and Chen S},
year = {2026},
journal = {Frontiers in bioengineering and biotechnology},
doi = {10.3389/fbioe.2026.1930588},
url = {https://doi.org/10.3389/fbioe.2026.1930588}
}RIS
TY - JOUR TI - Key biomechanical features of jump-landing under cognitive dual-task conditions: an XGBoost-SHAP-based explainable machine learning analysis. AU - Zhu Y AU - Zhang Y AU - Ma Q AU - Fang M AU - Cui R AU - Yu X AU - Chen S PY - 2026 JO - Frontiers in bioengineering and biotechnology DO - 10.3389/fbioe.2026.1930588 UR - https://doi.org/10.3389/fbioe.2026.1930588 ER -
APA
Y, Z., Y, Z., Q, M., M, F., R, C., X, Y., & S, C. (2026). Key biomechanical features of jump-landing under cognitive dual-task conditions: an XGBoost-SHAP-based explainable machine learning analysis.. Frontiers in bioengineering and biotechnology. https://doi.org/10.3389/fbioe.2026.1930588
Source records
- pubmed · retrieved 2026-09-25T06:01:03.044Z