Key biomechanical features of jump-landing under cognitive dual-task conditions: an XGBoost-SHAP-based explainable machine learning analysis
Yuhang Zhu, Yanlong Zhang, Qingqing Ma, Minting Fang, Ronghe Cui, Xiao Yu, Si Chen · 2026
Read Key biomechanical features of jump-landing under cognitive dual-task conditions: an XGBoost-SHAP-based explainable machine learning analysis by Yuhang Zhu, Yanlong Zhang, Qingqing Ma, Minting Fang, Ronghe Cui, Xiao Yu, Si Chen (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13597890.txt.
Description
Yuhang Zhu and co-authors measured jump landings in 2026 while young men also carried a thinking task, to learn how a busy mind changes the body's landing pattern. Dual task landings matter in sport, where players land while tracking the game, and poor landings tear knee ligaments.
Two hundred forty active men landed under single and dual task rules while cameras and a force plate recorded them. Thirteen measures of joint angles and landing form fed four machine learning models. A boosted tree model sorted the two conditions best, with test accuracy above ninety-two percent. An explanation method ranked which measures drove the sorting, led by how wide the feet land relative to the body. The work supports screening tools.
Catalog record
| CREATOR | Yuhang Zhu, Yanlong Zhang, Qingqing Ma, Minting Fang, Ronghe Cui, Xiao Yu, Si Chen |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 60 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |