Machine-learning classification of ACL-related neuromechanical risk profiles using fatigue-inclusive multimodal functional tests in soccer players

Himan Ebrahimi; Ali Asghar Norasteh; Hassan Daneshmandi; Ali Sharifnezhad; Mostafa Zarei

doi:10.2478/bhk-2026-0035

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Machine-learning classification of ACL-related neuromechanical risk profiles using fatigue-inclusive multimodal functional tests in soccer players

Biomedical Human Kinetics

Volume 18 (2026): Issue 1 (January 2026)

By: Himan Ebrahimi , Ali Asghar Norasteh , Hassan Daneshmandi , Ali Sharifnezhad and Mostafa Zarei

Open Access

|Nov 2026

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Abstract

Study aim

Functional performance tests are widely used in sport, yet their ability to identify athletes with elevated anterior cruciate ligament (ACL)-related neuromechanical risk, particularly under fatigue, remains unclear. This study evaluated whether features extracted from fatigue-inclusive multimodal functional tests, combined with machine-learning methods, can enable internally validated classification of ACL-related neuromechanical risk profiles. High versus low risk was defined by median-splitting an equally weighted, standardised surrogate ACL-risk index.

Material and methods

Seventy male soccer players completed six tests (Drop Vertical Jump [DVJ], Landing Error Scoring System, Change of Direction (COD), Single-Leg Squat, Tuck Jump, and Star Excursion Balance Test) at two time points. The experimental group was assessed before and after a soccer-specific fatigue protocol, whereas controls completed repeated testing without fatigue. Synchronised motion capture, force-plate, electromyography, and isokinetic assessments provided task-specific features of landing mechanics, lower-limb alignment, dynamic stability, and reactive neuromuscular control. Logistic regression, random forest, and a voting ensemble within an exploratory developmental framework were trained using stratified fivefold cross-validation following TRIPOD+AI recommendations.

Results

Fatigue induced deterioration across all tests, with Change of Direction and Drop Vertical Jump yielding strongest single-test performance (AUCs = 0.95 and 0.94). No meaningful changes occurred in controls. The ensemble achieved internally validated Accuracy = 0.91, F1 = 0.90, AUC = 0.95.

Conclusions

Fatigue-inclusive multimodal functional testing showed promising classification performance for ACL-related surrogate neuromechanical risk profiles and provides a framework for further validation.

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Articles in this issue

DOI: https://doi.org/10.2478/bhk-2026-0035 | Journal eISSN: 2080-2234

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Language: English

Page range: 346 - 362

Submitted on: Sep 21, 2026

Accepted on: Sep 29, 2026

Published on: Nov 10, 2026

Published by: University of Physical Education in Warsaw

In partnership with: Paradigm Publishing Services

Keywords:

Anterior cruciate ligament,

Fatigue,

Functional performance test,

Machine learning,

Soccer

Related subjects:

Medicine,

Basic medical science,

Basic medical science, other,

Sports and recreation,

Physical education,

Sports and recreation, other

© 2026 Himan Ebrahimi, Ali Asghar Norasteh, Hassan Daneshmandi, Ali Sharifnezhad, Mostafa Zarei, published by University of Physical Education in Warsaw
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 18 (2026): Issue 1 (January 2026)