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An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

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Machine Learning Applications in Non-Contact Lower Limb Sports Injury Prediction: A Systematic Review.

Jin Yuan¹, Quanwen Zeng¹, Anjie Wang¹

¹School of Physical Education, Anhui Polytechnic University, Wuhu, 241000, Anhui, China.

Journal of Sports Science & Medicine

|February 19, 2026

Summary

This summary is machine-generated.

Machine learning models show promise for predicting non-contact lower limb sports injuries. Tree-based approaches, like decision trees, offer strong predictive performance for athlete injury risk assessment.

Keywords:

Predictive analytics predictive models rehabilitation risk assessment risk factors sports medicine

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Lower Limb Biomechanical Analysis of Healthy Participants

Lower Limb Biomechanical Analysis of Healthy Participants

Published on: April 15, 2020

Area of Science:

Sports Medicine
Biomechanical Engineering
Data Science in Sports

Background:

Non-contact lower limb injuries are common and impactful in athletes.
Predictive modeling is crucial for developing effective prevention and rehabilitation strategies.
Machine learning (ML) offers advanced capabilities for analyzing complex sports injury data.

Purpose of the Study:

To systematically review the current evidence on machine learning for predicting non-contact lower limb sports injuries.
To assess the performance and methodologies of ML models in athletic injury risk prediction.
To identify key trends and future directions in this research area.

Main Methods:

Systematic literature review following PRISMA 2020 guidelines.
Searched databases: Web of Science, PubMed, SPORTDiscus (EBSCO) on January 20, 2025.
Included 15 studies after screening 92, assessed risk of bias using PROBAST tool.

Main Results:

Adult athletes in basketball and football (soccer) were most studied.
Random Forest and logistic regression were common; tree-based methods showed strongest performance (6 studies).
Highest Area Under the Curve (AUC) of 0.91 achieved by a CHAID decision tree; sensitivity up to 0.92.

Conclusions:

Machine learning demonstrates significant potential for predicting non-contact lower limb injuries in athletes.
Model interpretability is increasingly important for clinical application.
Future research needs multi-source data integration and prospective validation for practical translation into precision injury prevention.