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Related Experiment Video

Updated: May 28, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

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

Published on: May 26, 2020

Injury Prediction and Risk Modelling in Team Sports Using Artificial Intelligence and Sensor-Based Monitoring: A

Michail Tsenos1, Christos Kokkotis2, Dimitrios Draganidis3

  • 1Department of Informatics, Athens University of Economics and Business, 10434 Athens, Greece.

Journal of Functional Morphology and Kinesiology
|May 27, 2026
PubMed
Summary

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This summary is machine-generated.

Artificial intelligence (AI) and sensor data can predict team sport injuries. However, diverse methods and limited validation hinder reliable injury risk models, requiring multi-centre studies for practical application.

Area of Science:

  • Sports Medicine
  • Data Science
  • Biomechanics

Background:

  • Sports injuries significantly impact athletes and teams.
  • AI and sensor technology offer potential for injury prediction.
  • Methodological diversity in current research limits consistent conclusions.

Purpose of the Study:

  • To map evidence on AI and sensor use for injury prediction in team sports.
  • To identify trends and research gaps in AI-driven injury risk modeling.
  • To guide future development of reliable injury prediction systems.

Main Methods:

  • Scoping review following PRISMA-ScR guidelines.
  • Systematic searches in PubMed and Scopus databases.
  • Analysis of 11 eligible studies on AI/ML for injury prediction using sensor data.
Keywords:
athlete monitoringinjury risk modellingmachine learningsports injuriestraining loadwearable technology

Related Experiment Videos

Last Updated: May 28, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

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

Published on: May 26, 2020

Main Results:

  • Most studies focused on football/rugby, using wearable GPS/inertial sensor data.
  • External workload, injury history, and recovery markers were key predictors.
  • Significant heterogeneity in methodologies, validation, and performance metrics was observed.

Conclusions:

  • Current AI models for injury prediction are limited by methodological diversity and lack of external validation.
  • Future research needs multimodal data integration and multi-centre validation.
  • Developing interpretable and practical AI-based injury prediction systems is crucial.