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Reducing Video Verification Burden: Machine Learning Classification of Head Acceleration Events in Youth Football.

Giovanny A Romero A1, Josh Cherian1, N Stewart Pritchard1

  • 1Wake Forest University School of Medicine.

Research Square
|June 4, 2026
PubMed
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A machine learning pipeline effectively classifies head acceleration events (HAEs) in youth football, significantly reducing video review time by 91% while maintaining a low misclassification rate for athlete safety research.

Area of Science:

  • Sports Medicine
  • Biomechanical Engineering
  • Machine Learning in Sports

Background:

  • Head acceleration events (HAEs) in youth American football pose significant risks.
  • Manual video verification of HAEs is labor-intensive and time-consuming.
  • Objective data collection is crucial for understanding injury mechanisms.

Purpose of the Study:

  • To develop and evaluate a machine learning pipeline for classifying HAEs in youth football.
  • To reduce the manual workload associated with video verification of HAEs.
  • To assess the feasibility of large-scale HAE monitoring.

Main Methods:

  • An eXtreme Gradient Boosting (XGBoost) classifier was trained on three seasons of instrumented mouthguard data.
  • A comprehensive set of kinematics-derived features was utilized.
Keywords:
Head acceleration eventsVideo-based ground truth labelingmachine learning classificationyouth American Tackle football

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  • Model performance was evaluated using precision, recall, F1 score, and permutation testing under imbalanced data.

    • Main Results:

    • The XGBoost model achieved performance comparable or superior to previous methods under real-world conditions.
    • Video review time was reduced by 91% (from ~90 hours to <8 hours) by reviewing only classifier-flagged events.
    • A misclassification rate of 2.5-3% was maintained, with thresholds of 10-15 g further reducing review time with minimal error increase.

    Conclusions:

    • An optimized machine learning pipeline and review-threshold strategy significantly enhances the feasibility of large-scale HAE monitoring in youth football.
    • This approach supports research and surveillance applications for improving athlete safety.
    • The findings demonstrate a substantial reduction in operational burden for HAE data verification.