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Related Experiment Video

Updated: Sep 29, 2025

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact
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Physics-Informed Machine Learning Improves Detection of Head Impacts.

Samuel J Raymond1, Nicholas J Cecchi2, Hossein Vahid Alizadeh2

  • 1Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA. samueljraymond@gmail.com.

Annals of Biomedical Engineering
|March 18, 2022
PubMed
Summary
This summary is machine-generated.

A new physics-informed machine learning model analyzes mouthguard data to detect head impacts in American football. This AI approach improves concussion detection accuracy and significantly reduces manual analysis time.

Keywords:
American footballConcussionDeep learningInstrumented mouthguardPhysics-informed machine learningTraumatic brain injury

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Area of Science:

  • Biomechanics
  • Machine Learning
  • Sports Science

Background:

  • Head impacts in sports like American football pose significant concussion risks.
  • Current methods using sensor data and video analysis struggle with false positives and time inefficiency.
  • Traditional machine learning models perform poorly due to imbalanced datasets of true vs. false impacts.

Purpose of the Study:

  • To develop a physics-informed machine learning model for accurate head impact detection using instrumented mouthguard data.
  • To overcome the limitations of traditional methods in distinguishing true impacts from false positives.
  • To reduce the reliance on time-consuming manual video analysis for impact event verification.

Main Methods:

  • Created a physics-informed machine learning model integrating numerical head impact simulations with Finite Element head-neck models.
  • Augmented verified mouthguard impact data with a large synthetic dataset of simulated head impacts.
  • Trained and tested the model on kinematic data from instrumented mouthguards.

Main Results:

  • The physics-informed machine learning model achieved high performance with 88% negative and 87% positive predictive values.
  • The model reported an F1 score of 0.95, the best to date for American football impact detection.
  • Accurate detection of true and false impacts at 90% and 100% respectively, surpassing manual video analysis efficiency.

Conclusions:

  • The developed physics-informed machine learning model offers a more efficient and accurate solution for head impact detection in American football.
  • This AI-driven approach can significantly reduce the time spent on manual video analysis, saving over 12 hours for modest datasets.
  • The model shows potential for widespread use in sports, enhancing player safety and understanding of head injuries like concussion.