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Physics-Informed Machine Learning Improves Detection of Head Impacts

  • Concussions
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Abstract

In this work we present a new physics-informed machine learning model that can be used to analyze kinematic data from an instrumented mouthguard and detect impacts to the head. Monitoring player impacts is vitally important to understanding and protecting from injuries like concussion. Typically, to analyze this data, a combination of video analysis and sensor data is used to ascertain the recorded events are true impacts and not false positives. In fact, due to the nature of using wearable devices in sports, false positives vastly outnumber the true positives. Yet, manual video analysis is time-consuming. This imbalance leads traditional machine learning approaches to exhibit poor performance in both detecting true positives and preventing false negatives. Here, we show that by simulating head impacts numerically using a standard Finite Element head-neck model, a large dataset of synthetic impacts can be created to augment the gathered, verified, impact data from mouthguards. This combined physics-informed machine learning impact detector reported improved performance on test datasets compared to traditional impact detectors with negative predictive value and positive predictive values of 88 and 87% respectively. Consequently, this model reported the best results to date for an impact detection algorithm for American football, achieving an F1 score of 0.95. In addition, this physics-informed machine learning impact detector was able to accurately detect true and false impacts from a test dataset at a rate of 90% and 100% relative to a purely manual video analysis workflow. Saving over 12 h of manual video analysis for a modest dataset, at an overall accuracy of 92%, these results indicate that this model could be used in place of, or alongside, traditional video analysis to allow for larger scale and more efficient impact detection in sports such as American Football.

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Acknowledgments

This research was supported by the Pac-12 Conference’s Student-Athlete Health and Well-Being Initiative, the National Institutes of Health (R24NS098518), and the Taube Stanford Children’s Concussion Initiative and Stanford Department of Bioengineering.

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Authors and Affiliations

  1. Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA

    Samuel J. Raymond, Nicholas J. Cecchi, Hossein Vahid Alizadeh, Ashlyn A. Callan, Yuzhe Liu, Zhou Zhou & David B. Camarillo

  2. Stanford Center for Clinical Research, Stanford University, Stanford, CA, 94305, USA

    Eli Rice

  3. Department of Radiology, Stanford University, Stanford, CA, 94305, USA

    Michael Zeineh

  4. Department of Neurosurgery, Stanford University, Stanford, CA, 94305, USA

    David B. Camarillo

  5. Department of Mechanical Engineering, Stanford University, Stanford, CA, 94305, USA

    David B. Camarillo

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  1. Samuel J. Raymond
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Correspondence to Samuel J. Raymond.

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Associate Editor Stefan M. Duma oversaw the review of this article.

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Raymond, S.J., Cecchi, N.J., Alizadeh, H.V. et al. Physics-Informed Machine Learning Improves Detection of Head Impacts. Ann Biomed Eng 50, 1534–1545 (2022). https://doi.org/10.1007/s10439-022-02911-6

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