  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 hours 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.
