Features used for each random forest model.

The incidence of head impacts in rugby has been a growing concern for player safety. While rugby headgear shows potential to mitigate head impact intensity during laboratory simulations, evaluating its on-field effectiveness is challenging. Current rugby-specific laboratory testing methods may not represent on-field conditions. This study aimed to create a machine-learning model capable of matching head impacts recorded via wearable sensors to the nearest match in a pre-existing library of laboratory-simulated head impacts for further investigation. Separate random forest models were trained, and optimised, on a training dataset of laboratory head impact data to predict the impact location, impact surface angle, neck inclusion, and drop height of a given laboratory head impact. The models achieved hold-out test set accuracies of 0.996, 1.0, 0.998, and 0.96 for the impact location, neck inclusion, impact surface angle, and drop height respectively. When applied to a male and female youth rugby head impact dataset, most impacts were classified as being to the side or rear of the head, with very few at the front of the head. Nearly 80% were more similar to laboratory impacts that included the neck with an impact surface angled at 30 or 45° with just under 20% being aligned with impacts onto a flat impact surface, and most were classified as low drop height impacts (7.5-30cm). Further analysis of the time series kinematics and spatial brain strain resulting from impact is required to align the laboratory head impact testing with the on-field conditions.