Abstract
In response to growing evidence that repetitive head impact exposure and concussions can lead to long-term health consequences, many research studies are attempting to quantify the frequency and severity of head impacts incurred in various sports and occupations. The most popular apparatus for doing so is the instrumented mouthguard (iMG). While these devices hold greater promise of head kinematic accuracy than their helmet-mounted predecessors, data artifacts related to iMG decoupling still plague results. We recreated iMG decoupling artifacts in a laboratory test series using an iMG fit to a dentition mounted in a NOCSAE headform. With these data, we identified time, frequency, and time-frequency features of decoupled head impacts that we used in a machine learning classification algorithm to predict decoupling in six-degree-of-freedom iMG signals. We compared our machine learning algorithm predictions on the laboratory series and 80 video-verified field head acceleration events to several other proprietary and published methods for predicting iMG decoupling. We also present a salvaging method to remove decoupling artifacts from signals and reduce peak resultant error when decoupling is detected. Future researchers should expand these methods using on-field data to further refine and enable prediction of iMG decoupling during live volunteer use. Combining the presented machine learning model and salvaging technique with other published methods, such as infrared proximity sensing, advanced triggering thresholds, and video review, may enable researchers to identify and salvage data with decoupling artifacts that previously would have had to be discarded.