  Despite numerous research efforts, the precise mechanisms of concussion have
yet to be fully uncovered. Clinical studies on high-risk populations, such as
contact sports athletes, have become more common and give insight on the link
between impact severity and brain injury risk through the use of wearable
sensors and neurological testing. However, as the number of institutions
operating these studies grows, there is a growing need for a platform to share
these data to facilitate our understanding of concussion mechanisms and aid in
the development of suitable diagnostic tools. To that end, this paper puts
forth two contributions: 1) a centralized, open-source platform for storing and
sharing head impact data, in collaboration with the Federal Interagency
Traumatic Brain Injury Research informatics system (FITBIR), and 2) a deep
learning impact detection algorithm (MiGNet) to differentiate between true head
impacts and false positives for the previously biomechanically validated
instrumented mouthguard sensor (MiG2.0), all of which easily interfaces with
FITBIR. We report 96% accuracy using MiGNet, based on a neural network model,
improving on previous work based on Support Vector Machines achieving 91%
accuracy, on an out of sample dataset of high school and collegiate football
head impacts. The integrated MiG2.0 and FITBIR system serve as a collaborative
research tool to be disseminated across multiple institutions towards creating
a standardized dataset for furthering the knowledge of concussion biomechanics.
