A New Open-Access Platform for Measuring and Sharing mTBI Data

TL;DR

This paper introduces an open-source platform for sharing concussion impact data and a deep learning algorithm, MiGNet, that accurately detects true head impacts, facilitating collaborative research and advancing understanding of concussion mechanisms.

Contribution

It presents a centralized, open-source data sharing platform integrated with FITBIR and a novel deep learning impact detection algorithm, MiGNet, with improved accuracy over previous methods.

Findings

MiGNet achieves 96% accuracy in impact detection.

The platform enables multi-institutional data sharing for concussion research.

MiGNet outperforms previous SVM-based models.

Abstract

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.