Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural network

TL;DR

This paper presents a deep learning approach using 1D convolutional neural networks to denoise mouthguard measurements of head impacts, significantly improving accuracy for TBI research and impact detection.

Contribution

The study introduces a novel application of 1D-CNN models to denoise mouthguard kinematic data, enhancing measurement accuracy across multiple datasets and impact scenarios.

Findings

Significantly reduced errors in kinematic measurements

Improved accuracy in brain injury criteria and tissue strain calculations

Effective denoising on both laboratory and field impact data

Abstract

Wearable sensors for measuring head kinematics can be noisy due to imperfect interfaces with the body. Mouthguards are used to measure head kinematics during impacts in traumatic brain injury (TBI) studies, but deviations from reference kinematics can still occur due to potential looseness. In this study, deep learning is used to compensate for the imperfect interface and improve measurement accuracy. A set of one-dimensional convolutional neural network (1D-CNN) models was developed to denoise mouthguard kinematics measurements along three spatial axes of linear acceleration and angular velocity. The denoised kinematics had significantly reduced errors compared to reference kinematics, and reduced errors in brain injury criteria and tissue strain and strain rate calculated via finite element modeling. The 1D-CNN models were also tested on an on-field dataset of college football impacts and a post-mortem human subject dataset, with similar denoising effects observed. The models can be used to improve detection of head impacts and TBI risk evaluation, and potentially extended to other sensors measuring kinematics.