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

arXiv: https://arxiv.org/abs/2212.09832 · HTML: https://mlsanity.com/paper/2212.09832/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 68 references that are in the map.
- First posted on arXiv 19 Dec 2022.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Deep Recurrent Neural Networks for ECG Signal Denoising](https://mlsanity.com/paper/1807.11551/) (2018)
- [Deep Learning Head Model for Real-time Estimation of Entire Brain Deformation in Concussion](https://mlsanity.com/paper/2010.08527/) (2020)
- [Validation and Comparison of Instrumented Mouthguards for Measuring Head Kinematics and Assessing Brain Deformation in Football Impacts](https://mlsanity.com/paper/2008.01903/) (2020)
- [Relationship between brain injury criteria and brain strain across different types of head impacts can be different](https://mlsanity.com/paper/2012.10006/) (2020)
- [Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation](https://mlsanity.com/paper/2102.05020/) (2021)
- [Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics](https://mlsanity.com/paper/2104.09082/) (2021)
- [Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion](https://mlsanity.com/paper/2108.13577/) (2021)
- [Unsupervised cross-user adaptation in taste sensation recognition based on surface electromyography with conformal prediction and domain regularized component analysis](https://mlsanity.com/paper/2110.11339/) (2021)
- [Data-driven decomposition of brain dynamics with principal component analysis in different types of head impacts](https://mlsanity.com/paper/2110.14116/) (2021)

## Built on by (most cited first, 1 of 1)

- [Identification of head impact locations, speeds, and force based on head kinematics](https://mlsanity.com/paper/2409.08177/) (2024)

## Abstract (arXiv)

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.