ArticleAnnals of biomedical engineering2022
Piecewise Multivariate Linearity Between Kinematic Features and Cumulative Strain Damage Measure (CSDM) Across Different Types of Head Impacts.
Article in Annals of biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Quantifying Morphology-Related Deviations in Brain Strain Using an Automated Mesh Morphing Method.Annals of biomedical engineering · 2026Article
- Assessing Head Acceleration Events in Female Community Rugby Union Players: A Cohort Study Using Instrumented Mouthguards.Sports medicine (Auckland, N.Z.) · 2025Observational
- Efficient Generation of Pretraining Samples for Developing a Deep Learning Brain Injury Model via Transfer Learning.Annals of biomedical engineering · 2024Article
- Adaptive Machine Learning Head Model Across Different Head Impact Types Using Unsupervised Domain Adaptation and Generative Adversarial Networks.IEEE sensors journal · 2024Article
- Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics.Journal of sport and health science · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
In a previous study, we found that the relationship between brain strain and kinematic features cannot be described by a generalized linear model across different types of head impacts. In this study, we investigate if such a linear relationship exists when partitioning head impacts using a data-driven approach. We applied the K-means clustering method to partition 3161 impacts from various sources including simulation, college football, mixed martial arts, and car crashes. We found piecewise multivariate linearity between the cumulative strain damage (CSDM; assessed at the threshold of 0.15) and head kinematic features. Compared with the linear regression models without partition and the partition according to the types of head impacts, K-means-based data-driven partition showed significantly higher CSDM regression accuracy, which suggested the presence of piecewise multivariate linearity across types of head impacts. Additionally, we compared the piecewise linearity with the partitions based on individual features used in clustering. We found that the partition with maximum angular acceleration magnitude at 4706 rad/s
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Registered trials
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