Piecewise Multivariate Linearity Between Kinematic Features and Cumulative Strain Damage Measure (CSDM) Across Different Types of Head Impacts

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(2) led to the highest piecewise linearity. This study may contribute to an improved method for the rapid prediction of CSDM in the future.

Automated summary

This study investigates the relationship between brain strain and kinematic features in different types of head impacts. The researchers used a data-driven approach and found piecewise multivariate linearity between cumulative strain damage (CSDM) and head kinematic features. They compared different partition methods and found that the K-means clustering method showed significantly higher regression accuracy for CSDM. The study suggests that this method may contribute to the rapid prediction of CSDM in the future.