Piecewise Multivariate Linearity Between Kinematic Features and Cumulative Strain Damage Measure (CSDM) Across Different Types of Head Impacts
Abstract
Building on previous findings that no single global linear model can describe the relationship between brain strain and kinematic features across diverse head impact types, this study examines whether piecewise multivariate linearity exists. Using K-means clustering, 3161 impacts from simulations, college football, mixed martial arts, and car crashes were partitioned into data-driven clusters. Within clusters, cumulative strain damage measure (CSDM, threshold 0.15) was regressed on kinematic features. K-means-based partitioning significantly improved regression accuracy compared to models without partitioning or those based solely on impact type. Additional analyses showed that partitioning by maximum angular acceleration at 4706 rad/s² yielded particularly strong piecewise linearity. These results support the existence of piecewise multivariate linear relationships and suggest improved strategies for rapid CSDM prediction.
