mlsanityData to 14 Oct 2026

Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation

arXiv 2102.05020 ↗First posted 9 Feb 2021physics.bio-ph cs.LG stat.APcited by 8

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Abstract (arXiv)

Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of kinematics on the head, finite element (FE) models and various brain injury criteria based on different factors of these kinematics have been developed, but the contribution of different kinematic factors has not been comprehensively analyzed across different types of head impacts in a data-driven manner. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in 1) the derivative order (angular velocity, angular acceleration, angular jerk), 2) the direction and 3) the power (e.g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events. Ordinary least squares regressions were built from kinematics factors to the 95% maximum principal strain (MPS95), and we compared zero-order correlation coefficients, structure coefficients, commonality analysis, and dominance analysis. The angular acceleration, the magnitude, and the first power factors showed the highest predictive power for the majority of impacts including laboratory impacts, American football impacts, with few exceptions (angular velocity for MMA and NASCAR impacts). The predictive power of rotational kinematics in three directions (x: posterior-to-anterior, y: left-to-right, z: superior-to-inferior) of kinematics varied with different sports and types of head impacts.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 3 of its 69 references that are in the map. First posted on arXiv 9 Feb 2021.

Builds on

3 of 69 references.

References · 3

  1. 2020Deep Learning Head Model for Real-time Estimation of Entire Brain Deformation in Concussioncited by 10
  2. 2020Validation and Comparison of Instrumented Mouthguards for Measuring Head Kinematics and Assessing Brain Deformation in Football Impactscited by 8
  3. 2020Relationship between brain injury criteria and brain strain across different types of head impacts can be differentcited by 8

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Cited by year

Citing papers per year: 2021: 4; 2022: 1; 2023: 2; 2024: 1.

8 papers.

  1. 2021Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematicscited by 3
  2. 2021Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusioncited by 3
  3. 2021Data-driven decomposition of brain dynamics with principal component analysis in different types of head impactscited by 2
  4. 2021Kinematics clustering enables head impact subtyping for better traumatic brain injury predictioncited by 1
  5. 2022Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural networkcited by 1
  6. 2023Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptationcited by 0
  7. 2023Use of Deep Neural Networks for Uncertain Stress Functions with Extensions to Impact Mechanicscited by 0
  8. 2024Identification of head impact locations, speeds, and force based on head kinematicscited by 0