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Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics

Journal of Sport and Health Science 2023年第5期12卷 619-629,F0003页

作者： Xianghao Zhan Yiheng Li Yuzhe Liu Nicholas J.Cecchi Samuel J.Raymond Zhou Zhou Hossein Vahid Alizadeh Jesse Ruan Saeed Barbat Stephen Tiernan Olivier Gevaert Michael M.Zeineh Gerald A.Grant David B.Camarillo Department of Bioengineering Stanford UniversityStanfordCA 94305USA Department of Biomedical Data Science Stanford UniversityStanfordCA 94305USA Ford Motor Company 3001 Miller RdDearbornMI 48120USA Technological University Dublin DublinD07 EWV4Ireland Department of Radiology Stanford UniversityStanfordCA 94305USA Department of Neurosurgery Stanford UniversityStanfordCA 94305USA

Background:Traumatic brain injury can be caused by head impacts,but many brain injury risk estimation models are not equally accurate across the variety of impacts that patients may undergo,and the characteristics of different types of impacts are not well *** investigated the spectral characteristics of different head impact types with kinematics ***:Data were analyzed from 3262 head impacts from lab reconstruction,American football,mixed martial arts,and publicly available car crash data.A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types(e.g.,football,car crash,mixed martial arts).To test the classifier robustness,another 271 lab-reconstructed impacts were obtained from 5 other instrumented ***,with the classifier,type-specific,nearest-neighbor regression models were built for brain ***:The classifier reached a median accuracy of 96% over 1000 random partitions of training and test *** most important features in the classification included both low-and high-frequency features,both linear acceleration features and angular velocity *** head impact types had different distributions of spectral densities in low-and high-frequency ranges(e.g.,the spectral densities of mixed martial arts impacts were higher in the high-frequency range than in the low-frequency range).The type-specific regression showed a generally higher R2value than baseline models without ***:The machine-learning-based classifier enables a better understanding of the impact kinematics spectral density in different sports,and it can be applied to evaluate the quality of impact-simulation systems and on-field data augmentation.

关键词： Classification Contact sports head impacts Impact kinematics Traumatic brain injuryTagedAPTARAEnd

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