Prediction of occupant lumbar spine injuries based on machine learning and analysis of influencing factors
10.3969/j.issn.1005-202X.2025.03.016
- VernacularTitle:基于深度学习的汽车乘员腰椎损伤预测及影响因素分析
- Author:
Haiyan LI
1
;
Xinyu ZHANG
;
Ting KE
;
Yanxin WANG
;
Lijuan HE
;
Wenle LÜ
;
Shihai CUI
;
Shijie YUAN
Author Information
1. 天津科技大学机械工程学院,天津 300222;现代汽车安全技术国际联合研究中心,天津 300222
- Publication Type:Journal Article
- Keywords:
bionic model of lumbar spine injury;
injury mechanism;
machine learning;
principal component analysis;
neural network
- From:
Chinese Journal of Medical Physics
2025;42(3):388-396
- CountryChina
- Language:Chinese
-
Abstract:
Based on CT scan data,a bionic model of lumbar spine injuries with high biofidelity is developed and validated through cadaver experiments.Decoupling the constraint system that affects occupants during collisions due to inertial forces and the subsequent pressure exerted by the seat upon returning to position,a simulated fall experiment is designed.The simulated outcomes are trained and predicted using deep learning algorithms,and the accuracy of the trained neural network prediction model is verified.Key parameters are analyzed for correlation using principal component analysis and cross-reverse methods.The results shows that the predicted lumbar spine injury model obtained from training has high reliability(R2>0.9).Comprehensive analysis reveals that after experiencing axial impact,the L4 vertebral body bears the highest impact load and can be used as a representative measure of lumbar spine injury.Among the environmental variables,the axial force on the L4 lumbar spine is mainly affected by torso mass and fall height,both of which have positive correlations.Torso mass,fall height,and posture angle all have positive effects on internal energy.Conversely,torso mass and fall height have negative correlations with stress.These research findings provide a scientific basis for further elucidating lumbar spine injury mechanisms in intelligent cockpit environments,devising corresponding safety protection measures,and evaluating occupant safety in automobiles.
