Yuan Qiuqi, Hu Jingzhou, Xiao Zhi, Li Bin, Zhu Xiaoming, Niu Yunfei, Xu Shiwei
School of Mechanical and Vehicle Engineering, Hunan University, Changsha, China.
State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha, China.
Front Bioeng Biotechnol. 2024 Apr 30;12:1394177. doi: 10.3389/fbioe.2024.1394177. eCollection 2024.
Body sizes and head anatomical characteristics play the major role in the head injuries sustained by vulnerable road users (VRU) in traffic accidents. In this study, in order to study the influence mechanism of body sizes and head anatomical characteristics on head injury, we used age, gender, height, and Body Mass Index (BMI) as characteristic parameters to develop the personalized human body multi-rigid body (MB) models and head finite element (FE) models. Next, using simulation calculations, we developed the VRU head injury dataset based on the personalized models. In the dataset, the dependent variables were the degree of head injury and the brain tissue von Mises value, while the independent variables were height, BMI, age, gender, traffic participation status, and vehicle speed. The statistical results of the dataset show that the von Mises value of VRU brain tissue during collision ranges from 4.4 kPa to 46.9 kPa at speeds between 20 and 60 km/h. The effects of anatomical characteristics on head injury include: the risk of a more serious head injury of VRU rises with age; VRU with higher BMIs has less head injury in collision accidents; height has very erratic and nonlinear impacts on the von Mises values of the VRU's brain tissue; and the severity of head injury is not significantly influenced by VRU's gender. Furthermore, we developed the classification prediction models of head injury degree and the regression prediction models of head injury response parameter by applying eight different data mining algorithms to this dataset. The classification prediction models have the best accuracy of 0.89 and the best R2 value of 0.85 for the regression prediction models.
身体尺寸和头部解剖特征在交通事故中弱势道路使用者（VRU）所遭受的头部损伤中起主要作用。在本研究中，为了研究身体尺寸和头部解剖特征对头部损伤的影响机制，我们使用年龄、性别、身高和体重指数（BMI）作为特征参数来建立个性化人体多刚体（MB）模型和头部有限元（FE）模型。接下来，通过模拟计算，我们基于个性化模型建立了VRU头部损伤数据集。在数据集中，因变量是头部损伤程度和脑组织米塞斯值，自变量是身高、BMI、年龄、性别、交通参与状态和车速。数据集的统计结果表明，在20至60公里/小时的速度下，VRU脑组织在碰撞过程中的米塞斯值范围为4.4千帕至46.9千帕。解剖特征对头部损伤的影响包括：VRU头部受更严重损伤的风险随年龄增加；BMI较高的VRU在碰撞事故中的头部损伤较少；身高对VRU脑组织的米塞斯值有非常不稳定和非线性的影响；VRU的性别对头部损伤的严重程度没有显著影响。此外，我们通过对该数据集应用八种不同的数据挖掘算法，建立了头部损伤程度的分类预测模型和头部损伤响应参数的回归预测模型。分类预测模型的最佳准确率为0.89，回归预测模型的最佳R2值为0.85。
