Li Hai-Yan, Li Hai-Fang, Pan Jian-Yu, Cui Shi-Hai, He Guang-Long, He Li-Juan, Lü Wen-le
International Research Association on Emerging Automotive Safety Technology, Tianjin University of Science and Technology, Tianjin 300222, China.
Institute of Forensic Science, Ministry of Public Security, Beijing 100038, China.
Fa Yi Xue Za Zhi. 2022 Oct 25;38(5):573-578. doi: 10.12116/j.issn.1004-5619.2020.401108.
To analyze and predict the striking velocity range of stick blunt instruments in different populations, and to provide basic data for the biomechanical analysis of blunt force injuries in forensic identification.
Based on the Photron FASTCAM SA3 high-speed camera, Photron FASTCAM Viewer 4.0 and SPSS 26.0 software, the tester's maximum striking velocity of stick blunt instruments and related factors were calculated and analyzed, and inputed to the backpropagation (BP) neural network for training. The trained and verified BP neural network was used as the prediction model.
A total of 180 cases were tested and 470 pieces of data were measured. The maximum striking velocity range was 11.30-35.99 m/s. Among them, there were 122 female data, the maximum striking velocity range was 11.63-29.14 m/s; there were 348 male data, the maximum striking velocity range was 20.11-35.99 m/s. The maximum striking velocity of stick blunt instruments increased with the increase of weight and height, but there was no obvious increase trend in the male group; the maximum striking velocity decreased with age, but there was no obvious downward trend in the female group. The maximum striking velocity of stick blunt instruments has no significant correlation with the material and strike posture. The root mean square error (RMSE), the mean absolute error (MAE) and the coefficient of determination () of the prediction results by using BP neural network were 2.16, 1.63 and 0.92, respectively.
The prediction model of BP neural network can meet the demand of predicting the maximum striking velocity of different populations.
分析并预测不同人群中棍棒类钝器的打击速度范围，为法医鉴定中钝器伤的生物力学分析提供基础数据。
基于Photron FASTCAM SA3高速摄像机、Photron FASTCAM Viewer 4.0和SPSS 26.0软件，计算并分析测试者使用棍棒类钝器的最大打击速度及相关因素，并将其输入到反向传播（BP）神经网络进行训练。将训练并验证后的BP神经网络作为预测模型。
共测试180例，测量数据470条。最大打击速度范围为11.30 - 35.99米/秒。其中女性数据122条，最大打击速度范围为11.63 - 29.14米/秒；男性数据348条，最大打击速度范围为20.11 - 35.99米/秒。棍棒类钝器的最大打击速度随体重和身高的增加而增大，但男性组无明显增加趋势；最大打击速度随年龄减小，但女性组无明显下降趋势。棍棒类钝器的最大打击速度与材质和打击姿势无显著相关性。使用BP神经网络预测结果的均方根误差（RMSE）、平均绝对误差（MAE）和决定系数（）分别为2.16、1.63和0.92。
BP神经网络预测模型能够满足不同人群最大打击速度预测的需求。
