Strike Velocity Prediction of Stick Blunt Instruments Based on Backpropagation Neural Network.
10.12116/j.issn.1004-5619.2020.401108
- Author:
Hai-Yan LI
1
;
Hai-Fang LI
1
;
Jian-Yu PAN
1
;
Shi-Hai CUI
1
;
Guang-Long HE
2
;
Li-Juan HE
1
;
Wen-le LÜ
1
Author Information
1. International Research Association on Emerging Automotive Safety Technology, Tianjin University of Science and Technology, Tianjin 300222, China.
2. Institute of Forensic Science, Ministry of Public Security, Beijing 100038, China.
- Publication Type:Journal Article
- Keywords:
back propagation neural networks;
biomechanics;
blunt instrument;
finite element analysis;
forensic medicine;
strike
- MeSH:
Male;
Humans;
Female;
Neural Networks, Computer;
Software;
Wounds, Nonpenetrating;
Forensic Medicine
- From:
Journal of Forensic Medicine
2022;38(5):573-578
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES: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.
METHODS: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.
RESULTS: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 (R2) of the prediction results by using BP neural network were 2.16, 1.63 and 0.92, respectively.
CONCLUSIONS:The prediction model of BP neural network can meet the demand of predicting the maximum striking velocity of different populations.
