Biomechanical assessment of repetitive brain injury in pedestrian collisions based on wavelet packet energy analysis
Abstract
Purpose
Existing brain injury criteria mainly focus on assessing injuries in a single collision and lack of assessment of injuries in real-world multiple collisions.
Methods
Based on the head kinematics signals of rats in repeated collisions and pedestrian accident data, the frequency domain characteristics of linear and rotational motion signals were analyzed by wavelet packet transform. Finally, the ROC curve was used to evaluate the effectiveness of injury index prediction.
Results
The results of animal experiments show that as the impact intensity increases, the energy in the frequency domain during the rotational motion increases accordingly. In linear motion, there is a threshold of impact strength. The prediction results show that the prediction effect of metrics based on frequency domain features is better than that of traditional brain injury criteria.
Conclusions
The new metrics based on wavelet packet energy accumulation can better predict the injury in multiple head impact scenarios in pedestrian collisions. When combined with the acceleration response signal's amplitude-frequency accumulation characteristics, they can be used as a potential method to assess repetitive head injury.
© 2026 Liangliang Shi, Tao Xiong, Zhiyong Yin, Kui Li, Shengxiong Liu, Zhengdong Li, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.