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Wang F, Peng K, Zou T, Li Q, Li F, Wang X, Wang J, Zhou Z. Numerical Reconstruction of Cyclist Impact Accidents: Can Helmets Protect the Head-Neck of Cyclists? Biomimetics (Basel) 2023; 8:456. [PMID: 37887587 PMCID: PMC10603864 DOI: 10.3390/biomimetics8060456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/23/2023] [Accepted: 09/24/2023] [Indexed: 10/28/2023] Open
Abstract
Cyclists are vulnerable road users and often suffer head-neck injuries in car-cyclist accidents. Wearing a helmet is currently the most prevalent protection method against such injuries. Today, there is an ongoing debate about the ability of helmets to protect the cyclists' head-neck from injury. In the current study, we numerically reconstructed five real-world car-cyclist impact accidents, incorporating previously developed finite element models of four cyclist helmets to evaluate their protective performances. We made comparative head-neck injury predictions for unhelmeted and helmeted cyclists. The results show that helmets could clearly lower the risk of severe (AIS 4+) brain injury and skull fracture, as assessed by the predicted head injury criterion (HIC), while a relatively limited decrease in AIS 4+ brain injury risk can be achieved in terms of the analysis of CSDM0.25. Assessment using the maximum principal strain (MPS0.98) and head impact power (HIP) criteria suggests that helmets could lower the risk of diffuse axonal injury and subdural hematoma of the cyclist. The helmet efficacy in neck protection depends on the impact scenario. Therefore, wearing a helmet does not seem to cause a significant neck injury risk level increase to the cyclist. Our work presents important insights into the helmet's efficacy in protecting the head-neck of cyclists and motivates further optimization of protective equipment.
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Affiliation(s)
- Fang Wang
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China; (F.W.); (K.P.); (T.Z.); (Q.L.); (J.W.)
- Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha 410114, China
| | - Ke Peng
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China; (F.W.); (K.P.); (T.Z.); (Q.L.); (J.W.)
- Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha 410114, China
| | - Tiefang Zou
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China; (F.W.); (K.P.); (T.Z.); (Q.L.); (J.W.)
- Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha 410114, China
| | - Qiqi Li
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China; (F.W.); (K.P.); (T.Z.); (Q.L.); (J.W.)
- Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha 410114, China
| | - Fan Li
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China;
| | - Xinghua Wang
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China; (F.W.); (K.P.); (T.Z.); (Q.L.); (J.W.)
- Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha 410114, China
| | - Jiapeng Wang
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha 410114, China; (F.W.); (K.P.); (T.Z.); (Q.L.); (J.W.)
- Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha 410114, China
| | - Zhou Zhou
- Division of Neuronic Engineering, KTH Royal Institute of Technology, 14152 Stockholm, Sweden;
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Wang F, Huang J, Hu L, Hu S, Wang M, Yin J, Zou T, Li Q. Numerical investigation of the rider's head injury in typical single-electric self-balancing scooter accident scenarios. J R Soc Interface 2022; 19:20220495. [PMID: 36128701 PMCID: PMC9490341 DOI: 10.1098/rsif.2022.0495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 08/24/2022] [Indexed: 11/12/2022] Open
Abstract
As the use of electric self-balancing scooters (ESSs) increases, so does the number of related traffic accidents. Because of the special control method, mechanical structure and driving posture, ESSs are prone to various single-vehicle accidents, such as collisions with fixed obstacles and falls due to mechanical failures. In various ESS accident scenarios, the rider's head injury is the most frequent injury type. In this study, several typical single-ESS accident scenarios are reconstructed via computational methods, and the risk of riders' head/brain injury is assessed in depth using various injury criteria. Results showed that two types of ESSs (solo- and two-wheeler) do not have clear differences in head kinematics and head injury risks; the head kinematics (or falling posture) and ESS accident scenario exhibit a distinct effect on the head injury responses; half of the analysed ESS riders have a 50% probability of skull fracture, a few riders have a 50% risk of abbreviated injury scale (AIS) 4+ brain injury, and none has a diffuse axonal injury; the ESS speed plays an important role in producing the head/brain injury in ESS riders, and generally, higher ESS speed generates higher level of predicted head injury parameters. These findings will provide theoretical support for preventing head injury among ESS riders and data support for developing and legislating ESSs.
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Affiliation(s)
- Fang Wang
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan People's Republic of China
| | - Jiaxian Huang
- School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, Fujian, People's Republic of China
| | - Lin Hu
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan People's Republic of China
| | - Shenghui Hu
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan People's Republic of China
| | - Mingliang Wang
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan People's Republic of China
| | - Jiajie Yin
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan People's Republic of China
| | - Tiefang Zou
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan People's Republic of China
| | - Qiqi Li
- School of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan People's Republic of China
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Yu C, Wang F, Wang B, Li G, Li F. A Computational Biomechanics Human Body Model Coupling Finite Element and Multibody Segments for Assessment of Head/Brain Injuries in Car-To-Pedestrian Collisions. Int J Environ Res Public Health 2020; 17:E492. [PMID: 31941003 DOI: 10.3390/ijerph17020492] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 12/22/2019] [Accepted: 01/08/2020] [Indexed: 11/23/2022]
Abstract
It has been challenging to efficiently and accurately reproduce pedestrian head/brain injury, which is one of the most important causes of pedestrian deaths in road traffic accidents, due to the limitations of existing pedestrian computational models, and the complexity of accidents. In this paper, a new coupled pedestrian computational biomechanics model (CPCBM) for head safety study is established via coupling two existing commercial pedestrian models. The head–neck complex of the CPCBM is from the Total Human Model for Safety (THUMS, Toyota Central R&D Laboratories, Nagakute, Japan) (Version 4.01) finite element model and the rest of the parts of the body are from the Netherlands Organisation for Applied Scientific Research (TNO, The Hague, The Netherlands) (Version 7.5) multibody model. The CPCBM was validated in terms of head kinematics and injury by reproducing three cadaveric tests published in the literature, and a correlation and analysis (CORA) objective rating tool was applied to evaluate the correlation of the related signals between the predictions using the CPCBM and the test results. The results show that the CPCBM head center of gravity (COG) trajectories in the impact direction (YOZ plane) strongly agree with the experimental results (CORA ratings: Y = 0.99 ± 0.01; Z = 0.98 ± 0.01); the head COG velocity with respect to the test vehicle correlates well with the test data (CORA ratings: 0.85 ± 0.05); however, the correlation of the acceleration is less strong (CORA ratings: 0.77 ± 0.06). No significant differences in the behavior in predicting the head kinematics and injuries of the tested subjects were observed between the TNO model and CPCBM. Furthermore, the application of the CPCBM leads to substantial reduction of the computation time cost in reproducing the pedestrian head tissue level injuries, compared to the full-scale finite element model, which suggests that the CPCBM could present an efficient tool for pedestrian brain-injury research.
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