Documentation:FIB book/Computational Injury Biomechanics Modelling in Cycling

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Introduction

The Canadian Vital Statistics: Death Database (CVSD) reports an average of 74 cycling deaths per year between 2006 and 2017 where 13% of the cyclists were wearing a helmet [1]. The leading cause of death for cyclists is head-related injuries, with bike helmets cutting the risk of serious head injury by 60% [2]. Considering the rising increase of cycling commuters (approximately two times more in 2016 compared to 1996 [3]), it has become increasingly apparent that cycling helmets play a crucial role in cycling commuter safety.

In terms of injury biomechanical research and development of cycling helmets, computational modelling and simulation can be used in place of physical models and simulations for the purposes of verifying the effectiveness of injury prevention and validating novel designs. Due to its general lack of vital resources such as cadavers or post-mortem surrogates, it has shown that it can be a more cost-effective approach; as well as not requiring the involvement of human participants, so that it can be less time consuming [4]. By utilizing computational modelling for helmet design, one can investigate critical factors such as material, geometrical and other design criteria for optimal efficiency and assessment of injury prevention [4].

Computational Modelling

Virtual model of 3D scanned head and designed bicycle helmet

Software & Testing

Modelling software that is commonly used in injury biomechanics includes MADYMO (Mathematical Dynamic Model), LS-DYNA, SolidWorks, Adams, Geomagic Studio 12, and Autodesk CFD (computational fluid dynamics). Each of these computer programs ranges from simple three-dimensional models to represent geometries up to complex analytical models that rely on the laws of physics to analyze the movement and injury response of the human body to impact.

In order to develop a computational simulation, certain assumptions need to be made when information is unavailable or perhaps unreliable. For example, a finite element study of head impact with and without a helmet was conducted using a simplified model of the head and neck, and it was assumed that the initial contact location was where a contusion was shown in the real accident data [5]. Furthermore, the ground was assumed to be a rigid surface [5]. Even taking these considerations into account, the results indicated high levels of stress in the area that the fractures were shown in the real data, and it gave motivation to the fact that wearing a helmet significantly reduced the probability of skull fracture [5].  

It should also be recognized that computational simulations can give answers to other questions that other injury analyses cannot answer, or at least with less effort that is safer and more efficient. Different accident scenarios can be considered regarding impact speeds, the significance of how the loading or acceleration is experienced, and to determine the effectiveness of wearing a helmet without having to investigate on the tissue level [5].  

For the purposes of assessing cycling helmets, one study by K. Hansen et al. selected a model known as Strasbourg University Finite Element Head Model (SUFEHM) for the analysis of risk of Traumatic Brain Injury [6]. This had been previously validated by comparing the model to cadaver impact tests in a study by R. Wilinger, H.S. Kang and B. Diaw, in which they simulated and recorded intracranial pressures at five locations on the skull [7]. The data plotted from the cadaver tests and the finite element model were similar for direct head impacts for a short duration (~6ms), but not similar for longer durations, indicating a limitation and room for further development of the model [7]. The model itself is comprised of 13,208 elements that represent the skull, face, falx, tentorium, subarachnoid space, scalp, cerebrum, cerebellum and brain stem while also accounting for cerebrospinal fluid (CSF), the arachnoid and the dura mater [6], [7]. In terms of replicating the mechanical properties of the human head, the model attempts to replicate the brain’s viscoelasticity in shear behaviour [7]. For the study that evaluated the risk of TBI, the model was successfully able to provide valid injury tolerance curves that correlate an intracerebral stress response to the risk of TBI [6].

Numerical simulations also exist for improving the efficiency of cycling helmets as seen in a study by T.Z. Desta et al., where a Computational Fluid Dynamics (CFD) model was utilized [8]. CFD models mathematically represent the fluid flow in terms of fluid velocity, pressure, temperature and other variables (e.g., turbulence) [8]. From this model, the researchers were able to quantify the ventilation effectiveness of cycling helmets and investigate thermal regulation between two different types of helmets [8]. When it is not possible to conduct full-scale experiments, CFD can be used to generate data, such as during the early stages of design [8].

Validation

Computational models have played roles in experimental design and research for cycling helmet design ranging from material selection [4], [9], shock absorption [4], crash and accident reconstruction [5], [10], [11], risk assessment [6], [12], and optimization [8], [13]. The creation of computational models allow researchers and designers access to informative parameters that can later be used to evaluate the above. There are currently studies that validate the use of computational models in cycling helmet design by comparing results found in the computational model to real-life scenarios and data.

One example of a study by Teng et al., created and assessed a finite element model using LS-DYNA software to assess the performance of cycling helmets in shock absorption testing [4]. The study involved measuring the linear accelerations, rotational accelerations and impact forces experienced by the head while wearing the bicycle helmet and later compared these results with experimental data [4]. The head and helmet finite element models were coupled and the study found that the model was able to predict HIC values that were relatively close to the experimental setup it was being compared to (finite element model: 841.7, experimental results: 720, while these numbers differ, both are under the injury threshold of ~1000) [4]. In addition to this, they plotted the data of the linear accelerations and obtained a similar shape and peaks of the finite element model to the experimental data [4].

In another study by Milne et al., a finite element model was implemented using LS-DYNA software for assessing the degrees of protection given by commercial bicycle helmets under standard impact conditions [9]. This model was also compared to experimental data that was numerically reproduced and obtained similar results of the head form for acceleration time-history and peak g values [9]. However, it was also found that further improvement is needed for impact points located laterally and close to the helmet edge [9].

To reiterate, computational modelling has been shown to produce results and replicate real-life scenarios to a certain extent such as improving the design for cycling helmets. The models typically are validated through comparison to physical tests (e.g., cadaver testing) to verify that the models are simulating a realistic response. It is important to note that the validation of these models is specific and may not be used in scenarios in which they were not validated; for instance, the studies discussed here are developed for cycling helmet (head impact) simulations and would therefore likely not be valid for other types of contexts (e.g., developing sports' helmets).

Airflow lines and velocity diagram of a human head wearing a bicycle helmet
Airflow lines and velocity diagram of a human head wearing a bicycle helmet

Accident Reconstruction

Computer simulations can be used for accident reconstruction and assess the protective qualities of safety equipment. Two studies evaluated the protectiveness of cycling helmets using available software: LS Dyna [5] and MADYMO [10]. The first study simulated three real-life accidents where the cyclist landed on their head, comparing the stress pattern created by the finite element model to medical imaging of the cyclists' heads from the actual injury [5]. The second study used MADYMO to model four types of accidents: loss of control of bicycle, curb impact, side impact by vehicle, and rear impact by vehicle [10]. This study used AIS, HIC and Nij to determine if injury occurred on the model for the cyclist [10]. Another study used MADYMO to simulate cycling accidents for children across various speeds [12]. To model cyclists, the last two studies used the 50th percentile male [10] and six year old Hybrid III anthropometric device (ATD) [12]. Across these studies, many variables and functions were taken from literature (e.g., peak accelerations, coefficient of friction) and thus, they hold assumptions which need to be understood critically. For example, readers need to determine how the values of certain variables were determined and whether it is representative of the population.

Discussion

Computational modelling has been investigated and validated throughout many studies and different areas of research. For the purposes of designing, developing and improving bicycle helmets to prevent head injury for cycling commutes and leisure activities, one can observe complications as outlined below. These being: the limitations of computational models used; accounting for the helmet fit (customization) over a large, diverse population; and the use of anthropometric test devices (ATDs) to represent cyclists.

Limitations of the Head Model

When considering the main objective of helmet design for cycling is to prevent head injury, it makes sense that the development of computational models focus primarily on the head & brain regions as seen in many studies. In terms of finite element models, it is shown in many studies that the primary objective is to develop a head and/or brain finite element model and pair this with a helmet finite element model for simulations.

One potential oversight to this is that certain elements/parts of the body are excluded from simulations (e.g., neck, torso, extremities). Depending on the method of injury being evaluated such as direct head impacts, this may or may not have a certain amount of influence on the results obtained in the computational simulation when a head versus an entire human body is considered. This can be present in terms of mass differences and/or other parts of the body being involved in causing the head injury. However, the development of a whole-body human model can be costly, time-consuming and difficult to justify if the changes in results are negligible.

Another consideration is the degree of accuracy and amount of detail put into these models. It is at the researchers liberty to account for as much detail as they deem necessary as there is no official standard for this practice.

Accounting for Helmet Fit

For safety and effective use of helmets, the helmet wearer and/or computational model needs to account for appropriate size and fit of the helmet around the head region. Helmets that are not fitted well or worn appropriately may report a higher risk of head injury [14]. Several studies do not report their assumptions about how well-fitted the helmet model is on the simulated model of the cyclist’s head. Of the studies considered in this review, only one study stated that the cyclist used a properly fitted helmet and specified a range of moments to account for possible helmet slip [12]. This can be problematic as it can alter results obtained in the studies if left unaccounted for.

Anthropometric Testing Devices to Represent Cyclists

Computational models and simulations require the input of certain parameters in order to create an accurate model, such as anthropometric data of cyclists. To create these models and assess injury, parameters specific to the Hybrid III were used in some studies [10], [12]. The Hybrid III is an anthropometric testing device (ATD) created for the purpose of testing automobiles. This ATD has been verified and validated for the purpose of automobile testing through comparison to post-mortem human surrogates to improve biofidelity. Researchers justify their decision to use the Hybrid III’s parameters by emphasizing its prevalence in the automobile industry [10], and having a standardized form of assessing injury.

However, this ATD has not been validated to represent cyclists. Simulations and models using an ATD such as the Hybrid III intended for automobile testing should be regarded with caution. This is due to the fact that the Hybrid III has primarily been developed to recreate realistic movement and corresponding biofidelity in automobile collisions which does not always translate to cycling. Additionally, one study noted that a “limitation of [the Hybrid III] is that its joints move passively rather than moving to protect or brace, [thus] it is important to consider the results in this context.” [12]

Future Research

Computational modelling has its limitations and assumptions have to be made in order to account for missing information. Although most of these results are verified with other methods of analysis, there is a need to further understand the significance of these assumptions and limitations. Some of the data used in the aforementioned studies were from older experiments that rely on ATDs and surrogates of that time, and the human body has evolved and the industry has advanced considerably — it requires an audit in order to better represent the injury statistics, population and research found today. 

As previously mentioned, simulations use either a simplified model or the more advanced option: SUFEHM from Humanetics [6]. Currently, the SUFEHM is recognized as one of the best human head models in the world and is useful for predicting head injury [6]. If a finite element model of the human body was developed with the same level of anatomical features like the SUFEHM, we could have a better understanding of injury analysis (e.g., understanding energy dissipation throughout the body during injury). Furthermore, with the advancement in ATD technology and the increased usage of bicycles, it might be appropriate to develop an ATD designed to represent the biofidelity of commuter cyclists.

References

  1. ↑ Statistics Canada, “Circumstances surrounding cycling fatalities in Canada, 2006 to 2017,” Jul. 31, 2019. https://www150.statcan.gc.ca/n1/pub/82-625-x/2019001/article/00009-eng.htm (accessed Nov. 10, 2021).
  2. ↑ Parachute, “Cycling,” Aug. 27, 2021. https://parachute.ca/en/injury-topic/cyling/ (accessed Nov. 10, 2021).
  3. ↑ O. Moore, “Census 2016: Spike in number of Canadians cycling, taking public transit to work,” The Globe and Mail, Nov. 29, 2017. https://www.theglobeandmail.com/news/national/census-2016-spike-in-number-of-canadians-cycling-taking-public-transit-to-work/article37127643/ (accessed Nov. 10, 2021).
  4. ↑ 4.0 4.1 4.2 4.3 4.4 4.5 4.6 4.7 T. L. Teng, C. L. Liang, and V. H. Nguyen, “Development and validation of finite element model of helmet impact test,” Proc. Inst. Mech. Eng. Part L J. Mater. Des. Appl., vol. 227, no. 1, pp. 82–88, Jan. 2013, doi: 10.1177/1464420712451806.
  5. ↑ 5.0 5.1 5.2 5.3 5.4 5.5 5.6 M. Fahlstedt, P. Halldin, and S. Kleiven, “The protective effect of a helmet in three bicycle accidents—A finite element study,” Accid. Anal. Prev., vol. 91, pp. 135–143, Jun. 2016, doi: 10.1016/J.AAP.2016.02.025.
  6. ↑ 6.0 6.1 6.2 6.3 6.4 6.5 K. Hansen et al., “Angular Impact Mitigation system for bicycle helmets to reduce head acceleration and risk of traumatic brain injury,” Accid. Anal. Prev., vol. 59, pp. 109–117, Oct. 2013, doi: 10.1016/J.AAP.2013.05.019.
  7. ↑ 7.0 7.1 7.2 7.3 R. Willinger, H. S. Kang, and B. Diaw, “Three-Dimensional Human Head Finite-Element Model Validation Against Two Experimental Impacts,” Ann. Biomed. Eng. 1999 273, vol. 27, no. 3, pp. 403–410, 1999, doi: 10.1114/1.165.
  8. ↑ 8.0 8.1 8.2 8.3 8.4 T. Z. Desta, G. De Bruyne, J.-M. Aerts, M. Baelmans, and D. Berckmans, “Numerical Simulation and Ventilation Efficiency of Bicycle Helmets,” Computer Modeling in Engineering \& Sciences , vol. 31, no. 2. 2008, doi: 10.3970/cmes.2008.031.061.
  9. ↑ 9.0 9.1 9.2 9.3 G. Milne, C. Deck, R. P. Carreira, Q. Allinne, and R. Willinger, “Development and validation of a bicycle helmet: assessment of head injury risk under standard impact conditions,” Comput. Methods Biomech. Biomed. Engin., vol. 15, no. Suppl 1, pp. 309–310, 2012.
  10. ↑ 10.0 10.1 10.2 10.3 10.4 10.5 10.6 D. S. McNally and S. Whitehead, “A computational simulation study of the influence of helmet wearing on head injury risk in adult cyclists,” Accid. Anal. Prev., vol. 60, pp. 15–23, Nov. 2013, doi: 10.1016/J.AAP.2013.07.011.
  11. ↑ A. I. Radu, D. D. Trusca, G. R. Toganel, and B. C. Benea, “Designing and testing a stand used to simulate the dummy head impact with different surfaces using CAD software,” in IOP Conference Series: Materials Science and Engineering, 2020, pp. 1–9, doi: 10.1088/1757-899X/997/1/012058.
  12. ↑ 12.0 12.1 12.2 12.3 12.4 12.5 D. S. McNally and N. M. Rosenberg, “MADYMO simulation of children in cycle accidents: A novel approach in risk assessment,” Accid. Anal. Prev., vol. 59, pp. 469–478, Oct. 2013, doi: 10.1016/J.AAP.2013.07.022.
  13. ↑ T. Y. Pang, T. S. T. Lo, T. Ellena, H. Mustafa, J. Babalija, and A. Subic, “Fit, stability and comfort assessment of custom-fitted bicycle helmet inner liner designs, based on 3D anthropometric data,” Appl. Ergon., vol. 68, pp. 240–248, Apr. 2018, doi: 10.1016/J.APERGO.2017.12.002.
  14. ↑ Province of Manitoba, “Bike Safety.” https://www.gov.mb.ca/health/hep/bikesafety/ (accessed Nov. 10, 2021).
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