Ranking and Rating Bicycle Helmet Safety Performance in Oblique Impacts Using Eight Different Brain Injury Models

Abstract

Bicycle helmets are shown to offer protection against head injuries. Rating methods and test standards are used to evaluate different helmet designs and safety performance. Both strain-based injury criteria obtained from finite element brain injury models and metrics derived from global kinematic responses can be used to evaluate helmet safety performance. Little is known about how different injury models or injury metrics would rank and rate different helmets. The objective of this study was to determine how eight brain models and eight metrics based on global kinematics rank and rate a large number of bicycle helmets (n=17) subjected to oblique impacts. The results showed that the ranking and rating are influenced by the choice of model and metric. Kendall's tau varied between 0.50 and 0.95 when the ranking was based on maximum principal strain from brain models. One specific helmet was rated as 2-star when using one brain model but as 4-star by another model. This could cause confusion for consumers rather than inform them of the relative safety performance of a helmet. Therefore, we suggest that the biomechanics community should create a norm or recommendation for future ranking and rating methods.

Keywords: Bicycle helmet; Brain injury criteria; Concussion; Finite element models; Oblique impact tests; Test methods.

Figure 1

The test setup, from left to right Xrot, Yrot and Zrot together with the anatomical coordinate system of the head.

Figure 2

Boxplot of the different models for the three loading conditions based on seventeen different pulses per loading condition.

Figure 3

Boxplot of the different kinematic-based metrics for the three loading conditions with seventeen pulses per loading conditions.

Figure 4

Visualization of the two models with the highest (left) and lowest (right) Kendall’s tau. GHBMC and THUMS had a Kendall’s tau of 0.95 and GHBMC and UCDBTM a Kendall’s tau of 0.51. The circles indicate the different helmets from A to Q with the best performing helmet at the top and the worst performing helmet at the bottom. The lines between the circles are pulled between the same helmet for the different brain models.

Figure 5

The rating of helmet based on different brain models. The different colors are indicating the different helmets.

Figure 6

The rating of helmets based on different kinematic-based metrics. The different colors are indicating the different helmets.