Research Papers

Differences in Impact Performance of Bicycle Helmets During Oblique Impacts

[+] Author and Article Information
Megan L. Bland

Department of Biomedical Engineering
and Mechanics,
Virginia Tech,
440 Kelly Hall,
325 Stanger Street,
Blacksburg, VA 24061
e-mail: mbland27@vt.edu

Craig McNally

Center for Injury Biomechanics,
Virginia Tech,
2280 Kraft Drive VCOM II Building,
Blacksburg, VA 24060
e-mail: cmcnally@vt.edu

Steven Rowson

Department of Biomedical Engineering and
Virginia Tech,
343 Kelly Hall,
325 Stanger Street,
Blacksburg, VA 24061
e-mail: rowson@vt.edu

1Corresponding author.

Manuscript received August 18, 2017; final manuscript received April 10, 2018; published online May 24, 2018. Assoc. Editor: Barclay Morrison.

J Biomech Eng 140(9), 091005 (May 24, 2018) (10 pages) Paper No: BIO-17-1373; doi: 10.1115/1.4040019 History: Received August 18, 2017; Revised April 10, 2018

Cycling is a leading cause of sport-related head injuries in the U.S. Although bicycle helmets must comply with standards limiting head acceleration in severe impacts, helmets are not evaluated under more common, concussive-level impacts, and limited data are available indicating which helmets offer superior protection. Further, standards evaluate normal impacts, while real-world cyclist head impacts are oblique—involving normal and tangential velocities. The objective of this study was to investigate differences in protective capabilities of ten helmet models under common real-world accident conditions. Oblique impacts were evaluated through drop tests onto an angled anvil at common cyclist head impact velocities and locations. Linear and rotational accelerations were evaluated and related to concussion risk, which was then correlated with design parameters. Significant differences were observed in linear and rotational accelerations between models, producing concussion risks spanning >50% within single impact configurations. Risk differences were more attributable to linear acceleration, as rotational varied less between models. At the temporal location, shell thickness, vent configuration, and radius of curvature were found to influence helmet effective stiffness. This should be optimized to reduce impact kinematics. At the frontal, helmet rim location, liner thickness tapered off for some helmets, likely due to lack of standards testing at this location. This is a frequently impacted location for cyclists, suggesting that the standards testable area should be expanded to include the rim. These results can inform manufacturers, standards bodies, and consumers alike, aiding the development of improved bicycle helmet safety.

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Grahic Jump Location
Fig. 1

Example radius of curvature determination process for a frontal impact location. The impact center was specified (indicated by the upper X), then tangential lines were calculated using points 0.5 cm along the helmet edge to either side of the center. Radial lines were then calculated, and the intersection of these lines determined (lower X). Radius, R was computed as the distance between the intersection and the impact center.

Grahic Jump Location
Fig. 2

Headform orientation for a frontal impact (left) and a temporal impact (right). The anvil was sloped 30 deg from the horizontal to generate appropriate normal and tangential incident velocities. The offset angle between the neck and the anvil was 15 deg. Impact centers were mirrored across the midsagittal plane of the helmet and were a minimum of 120 mm apart.

Grahic Jump Location
Fig. 3

Empirical cumulative distribution function (CDF) of helmet damage replication data [12,13]. Average PLA±one standard deviation ranges were mapped to CDF percentiles, which were then used to determine weightings for each impact configuration based on an assumed total of 100 impacts. The dashed lines show an example of this range for the frontal-5.1 m/s results, which corresponded with a weighting of 20.5.

Grahic Jump Location
Fig. 4

Distributions of PLA (top) and peak rotational acceleration (PRA) (bottom) by impact configuration. Lower and upper bounds of boxes correspond with 25th and 75th percentiles, respectively, and whiskers extend to ±2.7σ. At each velocity, the frontal location tended to produce lower PLAs than the temporal location. PRA varied less across impact location.

Grahic Jump Location
Fig. 5

PLA (top) and PRA (bottom) per helmet model and location at each velocity. Error bars are standard deviations. Significance groupings are shown in Tables 2 and 3. The temporal location generally produced more significant differences in PLA between models. PRA showed fewer differences with the exception of GMIPS at the temporal location.

Grahic Jump Location
Fig. 6

Concussion risk per helmet model and location for the two impact velocities. Error bars are standard deviations. Significance groupings are shown in Table 4. Risk varied drastically between models, spanning over 50% between helmet models in some impact configurations.

Grahic Jump Location
Fig. 7

Correlations between WCR and helmet price (top), liner thickness or mass (middle), and predictive HLM results from MLR (bottom) for each location. Price and liner thickness were the most significantly correlated with WCR at the frontal location, while price and mass were the most significantly correlated with WCR at the temporal location.



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