Helmets are designed to protect the wearer from impacts that may cause serious injuries or trauma. Standardized tests are conducted to ensure their blunt impact absorption performance. Small variations when mounting and positioning any helmet on the headform during the test lead to uncertainty on the maximum peak linear acceleration, a threshold used to assess bicycle, wheeled recreational devices, and combat helmets for their impact absorption. Such uncertainty may lead to fallacious results and, thus, incorrect approval for a helmet. In turn, the unqualified helmet can significantly increase injury risk calculated through the Abbreviated Injury Scale. This study quantifies the uncertainty of the helmet positioning and the other blunt impact test parameters on the peak linear acceleration. For this purpose, over 1400 variations of a helmet blunt impact computational model were considered. The uncertainty quantification analysis was conducted through Sobol Sensitivity Analysis and Shapely Additive Explanations obtained from a Light Gradient Boosting Machine model, a decision support model developed to assist the experimental blunt impact test for helmets. The results indicated that the helmet positioning parameters had the highest contribution to the uncertainty. Additionally, the proposed model successfully determined whether a helmet passed or failed the test. The accuracy level of these predictions was at 80.17% when the helmet positioning parameters vary from one test to the next, and at 98.89% when considering the helmet was positioned correctly.
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