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Age-Infusion Approach to Derive Injury Risk Curves for Dummies from Human Cadaver Tests. Front Bioeng Biotechnol 2015;3:196 PMID: 26697422 PMCID: PMC4677537

Pubmed ID



Injury criteria and risk curves are needed for anthropomorphic test devices (dummies) to assess injuries for improving human safety. The present state of knowledge is based on using injury outcomes and biomechanical metrics from post-mortem human subject (PMHS) and mechanical records from dummy tests. Data from these models are combined to develop dummy injury assessment risk curves (IARCs)/dummy injury assessment risk values (IARVs). This simple substitution approach involves duplicating dummy metrics for PMHS tested under similar conditions and pairing with PMHS injury outcomes. It does not directly account for the age of each specimen tested in the PMHS group. Current substitution methods for injury risk assessments use age as a covariate and dummy metrics (e.g., accelerations) are not modified so that age can be directly included in the model. The age-infusion methodology presented in this perspective article accommodates for an annual rate factor that modifies the dummy injury risk assessment responses to account for the age of the PMHS that the injury data were based on. The annual rate factor is determined using human injury risk curves. The dummy metrics are modulated based on individual PMHS age and rate factor, thus "infusing" age into the dummy data. Using PMHS injuries and accelerations from side-impact experiments, matched-pair dummy tests, and logistic regression techniques, the methodology demonstrates the process of age-infusion to derive the IARCs and IARVs.

Author List

Yoganandan N, Banerjee A, Pintar FA


Anjishnu Banerjee PhD Assistant Professor in the Institute for Health and Equity department at Medical College of Wisconsin
Frank A. Pintar PhD Chair, Professor in the Biomedical Engineering department at Medical College of Wisconsin
Narayan Yoganandan PhD Professor in the Neurosurgery department at Medical College of Wisconsin

jenkins-FCD Prod-299 9ef562391eceb2b8f95265c767fbba1ce5a52fd6