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Assessment of the Simulated Injury Monitor (SIMon) in Analyzing Head Injuries in Pedestrian Crashes

Journal Article
ISSN: 1946-3995, e-ISSN: 1946-4002
Published April 16, 2012 by SAE International in United States
Assessment of the Simulated Injury Monitor (SIMon) in Analyzing Head Injuries in Pedestrian Crashes
Citation: Ott, K., Wiechel, J., Guenther, D., Stammen, J. et al., "Assessment of the Simulated Injury Monitor (SIMon) in Analyzing Head Injuries in Pedestrian Crashes," SAE Int. J. Passeng. Cars - Mech. Syst. 5(1):487-505, 2012,
Language: English


Objectives. Examination of head injuries in the Pedestrian Crash Data Study (PCDS) indicates that many pedestrian head injuries are induced by a combination of head translation and rotation. The Simulated Injury Monitor (SIMon) is a computer algorithm that calculates both translational and rotational motion parameters relatable head injury. The objective of this study is to examine how effectively HIC and three SIMon correlates predict the presence of either their associated head injury or any serious head injury in pedestrian collisions.
Methods. Ten reconstructions of actual pedestrian crashes documented by the PCDS were conducted using a combination of MADYMO simulations and experimental headform impacts. Linear accelerations of the head corresponding to a nine-accelerometer array were calculated within the MADYMO model's head simulation. Injury risk calculated by SIMon (relative motion damage measure RMDM, cumulative strain damage measure CSDM, dilatation damage measure DDM) and HIC were compared to the presence or absence of either their associated injury or any serious head injury in each case using receiver operating characteristic (ROC) analysis.
Results. HIC (AUC = 0.91) and CSDM (AUC = 0.89) were both very effective at predicting their associated injury types (AIS 3+ skull fracture and DAI, respectively). DDM (AUC = 0.68) and RMDM (AUC = 0.56) were not as effective in predicting their respective injury types (contusion and acute subdural hematoma, respectively). However, HIC (AUC = 0.67) and CSDM (AUC = 0.62) were less effective than RMDM and DDM (AUC = 0.86 for both) for predicting any AIS 3+ head injury type.
Conclusions. For the ten cases evaluated in this study, each correlate was strong at predicting either its associated injury or any head injury. However, there was no single injury correlate that performed effectively in predicting both its associated injury and any AIS 3+ head injury. Because pedestrian head injuries are often associated with a combination of linear and rotational loading, supplementing HIC with correlates that capture other loading patterns could lead to more robust head injury assessment.