Analysis of minimum pulse shape information needed for accurate chest injury prediction in real life frontal crashes

Abstract

The relationship between crash pulse shape and injury risk has been studied primarily with laboratory studies, but these are not necessarily representative of most real-life crashes. For the past decade, pulse information from real-life crashes has been available through event data recorders. The aim of this study is to evaluate how crash pulses from event data recorders can be parameterized with as few parameters as possible without losing the ability to accurately predict occupant injury. Pulses from 122 NASS/CDS cases with a delta velocity over 40 km/h were parameterized using eigenvector analysis. Six different pulses were created for each of these cases, including the original pulse and five approximations with gradually more pulse information. Using a finite-element sled model with the detailed THUMS human body model, the risk of chest injury was evaluated for each pulse version in each case. By comparing the results from each pulse approximation to the original pulse, the change in chest injury could be evaluated as a function of pulse approximation for each case. Using linear regression to analyse the chest injury error results it was found that a pulse with as few as four parameters-delta velocity, duration, and two shape parameters-can sufficiently describe the pulse shape from a chest injury point of view.

Protection challenges in seat positions with large rearward adjustment in frontal collisions: An approach using stochastic human body model simulations

“…The model was originally derived from laser scans of 14 cars and validated with their respective crash test data to analyze driver rib fractures in nearside oblique frontal accidents (Iraeus, 2015;Iraeus and Lindquist, 2016). Variants of the model have since been used to analyze the influence of crash pulse parameters on rib fractures (Iraeus and Lindquist, 2020) and study rib fracture risk as a function of age and rib strain (Larsson et al, 2021). For use in the Therefore, the open section on the inboard side of the footwell, which could accommodate for the deformations of the footwell material, was closed with elements of the same material as the footwell part.…”

“…While in a previous study, a simple algorithm derived from rigid wall crash test data was presented (Ressi et al, 2020), it did not produce satisfactory results with the moderate overlap crash test data and was therefore rejected for the present paper. In a recent study using the GVI, the airbag TTD was estimated using the median value derived from event data recorders (Iraeus and Lindquist, 2020). Since the TTD values observed in the 43 moderate overlap crash tests varied between 16 and 52 ms for the same well-defined collision scenario (i.e., identical initial velocity, overlap, and barrier configuration), a random TTD selection was deemed more realistic for the present study than a single median value for all cases.…”

“…One important aspect in this could be the expected under-reporting of mTBI in accident databases (Wu et al, 2022), resulting in lower MAIS2+ head injury rates in the IGLAD sample. On the other hand, while the GVI was validated with crash test data from 14 vehicles using crash test dummies, the model was subsequently utilized primarily for strain-based rib fracture prediction (Iraeus, 2015;Iraeus and Lindquist, 2016;Iraeus and Lindquist, 2020;Larsson et al, 2021). Additional GVI model validation focusing on the head-airbaginteraction could potentially improve the results.…”

“…This is considerable lower than the 11.6% thoracic injury frequency observed in the IGLAD sample (listed in Table 2). To check these results for plausibility, the "Forman smoothed" IRF (Iraeus and Lindquist, 2020) was also implemented. While at 1.32%, this function predicted a slightly higher rate of AIS2+ thorax injuries, this rate is still considerably smaller than the real-world observation.…”

Rib Cortical Bone Fracture Risk as a Function of Age and Rib Strain: Updated Injury Prediction Using Finite Element Human Body Models

“…The drawback with this type of function is that very small strain increments can give large risk increments, which is an undesired feature in design optimization. To overcome this limitation, the framework was updated with a smooth risk curve ( Iraeus and Lindquist, 2020 ). The same Kemper et al (2005) ; Kemper et al (2007) strain data and 5.1% reduction used for the Forman 2012 risk curve was used, but a Weibull distribution was fitted.…”