A Generic Trauma Severity Computer Method Applied to Pedestrian Collisions

TL;DR

This paper introduces a novel Organ Trauma Model (OTM) for assessing pedestrian brain injury severity, which improves upon existing methods by incorporating impact specifics, aging effects, and hemorrhage prediction, validated against real-world accidents.

Contribution

The paper presents a new trauma severity model that predicts brain injuries more accurately than traditional methods, including aging and hemorrhage effects, and validated with real pedestrian accident data.

Findings

OTM predicts injury severity closer to real outcomes than MPS-based methods.

Incorporates aging effects and hemorrhage modeling into trauma prediction.

Validated against three real pedestrian accidents with promising results.

Abstract

In the real world, the severity of traumatic injuries are measured using the Abbreviated Injury Scale (AIS). However the AIS scale cannot currently be computed by using finite element human computer models, which calculate a maximum principal strains (MPS). Further, MPS only establishes a threshold above which a serious or fatal injury occurs. In order to overcome these limitations, a unique Organ Trauma Model (OTM) able to calculate the threat to life of any organ injury is proposed. The focus, in this case is on real world pedestrian brain injuries. The OTM uses a power method, named Peak Virtual Power (PVP), and defines brain white and grey matters trauma responses as a function of impact location and impact speed extracted from the pedestrian collision kinematics. This research has included ageing in the injury severity computation by including soft tissue material degradation, as well as brain volume changes. Further, to account for the limitations of the Lagrangian formulation of the brain model in representing haemorrhage, an approach to include the effects of subdural hematoma is proposed and included as part of the OTM predictions in this study. The OTM model was tested against three real-life pedestrian accidents and has proven to reasonably predict the Post Mortem (PM) outcome. Its AIS predictions are closer to the real world injury severity than standard MPS methods currently recommended. This study suggests that the OTM has the potential to improve forensic predictions as well as contribute to the improvement in vehicle safety design through the ability to measure injury severity. This study concludes that future advances in trauma computing would require the development of a brain model which could predict haemorrhaging.