The Modelling of Age in a Human Body Model and its Application in Trauma Predictions

Abstract

Road traffic accidents are catastrophic events leading to serious injury and in some cases fatality. Improving road safety requires knowledge of injury causation. The dichotomy is that traumatic injuries as a result of road traffic collisions are assessed using the Abbreviated Injury Scale (AIS), which is a measurement of the probability of death, whilst the engineering tools available to support the understanding of injury causation rely on engineering measurements of stress and strain. Further to this, age consideration is not adequately dealt with using existing engineering tools.

This thesis proposes the application of Peak Virtual Power (PVP) to establish a relationship between the AIS trauma scale and the engineering measurements of stress and strain. Further to this, and to account for age, the thesis proposes a change to the engineering tool by applying age effects to the physical parameters that define the Total Human Model for Safety (THUMS). The result is a new and innovative set of injury curves that enables a direct relation between the engineering measurements coming from the simulations and AIS for different pedestrian age groups.

The validity of this new approach is demonstrated by showing how the application of PVP and age improved injury prediction accuracy for four real-world pedestrian accident scenarios. In 28 organ blunt trauma predictions (4 accident cases, 7 organs), a non-aged model predicts 36% of AIS PM outcomes, while an aged model predicts 46%, the improvement on number of successful predictions is around 10%, with unpredicted terms, the AIS results are closer to PM reports on an age model. At the same time, computing a poly-trauma response, based on ISS, the aged model shows better prediction results compare with non-aged model. By calculating AIS and the corresponding injury severity score (ISS), the research concluded that aged human models, by changing the physical characteristics, improved injury prediction accuracy compared to non-aged ones.

The research also identified limitations in the simulation of the accident scenarios, as well as trauma predictions responses in some organs in the human body models. Under certain conditions differences between the trauma estimation and the PM reports were observed. Two reasons were identified: (a) body contacts with bonnet involving rolling over cannot be simply duplicated using a single impact scenario, and (b) the organs modelling method used in THUMS differs from a real-world human body as the interaction between organs (the contacts) differs between the engineering tool (THUMS) and the real-world human body. These are important findings of this research that need to be investigated further

Date of Award	2022
Original language	English
Awarding Institution	Coventry University
Supervisor	Christophe Bastien (Supervisor)