Towards the Inclusion of Pelvis Population Variance in Human Body Models
Licentiatavhandling, 2022

With a future large-scale introduction of autonomous vehicles, the proportion of intersection crashes on the total number of motor vehicle crashes is expected to increase. The pelvis is frequently exposed to high loads in several of these impacts. In addition, autonomous driving is expected to result in new seating positions where reclined seating increases the risk of the pelvis sliding under the lap belt, producing submarining induced injuries. If unaddressed, submarining may result in an increased prevalence of abdominal and spinal injuries, and if addressed by advanced restraint systems, the risk of pelvic fractures may increase due to higher pelvis loads.

Finite Element Human Body Models (FE-HBMs) represent the most advanced tool available to use in the design of safety systems for current and future vehicles. FE-HBMs represent the human anatomy, anthropometry, and physical properties to predict a biomechanical response to external loading via computer simulations. To date, these models are typically defined based on an average male or female subject in terms of global measurements like age, stature, and weight. However, individual variability is an intrinsic property of humans that must be considered in order to capture the vulnerable population and maximise the efficiency of vehicle safety systems. FE-HBMs provides the opportunity to include both geometrical and material variability in the analysis.

In this thesis, methods/tools that enable inclusion of pelvis population variance in HBMs were developed. As part of this work, the population variance in pelvis shape has been described and a morphometric model capable of predicting pelvis shape was developed. A new generic pelvis FE-model was generated from the average pelvis geometry, which can be morphed to the population variance in pelvis shape. The model was validated for lateral impacts followed by a sensitivity analysis on model response to input variance. Results show that while 90% of the population shape variance was captured in the analysis, only 29% was predicted by a morphometric model using sex, age, stature, and BMI, as independent variables. The sensitivity analysis found that material properties account for the majority of the response variance (≈50-65%) in pelvis lateral impacts, and that input variables controlling shape contribute by a similar magnitude (≈35-40%).

Increased knowledge about population variability, and inclusion in future safety evaluations, can result in more robust systems that would reduce the risk of pelvis injuries in real-world accidents.

Finite Element Human Body Model

Sparse Principal Component Analysis

Global Sensitivity Analysis

Pelvis

Population Variance

Alfa-salen i hus Saga, Forskningsgången 3, Chalmers Lindholmen
Opponent: Associate Professor Jason R. Kerrigan, University of Virginia, Center for Applied Biomechanics, USA

Författare

Erik Brynskog

Chalmers, Mekanik och maritima vetenskaper, Fordonssäkerhet, Personskadeprevention

Predicting pelvis geometry using a morphometric model with overall anthropometric variables

Journal of Biomechanics,; Vol. 126(2021)

Artikel i vetenskaplig tidskrift

Brynskog E., Iraeus J., Pipkorn B., Davidsson J. Population Variance in Pelvis Response to Lateral Impacts – A Global Sensitivity Analysis.

Höft- och ryggskadepredikteringsmodeller för kvinnor och män i varierande fordon sittställningar i framtida autonoma fordon

FFI - Fordonsstrategisk forskning och innovation (2018-04998), 2019-04-01 -- 2022-03-31.

Drivkrafter

Hållbar utveckling

Ämneskategorier

Teknisk mekanik

Annan medicinteknik

Farkostteknik

Infrastruktur

C3SE (Chalmers Centre for Computational Science and Engineering)

Thesis for the degree of Licentiate – Department of Mechanics and Maritime Sciences: 2022:01

Utgivare

Chalmers

Alfa-salen i hus Saga, Forskningsgången 3, Chalmers Lindholmen

Online

Opponent: Associate Professor Jason R. Kerrigan, University of Virginia, Center for Applied Biomechanics, USA

Mer information

Senast uppdaterat

2022-04-01