Showing papers by "Jason R. Kerrigan published in 2021"

Comparison of porcine and human adipose tissue loading responses under dynamic compression and shear: A pilot study

Abstract: Understanding the mechanical properties of human adipose tissue, and its influence on seat belt-pelvis interaction is beneficial for computational human body models that are developed for injury prediction in the vehicle crashworthiness simulations. While various studies have characterized adipose tissue, most of the studies used porcine adipose tissue as a surrogate, and none of the studies were performed at loading rates relevant for motor vehicle collisions. In this work, the mechanical response of human and porcine adipose tissue was studied. Two dynamic loading modes (compression and simple shear) were tested in adipose tissue extracted from the human abdomen and porcine back. An Ogden hyperelastic model was used to fit the loading response, and specific material parameters were obtained for each specimen. Two-sample t-tests were performed to compare the effective shear moduli and peak stresses from porcine and human samples. The material response of the human adipose tissue was consistent with previous studies. Porcine adipose tissue was found to be significantly stiffer than human adipose tissue under compression and shear loading. Also, when material model parameters were fit to only one loading mode, the predicted response in the other mode showed a poor fit.

Kinematic and Injury Response of Reclined PMHS in Frontal Impacts.

Abstract: Frontal impacts with reclined occupants are rare but severe, and they are anticipated to become more common with the introduction of vehicles with automated driving capabilities. Computational and physical human surrogates are needed to design and evaluate injury countermeasures for reclined occupants, but the validity of such surrogates in a reclined posture is unknown. Experiments with post-mortem human subjects (PMHS) in a recline posture are needed both to define biofidelity targets for other surrogates and to describe the biomechanical response of reclined occupants in restrained frontal impacts. The goal of this study was to evaluate the kinematic and injury response of reclined PMHS in 30 g, 50 km/h frontal sled tests. Five midsize adult male PMHS were tested. A simplified semi-rigid seat with an anti-submarining pan and a non-production threepoint seatbelt (pre-tensioned, force-limited, seat-integrated) were used. Global motions and local accelerations of the head, pelvis, and multiple vertebrae were measured. Seat and seatbelt forces were also measured. Injuries were assessed via post-test dissection. The initial reclined posture aligned body regions (pelvis, lumbar spine, and ribcage) in a way that reduced the likelihood of effective restraint by the seat and seatbelt: the occupant's pelvis was initially rotated posteriorly, priming the occupant for submarining, and the lumbar spine was loaded in combined compression and bending due to the inertia of the upper torso during forward excursion. Coupled with the high restraining forces of the seat and seatbelt, the unfavorable kinematics resulted in injuries of the sacrum/coccyx (four of five PMHS injured), iliac wing (two of five PMHS injured), lumbar spine (three of five PMHS injured), and ribcage (all five PMHS suffered sternal fractures, and three of five PMHS suffered seven or more rib fractures). The kinematic and injury outcomes strongly motivate the development of injury criteria for the lumbar spine and pelvis, the inclusion of intrinsic variability (e.g., abdomen depth and pelvis shape) in computational simulations of frontal impacts with reclined occupants, and the adaptation of comprehensive restraint paradigms to predicted variability of occupant posture.

In Vitro Mechanical Characterization and Modeling of Subcutaneous Adipose Tissue: A Comprehensive Review.

Abstract: Mechanical models of adipose tissue are important for various medical applications including cosmetics, injuries, implantable drug delivery systems, plastic surgeries, biomechanical applications such as computational human body models for surgery simulation, and blunt impact trauma prediction. This article presents a comprehensive review of in vivo experimental approaches that aimed to characterize the mechanical properties of adipose tissue, and the resulting constitutive models and model parameters identified. In particular, this study examines the material behavior of adipose tissue, including its nonlinear stress-strain relationship, viscoelasticity, strain hardening and softening, rate-sensitivity, anisotropy, preconditioning, failure behavior, and temperature dependency.

Failure Tolerance of the Human Lumbar Spine in Combined Compression and Flexion Loading

Abstract: Vehicle safety systems have substantially decreased motor vehicle crash-related injuries and fatalities, but injuries to the lumbar spine still have been reported Experimental and computational analyses of upright and, particularly, reclined occupants in frontal crashes have shown that the lumbar spine can be subjected to axial compression followed by combined compression-flexion loading Lumbar spine failure tolerance in combined compression-flexion has not been widely explored in the literature Therefore, the goal of this study was to measure the failure tolerance of the lumbar spine in combined compression and flexion Forty 3-vertebra lumbar spine segments were pre-loaded with axial compression and then subjected to dynamic flexion bending until failure Clinically relevant middle vertebra fractures were observed in twenty-one of the specimens, including compression and burst fractures The remaining nineteen specimens experienced failure at the potting grip interface Since specimen characteristics and pre-test axial load varied widely within the sample, failure forces (mean 34 kN, range 16-51 kN) and moments (mean 73 Nm, range 0-181 Nm) also varied widely Tobit univariate regressions were performed to determine the relationship between censored failure tolerance and specimen sex, segment type (upper/lower), age, and cross-sectional area Age, sex, and cross-sectional area significantly affected failure force and moment individually (p<00024) These data can be used to develop injury prediction tools for lumbar spine fractures and further research in future safety systems

Effect of Temperature and Freezing on Human Adipose Tissue Material Properties Characterized by High-Rate Indentation: Puncture Testing.

Abstract: The characterization of human subcutaneous adipose tissue (SAT) under high-rate loading is valuable for development of biofidelic finite element human body models (FE-HBMs) to predict seat belt-pelvis interaction and injury risk in vehicle crash simulations. While material characterization of SAT has been performed at 25 °C or 37 °C, the effect of temperature on mechanical properties of SAT under high-rate and large-deformation loading has not been investigated. Similarly, while freezing is the most common preservation technique for cadaveric specimens, the effect of freeze-thaw on the mechanical properties of SAT is also absent from the literature. Therefore, the aim of this study was to determine the effect of freezing and temperature on mechanical properties of human SAT. Fresh and previously frozen human SAT specimens were obtained and tested at 25 °C and 37 °C. High-rate indentation and puncture tests were performed, and indentation-puncture force-depth responses were obtained. While the chance of material failure was found to be different between temperatures and between fresh and previously frozen tissue, statistical analyses revealed that temperature and freezing did not change the shear modulus and failure characteristics of SAT. Therefore, the results of the current study indicated that SAT material properties characterized from either fresh or frozen tissue at either 25 °C or 37 °C could be used for enhancing the biofidelity of FE-HBMs.

Leveraging machine learning for predicting human body model response in restraint design simulations.

Abstract: The objective of this study was to leverage and compare multiple machine learning techniques for predicting the human body model response in restraint design simulations. Parametric simulations with 16 independent variables were performed. Ordinary least-squares (OLS), least absolute shrinkage and selection operator (LASSO), neural network (NN), support vector regression (SVR), regression forest (RF), and an ensemble method were used to develop response surface models of the simulations. The hyperparameters of the machine learning techniques were optimized through grid search and cross-validation to avoid under-fitting and over-fitting. The ensemble method outperformed other techniques, followed by LASSO, SVR, NN, RF, and OLS. Findings indicated that optimizing the metamodel hyper-parameters are essential to predict the optimum set of restraint design parameters.

Evaluation of behaviour of an obese human body model in frontal sled tests

Abstract: The goal of this study was to assess the behaviour of an obese Human Body Model (HBM) in frontal sled tests. The results of rear-seat sled tests with an obese (BMI = 35) Post Mortem Human Surrogate (PMHS) were used to evaluate the performance of the obese HBM in matching conditions. Also, the responses of a non-obese HBM (BMI = 25) and the obese HBM were compared in a front-seat frontal impact test. In the rear-seat tests, both the obese HBM and the obese PMHS experienced a large forward excursion, delayed lap belt engagement with the pelvis, and a reclined-to-upright torso angle throughout the tests, which were the effects of large body mass and thick flesh. In the front-seat simulations, the obese HBM experienced a larger excursion than the non-obese HBM. The obese HBM can be a useful tool to design and optimise restraint system for front-seat occupants with obesity.