A numerical study on the safety belt-to-pelvis interaction.

The slide of the lap belt over the iliac crest of the pelvis during vehicle frontal crashes can substantially increase the risk of some occupant injuries. A multitude of factors, related to occupants or the design of belt, are associated with this phenomenon. This study investigates safety belt-to-pelvis interaction and identifies the most influential parameters. It also explores how initial lap belt position influences the interaction between lap belt and pelvis. A finite element model of the interaction between lap belt with pelvis through a soft tissue part was created. Belt angle, belt force, belt loading rate and belt-to-body friction as belt design parameters, and pelvis angle, constitute parameters of soft tissue, and soft tissue-to-pelvis friction as occupant parameters were inspected. For the soft tissue part, subcutaneous adipose tissue with different thicknesses was created and the effect initial lap belt position may have on lap belt-to-pelvis interaction was investigated. The influential parameters have been identified as: the belt angle and belt force as belt design parameters and the pelvis angle and compressibility of soft tissue as occupant parameters. The risk for the slide of lap belt over the iliac crest of the pelvis was predicted higher as the initial lap belt positions goes superior to the pelvis. Of different submarining parameters, the lap belt angle represents the most influential one. The lap belt-to-pelvis interaction is influenced by the thickness of subcutaneous adipose tissue between lap belt and pelvis indicating a higher risk for obese occupants.

A Priori Assessment of Adipose Tissue Mechanical Testing by Global Sensitivity Analysis.

In modeling the mechanical behavior of soft tissues, the proper choice of an experiment for identifying material parameters is not an easy task. In this study, a finite element computational framework is used to virtually simulate and assess commonly used experimental setups: rotational rheometer tests, confined- and unconfined-compression tests, and indentation tests. Variance-based global sensitivity analysis is employed to identify which parameters in different experimental setups govern model prediction and are thus more likely to be determined through parameter identification processes. Therefore, a priori assessment of experimental setups provides a base for systematic and reliable parameter identification. It is found that in indentation tests and unconfined-compression tests, incompressibility of soft tissues (adipose tissue in this study) plays an important role at high strain rates. That means bulk stiffness constitutes the main part of the mechanism of tissue response; thus, these experimental setups may not be appropriate for identifying shear stiffness. Also, identified material parameters through loading-unloading shear tests at a certain rate might not be reliable for other rates, since adipose tissue shows highly strain rate dependent behavior. Frequency sweep tests at a wide-enough frequency range seem to be the best setup to capture the strain rate behavior. Moreover, analyzing the sensitivity of model parameters in the different experimental setups provides further insight about the model itself.

A Nonlinear Viscoelastic Model for Adipose Tissue Representing Tissue Response at a Wide Range of Strain Rates and High Strain Levels.

Finite element human body models (FEHBMs) are nowadays commonly used to simulate pre- and in-crash occupant response in order to develop advanced safety systems. In this study, a biofidelic model for adipose tissue is developed for this application. It is a nonlinear viscoelastic model based on the Reese et al.'s formulation. The model is formulated in a large strain framework and applied for finite element (FE) simulation of two types of experiments: rheological experiments and ramped-displacement experiments. The adipose tissue behavior in both experiments is represented well by this model. It indicates the capability of the model to be used in large deformation and wide range of strain rates for application in human body models.

An open access database for the evaluation of heart sound algorithms.

In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.