Browse Topic: Crash research
Pelvic orientation in vehicles is crucial for preventing injuries and creating safer vehicles and restraint systems. A better understanding of pelvic orientation could provide more accurate anthropomorphic test device (ATD) models of underrepresented populations such as obese individuals, children, and small females. Sonomicrometry is the use of piezoelectric transducers that transmit ultrasound signals to each other to measure the distance between them. These signals may be aggregated using triangulation. In this experiment, ultrasound crystals were secured to the surface of a porcine surrogate to evaluate pelvic movement. This data was then processed using Sonometrics software to generate a 3D model of four static positions and three dynamic tests. The test was validated using a camera and a 3D measurement arm (CMM) to validate XYZ positions. This article discusses how this method could be helpful for developing more accurate ATD models, preventing fatalities in vehicle crashes
Rear-end vehicle collisions may lead to whiplash-associated disorders (WADs), comprising a variety of neck and head pain responses. Specifically, increased axial head rotation has been associated with the risk of injuries during rear impacts, while specific tissues, including the capsular ligaments, have been implicated in pain response. Given the limited experimental data for out-of-position rear impact scenarios, computational human body models (HBMs) can inform the potential for tissue-level injury. Previous studies have considered external boundary conditions to reposition the head axially but were limited in reproducing a biofidelic movement. The objectives of this study were to implement a novel head repositioning method to achieve targeted axial rotations and evaluate the tissue-level response for a rear impact condition. The repositioning method used reference geometries to rotate the head to three target positions, showing good correspondence to reported interverbal rotations
Thorax injuries are a significant cause of mortality in automotive crashes, with varying susceptibility across sex and age demographics. Finite element (FE) human body models (HBMs) offer the potential for injury outcome analysis by incorporating anthropometric variations. Recent advancements in material constitutive models, cortical bone fracture and continuum damage mechanics model (CFraC) and an orthotropic trabecular bone model (OrthoT), offer the opportunity to further improve rib models. In this study, the CFraC and OrthoT material modes, coupled with age-specific material properties, were progressively implemented to the Global Human Body Model Consortium small female 6th rib. Four distinct 6th rib models were developed and compared against sex and age-specific experimental data. The updated material models notably refined the predictions of force–displacement responses, aligning them more closely with the experimental averages. The CFraC model significantly improved the
Letter from the Focus Issue Editors
Ongoing research in simulated vehicle crash environments utilizes postmortem human subjects (PMHS) as the closest approximation to live human response. Lumbar spine injuries are common in vehicle crashes, necessitating accurate assessment methods of lumbar loads. This study evaluates the effectiveness of lumbar intervertebral disc (IVD) pressure sensors in detecting various loading conditions on component PMHS lumbar spines, aiming to develop a reliable insertion method and assess sensor performance under different loading scenarios. The pressure sensor insertion method development involved selecting a suitable sensor, using a customized needle-insertion technique, and precisely placing sensors into the center of lumbar IVDs. Computed tomography (CT) scans were utilized to determine insertion depth and location, ensuring minimal tissue disruption during sensor insertion. Tests were conducted on PMHS lumbar spines using a robotic test system for controlled loading in flexion
University of Waterloo Chemical Engineering Researcher Dr. Elisabeth Prince teamed up with researchers from the University of Toronto and Duke University to design the synthetic material made using cellulose nanocrystals, which are derived from wood pulp. The material is engineered to replicate the fibrous nanostructures and properties of human tissues, thereby recreating its unique biomechanical properties
Scientists at Osaka University, in cooperation with Joanneum Research (Weiz, Austria), have introduced wireless health monitoring patches that use embedded piezoelectric nanogenerators to power themselves with harvested biomechanical energy. This work may lead to new autonomous health sensors as well as battery-less wearable electronic devices
Letter from the Special Issue Editors
The purpose and scope of this SAE Recommended Practice is to provide a basis for classification of the extent of vehicle deformation caused by vehicle accidents on the highway. It is necessary to classify collision contact deformation (as opposed to induced deformation) so that the accident deformation may be segregated into rather narrow limits. Studies of collision deformation can then be performed on one or many data banks with assurance that the data under study are of essentially the same type.1 The seven-character code is also an expression useful to persons engaged in automobile safety, to describe appropriately a field-damaged vehicle with conciseness in their oral and written communications. Although this classification system was established primarily for use by professional teams investigating accidents in depth, other groups may also find it useful. The classification system consists of seven characters, three numeric, and four alphameric, arranged in a specific order. The
Field accident data and vehicle crash and sled testing indicate that occupant kinematics, loading, and associated injury risk generally increase with crash severity. Further, these data demonstrate that the use of restraints, such as three-point belts, provides mitigation of kinematics and reduction in loading and injury potential. This study evaluated the role of seat belts in controlling occupant kinematics and reducing occupant loading in moderate severity frontal collisions. Frontal tests with belted and unbelted anthropomorphic test devices (ATDs) in the driver and right front passenger seats were performed at velocity changes (delta-Vs) of approximately 19 kph (12 mph) and 32 kph (20 mph) without airbag deployment. At the lower-moderate severity (19 kph), motion of the belted ATDs was primarily arrested by seat belt engagement, while motion of the unbelted ATDs was primarily arrested by interaction with forward vehicle structures. Occupant loading and injury risk was generally
Exoskeletons, many of which are powered by springs or motors, can cause pain or injury if their joints are not aligned with the user’s. To mitigate these risks, a new measurement method was developed to test whether an exoskeleton and the person wearing it are moving smoothly and in harmony
We recently developed a three-direction (vertical, longitudinal, and lateral) coupled biodynamic model of seated posture under vibration. However, in that study we only tested one algorithm to identify the model parameters. This article investigates four different optimization solvers in Matlab®, i.e., particle swarm optimization (particleswarm), particle swarm and local optimization method (fmincon), genetic algorithm (ga) and local optimization method (fmincon), and local optimization method (fmincon) to identify coupled biodynamic model parameters. Based on the obtained parameters, it further compares experimental and simulation results to determine the best optimization solver in terms of the root mean square error (RMSE), linear regression (R 2), goodness of fit (ε), and Central Processing Unit (CPU) time. The results show that particle swarm optimization is the best one for identifying the biodynamic model’s parameters
Reliable online recognition and prediction of human actions and activities in temporal sequences has many potential applications in a wide range of Army-relevant fields, ranging from video surveillance, warfighter assistance, human computer interface, intelligent humanoid robots, and unmanned and autonomous vehicles, to diagnosis, assessment and treatment of musculoskeletal disorders, etc. A computational approach for action prediction can extend these findings to machines and also promote further research in human prediction and intention sensing
Monitoring the progression of multiple sclerosis-related gait issues can be challenging in adults over 50 years old, requiring a clinician to differentiate between problems related to MS and other age-related issues. To address this problem, researchers integrated gait data and machine learning to advance the tools used to monitor and predict disease progression
Items per page:
50
1 – 50 of 475