Journal of Biomechanical Engineering-Transactions of the Asme最新文献

This study develops a comprehensive framework that integrates computational fluid dynamics (CFD) and machine learning (ML) to predict milk flow behavior in lactating breasts. Utilizing CFD and other high-fidelity simulation techniques to tackle fluid flow challenges often entails significant computational resources and time investment. Artificial neural networks (ANNs) offer a promising avenue for grasping complex relationships among high-dimensional variables. This study leverages this potential to introduce an innovative data-driven approach to CFD. The initial step involved using CFD simulations to generate the necessary training and validation datasets. A machine learning pipeline was then crafted to train the ANN. Furthermore, various ANN architectures were explored, and their predictive performance was compared. The design of experiments method was also harnessed to identify the minimum number of simulations needed for precise predictions. This study underscores the synergy between CFD and ML methodologies, designated as ML-CFD. This novel integration enables a neural network to generate CFD-like results, resulting in significant savings in time and computational resources typically required for traditional CFD simulations. The models developed through this ML-CFD approach demonstrate remarkable efficiency and robustness, enabling faster exploration of milk flow behavior in individual lactating breasts compared to conventional CFD solvers.

A negative Poisson's ratio structural vascular stent with arrow-shaped cell is proposed in the paper. In order to improve the bending flexibility of the stent, three shapes of connecting bars, namely, U-shaped, S-shaped, and linear, are designed. The influence of structural parameters of the stent connecting bars on the bending performance of the stent is studied using the finite element method and experimental method. The research results show that compared to S-shaped stents, U-shaped stents have better bending flexibility. In addition, the influence of the parameters of the connection bars of a U-shaped stent on its flexibility is also explored. The longer the length of the U-shaped connection bars, the lower the corresponding bending stiffness and better bending flexibility. The larger the width of the U-shaped connecting bar, the greater the bending stiffness of the stent, and the worse its bending flexibility. And some experiments are conducted using the four points bending method. The deformation of the S-shaped stent and U-shaped stent under bending is very similar, and the bending stiffness of the U-shaped stent is lower than that of the S-shaped bracket, which also verified the correctness of the finite element calculation.

The sit-to-stand (STS) movement is a common activity essential for independence and mobility. Traditional methods for assessing STS often involve costly laboratory equipment, limiting their accessibility. This study introduced an economic alternative to the standard motion capture setup. The system presented in this study used an Azure Kinect and a plantar pressure sensor mat to acquire kinematic and kinetic data simultaneously during the STS. The Kinect provided noncontact motion capture, while the pressure sensor array measured ground reaction forces. To address the Kinect's inherent limitations in capturing extremity movements and the sensor array's inability to measure tangential forces, algorithms for the correction of lower limb joints and a multisource fusion model were developed. The accuracy of the proposed system was evaluated against a gold standard Vicon motion capture system. The results indicated that the system delivered estimates comparable to reference values for joint angles (r ranging from 0.85 to 0.99), antero-posterior and vertical ground reaction forces (r ranging from 0.81 to 0.98), joint reaction forces of knee and ankle (r ranging from 0.83 to 0.90), and joint moments of hip and ankle (r ranging from 0.77 to 0.82), suggesting that the proposed system can provide vital kinematic and kinetic data for efficient STS analysis. This study offered an accessible and practical solution for monitoring and assessing mobility in various settings.

Hearing loss is highly related to acoustic injuries and mechanical damage of ear tissues. The mechanical responses and failures of ear tissues are difficult to measure experimentally, especially cochlear hair cells within the organ of Corti (OC) at microscale. Finite element (FE) modeling has become an important tool for simulating acoustic wave transmission and studying cochlear mechanics. This study harnessed a multiscale FE model to investigate the mechanical behaviors of ear tissues in response to acoustic wave and developed a fatigue mechanical model to describe the outer hair cells (OHCs) failure. A three-dimensional (3D) multiscale FE model consisting of a macroscale model of the ear canal, middle ear, and three-chambered cochlea and a microscale OC model on a representative basilar membrane section, including the hair cells, membranes, and supporting cells, was established. Harmonic acoustic mode was used in the FE model for simulating various acoustic pressures and frequencies. The cochlear basilar membrane and the cochlear pressure induced by acoustic pressures were derived from the macroscale model and used as inputs for microscale OC model. The OC model identified the stress and strain concentrations in the reticular lamina (RL) at the root of stereocilia hair bundles and in the Deiter's cells at the connecting ends with OHCs, indicating the potential mechanical damage sites. OHCs were under cyclic loading and the alternating stress was quantified by the FE model. A fatigue mechanism for OHCs was established based on the modeling results and experimental data. This mechanism would be used for predicting fatigue failure and the resulting hearing loss.

A criterion characterizing the combined neurotoxicity of amyloid beta and tau oligomers is suggested. A mathematical model for calculating the value of this criterion during senile plaque and neurofibrillary tangle (NFT) formation is proposed. Computations show that for physiologically relevant parameter values, the value of the criterion increases approximately linearly with time. Once neurofibrillary tangles begin forming in addition to senile plaques, there is an increase in the slope characterizing the rate at which the criterion increases. The critical value of the criterion at which a neuron dies is estimated. Unless the production rates of amyloid beta and tau monomers are very large, computations predict that for the accumulated toxicity to reach the critical value, the neural machinery responsible for the degradation of amyloid beta and tau monomers and aggregates must become dysfunctional. The value of the criterion after 20 years of the aggregation process is strongly influenced by the deposition rates of amyloid beta and tau oligomers into senile plaques and NFTs. This suggests that deposition of amyloid beta and tau oligomers into senile plaques and NFTs may reduce accumulated toxicity by sequestering more toxic oligomeric species into less toxic insoluble aggregates.

Frequency-domain analysis of brain tissue motion has received increased focus in recent years as an approach to describing the response of the brain to impact or vibration sources in the built environment. While researchers in many experimental and numerical studies have sought to identify natural resonant frequencies of the brain, sparse description of the associated vibration modes limits comparison of results between studies. We performed a modal analysis to extract the natural frequencies and associated mode shapes of a finite element (FE) model of the head. The vibration modes were characterized using two-dimensional (2D) plate deformation notation in the basic medical planes. Many of the vibration modes characterized are similar to those found in previous numerical and experimental studies. We propose this characterization method as an approach to increase compatibility of results between studies of brain vibration behavior.

Current studies on human locomotion focus mainly on solid ground walking conditions. In this paper, we present a biomechanical comparison of human walking locomotion on solid ground and sand. A novel dataset containing three-dimensional motion and biomechanical data from 20 able-bodied adults for walking locomotion on solid ground and sand is collected. We present the data collection methods and report the sensor data along with the kinematic and kinetic profiles of joint biomechanics. The results reveal significant gait adaptations to the yielding terrain (i.e., sand), such as increased stance duration, reduced push-off force, and altered joint angles and moments. Specifically, the knee angle during the gait cycle on sand shows a delayed peak flexion and an increased overall magnitude, highlighting an adaptation to maintain stability on yielding terrain. These adjustments, including changes in joint timing and energy conservation mechanisms, provide insights into the motion control strategies humans adopt to navigate on yielding terrains. The dataset, containing synchronized ground reaction forces (GRFs) and kinematic data, offers a valuable resource for further exploration in foot-terrain interactions and human walking assistive devices development on yielding terrains.

This study aimed to compare the sinking and shifting characteristics of an enhanced expulsion-proof intervertebral fusion device (EEIFD) with a traditional transforaminal lumbar interbody fusion device (TTLIFD). Five specimens of each device were selected for analysis. Four mechanical tests-compression, subsidence, expulsion, and torque-were conducted for each cage. Additionally, a blade-cutting torque test was performed on the EEIFD, with load-displacement curves and mechanical values recorded. In static axial compression, static subsidence, and dynamic subsidence tests, the EEIFD demonstrated performance comparable to the TTLIFD. In expulsion testing, the maximum expulsion force for the EEIFD when the blade was rotated out (534.02 ± 21.24 N) was significantly higher than when the blade was not rotated out (476.97 ± 24.45 N) (P = 6.81 × 10-4). Moreover, the maximum expulsion force for the EEIFD with blade rotation (534.02 ± 21.24 N) was significantly higher than that of the TTLIFD (444.01 ± 12.42 N) (P = 9.82 × 10-5). These findings indicated that the EEIFD effectively enhanced expulsion prevention and antisubsidence performance.

Anterior disc displacement (ADD) is one of the most prevalent temporomandibular disorders (TMD). It was widely recognized that occlusal factors could affect temporomandibular joint (TMJ). However, the impacts of ADD on the biomechanical environment of TMJ and occlusion are still unclear. This study aimed to describe the effects of ADD on the masticatory system, including TMJ and occlusion. The finite element model (FEM) was constructed based on the medical images of a healthy adult male. The complete skull, masticatory muscles, TMJs, and related ligaments were included. Three FEMs with different degrees of ADD were constructed with disc-condyle angles of 10 deg, 20 deg, and 30 deg. The muscle forces corresponding to intercuspal clenching (ICC) were applied as the loading condition. Four models were conducted: normal, mild, moderate, and severe ADD. It was found that the overall stress distribution was relatively consistent across the four models. The contact stress on the TMJ and occlusion in severe ADD was visibly different from the other three models. In addition, the contact stress on the condyle gradually increased with the increasing occlusal strength. Abnormally high-stress concentration began to appear on the condyle at 30% muscle strength. Moderate ADD was more of a transitional stage. Compared to mild and moderate ADD, severe ADD had visibly effects on the stress response of the TMJ and the entire mandible (including occlusion), such as abnormally high stresses of the condyle, stress concentration on the second molar, and prone to disc extrusion and anterior slippage during high-strength occlusion.

Despite the increasing use of inertial measurement units (IMUs) and machine learning techniques for gait analysis, there remains a gap in which feature selection methods are best tailored for gait time series prediction. This study explores the impact of using various feature selection methods on the performance of a random forest (RF) model in predicting lower limb joints kinematics from two IMUs. The primary objectives of this study are as follows: (1) Comparing eight feature selection methods based on their ability to identify more robust feature sets, time efficiency, and impact on RF models' performance, and (2) assessing the performance of RF models using generalized feature sets on a new dataset. Twenty-three typically developed (TD) children (ages 6-15) participated in data collection involving optical motion capture (OMC) and IMUs. Joint kinematics were computed using opensim. By employing eight feature selection methods (four filter and four embedded methods), the study identified 30 important features for each target. These selected features were used to develop personalized and generalized RF models to predict lower limbs joints kinematics during gait. This study reveals that various feature selection methods have a minimal impact on the performance of personalized and generalized RF models. However, the RF and mutual information (MI) methods provided slightly lower errors and outliers. MI demonstrated remarkable robustness by consistently identifying the most common features across different participants. ElasticNet emerged as the fastest method. Overall, the study illuminated the robustness of RF models in predicting joint kinematics during gait in children, showcasing consistent performance across various feature selection methods.