搜索结果：Acta of bioengineering and biomechanics

Humans and animals lose tissues and organs due to congenital defects, trauma, and diseases. The human body has a low regenerative potential as opposed to the urodele amphibians commonly referred to as salamanders. Globally, millions of people would benefit immensely if tissues and organs can be replaced on demand. Traditionally, transplantation of intact tissues and organs has been the bedrock to replace damaged and diseased parts of the body. The sole reliance on transplantation has created a waiting list of people requiring donated tissues and organs, and generally, supply cannot meet the demand. The total cost to society in terms of caring for patients with failing organs and debilitating diseases is enormous. Scientists and clinicians, motivated by the need to develop safe and reliable sources of tissues and organs, have been improving therapies and technologies that can regenerate tissues and in some cases create new tissues altogether. Tissue engineering and/or regenerative medicine are fields of life science employing both engineering and biological principles to create new tissues and organs and to promote the regeneration of damaged or diseased tissues and organs. Major advances and innovations are being made in the fields of tissue engineering and regenerative medicine and have a huge impact on three-dimensional bioprinting (3D bioprinting) of tissues and organs. 3D bioprinting holds great promise for artificial tissue and organ bioprinting, thereby revolutionizing the field of regenerative medicine. This review discusses how recent advances in the field of regenerative medicine and tissue engineering can improve 3D bioprinting and vice versa. Several challenges must be overcome in the application of 3D bioprinting before this disruptive technology is widely used to create organotypic constructs for regenerative medicine.

Sexual dimorphism is a critical factor in many biological and medical research fields. In biomechanics and bioengineering, understanding sex differences is crucial for studying musculoskeletal conditions such as temporomandibular disorder (TMD). This paper focuses on the association between the craniofacial skeletal morphology and temporomandibular joint (TMJ) related masticatory muscle attachments to discern sex differences. Data were collected from 10 male and 11 female cadaver heads to investigate sex-specific relationships between the skull and muscles. We propose a conditional cross-covariance reduction (CCR) model, designed to examine the dynamic association between two sets of random variables conditioned on a third binary variable (e.g., sex), highlighting the most distinctive sex-related relationships between skull and muscle attachments in the human cadaver data. Under the CCR model, we employ a sparse singular value decomposition algorithm and introduce a sequential permutation for selecting sparsity (SPSS) method to select important variables and to determine the optimal number of selected variables.

Background Objectives:The biomechanics of Tori s body motion during Sutemi,sacrifice techniques,Makikomi wrapping techniques,and Tai Atari,Body Strike techniques in judo is crucial for understanding these techniques' effectiveness and safety. This study examined the biomechanics by analysing the entire body of the tori during execution. Methods:We analysed Tori s body motion using original biomechanical models, considering the balance of sacrificing to throw Uke and body mass to strike the opponent. General equations for Sutemi, Makikomi, and Tai atari were established, and their utilisation in high level male competition was statistically analysed. Results:The results show that effective Sutemi and Makikomi execution depends on the correct use of Tori's body mass.The biomechanical model revealed the relationships between angular falling speed, body angle, and athlete height.The model allowed us to evaluate the falling velocity, showing that the angular velocity and acceleration were proportional to the gravitational acceleration and body-Tatami angle and inversely proportional to the athlete height.The stopping and sliding forces are directly proportional to the sum of the Tori an

Current studies on human locomotion focus mainly on solid ground walking conditions. In this paper, we present a biomechanic comparison of human walking locomotion on solid ground and sand. A novel dataset containing 3-dimensional motion and biomechanical data from 20 able-bodied adults for 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. A comprehensive analysis of human gait and joint stiffness profiles is presented. The kinematic and kinetic analysis reveals that human walking locomotion on sand shows different ground reaction forces and joint torque profiles, compared with those patterns from walking on solid ground. These gait differences reflect that humans adopt motion control strategies for yielding terrain conditions such as sand. The dataset also provides a source of locomotion data for researchers to study human activity recognition and assistive devices for walking on different terrains.

Background: Biomechanical analysis of gait employs various methods used in kinematic and kinetic analysis, EMG, and others. One of the most frequently used methods is kinetic analysis based on the assessment of the ground reaction forces (GRF) recorded on two force plates. Objective: The aim of the study was to present a method of gait analysis based on the assessment of the GRF recorded during the stance phase of two steps. Methods: The GRF recorded with a force plate on one leg during stance phase has three components acting in directions: Fx - mediolateral, Fy - anteroposterior, and Fz - vertical. A custom-written MATLAB script was used for gait analysis in this study. This software displays instantaneous force data for both legs as Fx(t), Fy(t) and Fz(t) curves, automatically determines the extremes of functions and sets the visual markers defining the individual points of interest. Positions of these markers can be easily adjusted by the rater, which may be necessary if the GRF has an atypical pattern. The analysis is fully automated and analyzing one trial takes only 1-2 minutes. Results: The method allows quantification of temporal variables of the extremes of the Fx(t), Fy(t), Fz(t) functions, durations of the braking and propulsive phase, duration of the double support phase, the magnitudes of reaction forces in extremes of measured functions, impulses of force, and indices of symmetry. The analysis results in a standardized set of 78 variables (temporal, force, indices of symmetry) which can serve as a basis for further research and diagnostics. Conclusions: The resulting set of variable offers a wide choice for selecting a specific group of variables with consideration to a particular research topic. The advantage of this method is the standardization of the GRF analysis, low time requirements allowing rapid analysis of a large number of trials in a short time, and comparability of the variables obtained during different research measurements.

Computational biomechanics models of the brain have become an important tool for investigating the brain responses to mechanical loads. The geometry, loading conditions, and constitutive properties of such brain models are well-studied and generally accepted. However, there is a lack of experimental evidence to support models of the layers of tissues (brain-skull interface) connecting the brain with the skull which determine boundary conditions for the brain. We present a new protocol for determining the biomechanical properties of the brain-skull interface and present the preliminary results (for a small number of tissue samples extracted from sheep cadaver heads). The method consists of biomechanical experiments using brain tissue and brain-skull complex (consisting of the brain tissue, brain-skull interface, and skull bone) and comprehensive computer simulation of the experiments using the finite element (FE) method. Application of the FE simulations allowed us to abandon the traditionally used approaches that rely on analytical formulations that assume cuboidal (or cylindrical) sample geometry when determining the parameters that describe the biomechanical behaviour of the brai

In this paper we present a derivation and multiscale analysis of a mathematical model for plant cell wall biomechanics that takes into account both the microscopic structure of a cell wall coming from the cellulose microfibrils and the chemical reactions between the cell wall's constituents. Particular attention is paid to the role of pectin and the impact of calcium-pectin cross-linking chemistry on the mechanical properties of the cell wall. We prove the existence and uniqueness of the strongly coupled microscopic problem consisting of the equations of linear elasticity and a system of reaction-diffusion and ordinary differential equations. Using homogenization techniques (two-scale convergence and periodic unfolding methods) we derive a macroscopic model for plant cell wall biomechanics.

Despite advancements in surgical techniques, hernia recurrence rates remain high, underscoring the need for improved understanding of abdominal wall behaviour. While surgeons are aware of many factors contributing to hernia occurrence (e.g obesity, smoking, surgical technique or site infection), it would be of interest to consider it as a biomechanical pathology. Indeed, an abdominal hernia arises from an imbalance between abdominal wall deformability and applied forces. This review article discusses how biomechanics offer a quantitative framework for assessing healthy and damaged tissue behaviour, guiding personalised surgical strategies throughout the pre-, intra-, and post-operative periods. The abdominal wall is a dynamic, load-bearing structure, continuously subjected to intra-abdominal pressure and mechanical stress. Its biomechanical properties, including elasticity and resistance to loading forces, dictate its function and response to surgical intervention. The linea alba is the stiffest component experiencing the highest stress, while the abdominal wall's anisotropic nature influences deformation patterns. Various experimental and computational methods enable biomechanical

The integration of machine learning and deep learning has transformed data analytics in biomechanics, enabled by extensive wearable sensor data. However, the field faces challenges such as limited large-scale datasets and high data acquisition costs, which hinder the development of robust algorithms. Data augmentation techniques show promise in addressing these issues, but their application to biomechanical time-series data requires comprehensive evaluation. This scoping review investigates data augmentation methods for time-series data in the biomechanics domain. It analyzes current approaches for augmenting and generating time-series datasets, evaluates their effectiveness, and offers recommendations for applying these techniques in biomechanics. Four databases, PubMed, IEEE Xplore, Scopus, and Web of Science, were searched for studies published between 2013 and 2024. Following PRISMA-ScR guidelines, a two-stage screening identified 21 relevant publications. Results show that there is no universally preferred method for augmenting biomechanical time-series data; instead, methods vary based on study objectives. A major issue identified is the absence of soft tissue artifacts in sy

This paper presents the principal challenges and opportunities associated with computational biomechanics research. The underlying cognitive control involved in the process of human motion is inherently complex, dynamic, multidimensional, and highly non-linear. The dynamics produced by the internal and external forces and the body's ability to react to them is biomechanics. Complex and non-rigid bodies, needs a lot of computing power and systems to execute however, in the absence of adequate resources, one may rely on new technology, machine learning tools and model order reduction approaches. It is also believed that machine learning approaches can enable us to embrace this complexity, if we could use three arms of ML i.e. predictive modeling, classification, and dimensionality reduction. Biomechanics, since it deals with motion and mobility come with a huge set of data over time. Using computational (Computer Solvers), Numerical approaches (MOR) and technological advances (Wearable sensors), can let us develop computationally inexpensive frameworks for biomechanics focused studies dealing with a huge amount of data. A lot of misunderstanding arises because of extensive data, stan

This chapter provides an overview of recent and promising Machine Learning applications, i.e. pose estimation, feature estimation, event detection, data exploration & clustering, and automated classification, in gait (walking and running) and sports biomechanics. It explores the potential of Machine Learning methods to address challenges in biomechanical workflows, highlights central limitations, i.e. data and annotation availability and explainability, that need to be addressed, and emphasises the importance of interdisciplinary approaches for fully harnessing the potential of Machine Learning in gait and sports biomechanics.

Modeling spinal motion is fundamental to understanding human biomechanics, yet remains underexplored in computer vision due to the spine's complex multi-joint kinematics and the lack of large-scale 3D annotations. We present a biomechanics-aware keypoint simulation framework that augments existing human pose datasets with anatomically consistent 3D spinal keypoints derived from musculoskeletal modeling. Using this framework, we create the first open dataset, named SIMSPINE, which provides sparse vertebra-level 3D spinal annotations for natural full-body motions in indoor multi-camera capture without external restraints. With 2.14 million frames, this enables data-driven learning of vertebral kinematics from subtle posture variations and bridges the gap between musculoskeletal simulation and computer vision. In addition, we release pretrained baselines covering fine-tuned 2D detectors, monocular 3D pose lifting models, and multi-view reconstruction pipelines, establishing a unified benchmark for biomechanically valid spine motion estimation. Specifically, our 2D spine baselines improve the state-of-the-art from 0.63 to 0.80 AUC in controlled environments, and from 0.91 to 0.93 AP fo

Markerless biomechanics increasingly relies on 3D skeletal keypoints extracted from video, yet downstream biomechanical mappings typically treat these estimates as deterministic, providing no principled mechanism for frame-wise quality control. In this work, we investigate predictive uncertainty as a quantitative measure of confidence for mapping 3D pose keypoints to 3D anatomical landmarks, a critical step preceding inverse kinematics and musculoskeletal analysis. Within a temporal learning framework, we model both uncertainty arising from observation noise and uncertainty related to model limitations. Using synchronized motion capture ground truth on AMASS, we evaluate uncertainty at frame and joint level through error--uncertainty rank correlation, risk--coverage analysis, and catastrophic outlier detection. Across experiments, uncertainty estimates, particularly those associated with model uncertainty, exhibit a strong monotonic association with landmark error (Spearman $ρ\approx 0.63$), enabling selective retention of reliable frames (error reduced to $\approx 16.8$ mm at 10% coverage) and accurate detection of severe failures (ROC-AUC $\approx 0.92$ for errors $>50$ mm). R

In this paper homogenization of a mathematical model for biomechanics of a plant tissue with randomly distributed cells is considered. Mechanical properties of a plant tissue are modelled by a strongly coupled system of reaction-diffusion-convection equations for chemical processes in plant cells and cell walls, the equations of poroelasticity for elastic deformations of plant cell walls and middle lamella, and the Stokes equations for fluid flow inside the cells. The nonlinear coupling between the mechanics and chemistry is given by the dependence of elastic properties of plant tissue on densities of chemical substances as well as by the dependence of chemical reactions on mechanical stresses present in a tissue. Using techniques of stochastic homogenization we derive rigorously macroscopic model for plant tissue biomechanics with random distribution of cells. Strong stochastic two-scale convergence is shown to pass to the limit in the non-linear reaction terms. Appropriate meaning of the boundary terms is introduced to define the macroscopic equations with flux boundary conditions and transmission conditions on the microscopic scale.

Spine biomechanics is at a transformation with the advent and integration of machine learning and computer vision technologies. These novel techniques facilitate the estimation of 3D body shapes, anthropometrics, and kinematics from as simple as a single-camera image, making them more accessible and practical for a diverse range of applications. This study introduces a framework that merges these methodologies with traditional musculoskeletal modeling, enabling comprehensive analysis of spinal biomechanics during complex activities from a single camera. Additionally, we aim to evaluate their performance and limitations in spine biomechanics applications. The real-world applications explored in this study include assessment in workplace lifting, evaluation of whiplash injuries in car accidents, and biomechanical analysis in professional sports. Our results demonstrate potential and limitations of various algorithms in estimating body shape, kinematics, and conducting in-field biomechanical analyses. In industrial settings, the potential to utilize these new technologies for biomechanical risk assessments offers a pathway for preventive measures against back injuries. In sports activ

Markerless 3D movement analysis from monocular video enables accessible biomechanical assessment in clinical and sports settings. However, most research-grade pipelines rely on GPU acceleration, limiting deployment on consumer-grade hardware and in low-resource environments. In this work, we optimize a monocular 3D biomechanics pipeline derived from the MonocularBiomechanics framework for efficient CPU-only execution. Through profiling-driven system optimization, including model initialization restructuring, elimination of disk I/O serialization, and improved CPU parallelization. Experiments on a consumer workstation (AMD Ryzen 7 9700X CPU) show a 2.47x increase in processing throughput and a 59.6\% reduction in total runtime, with initialization latency reduced by 4.6x. Despite these changes, biomechanical outputs remain highly consistent with the baseline implementation (mean joint-angle deviation 0.35$^\circ$, $r=0.998$). These results demonstrate that research-grade vision-based biomechanics pipelines can be deployed on commodity CPU hardware for scalable movement assessment.

Pose estimation has promised to impact healthcare by enabling more practical methods to quantify nuances of human movement and biomechanics. However, despite the inherent connection between pose estimation and biomechanics, these disciplines have largely remained disparate. For example, most current pose estimation benchmarks use metrics such as Mean Per Joint Position Error, Percentage of Correct Keypoints, or mean Average Precision to assess performance, without quantifying kinematic and physiological correctness - key aspects for biomechanics. To alleviate this challenge, we develop OpenCapBench to offer an easy-to-use unified benchmark to assess common tasks in human pose estimation, evaluated under physiological constraints. OpenCapBench computes consistent kinematic metrics through joints angles provided by an open-source musculoskeletal modeling software (OpenSim). Through OpenCapBench, we demonstrate that current pose estimation models use keypoints that are too sparse for accurate biomechanics analysis. To mitigate this challenge, we introduce SynthPose, a new approach that enables finetuning of pre-trained 2D human pose models to predict an arbitrarily denser set of keypo

Organs are complex systems composed of different cells, proteins and signalling molecules that are arranged in a highly ordered structure to orchestrate a myriad of functions in our body. Biofabrication strategies can be applied to engineer 3D tissue models in vitro by mimicking the structure and function of native tissue through the precise deposition and assembly of materials and cells. This approach allows the spatiotemporal control over cell-cell and cell-extracellular matrix communication and thus the recreation of tissue-like structures. In this Review, we examine biofabrication strategies for the construction of functional tissue replacements and organ models, focusing on the development of biomaterials, such as supramolecular and photosensitive materials, that can be processed using biofabrication techniques. We highlight bioprinted and bioassembled tissue models and survey biofabrication techniques for their potential to recreate complex tissue properties, such as shape, vasculature and specific functionalities. Finally, we discuss challenges, such as scalability and the foreign body response, and opportunities in the field and provide an outlook to the future of biofabrication in regenerative medicine.

This paper illustrates how multilevel functional models can detect and characterize biomechanical changes along different sport training sessions. Our analysis focuses on the relevant cases to identify differences in knee biomechanics in recreational runners during low and high-intensity exercise sessions with the same energy expenditure by recording $20$ steps. To do so, we review the existing literature of multilevel models, and then, we propose a new hypothesis test to look at the changes between different levels of the multilevel model as low and high-intensity training sessions. We also evaluate the reliability of measures recorded in three-dimension knee angles from the functional intra-class correlation coefficient (ICC) obtained from the decomposition performed with the multilevel funcional model taking into account $20$ measures recorded in each test. The results show that there are no statistically significant differences between the two modes of exercise. However, we have to be careful with the conclusions since, as we have shown, human gait-patterns are very individual and heterogeneous between groups of athletes, and other alternatives to the p-value may be more approp