This study presents a three-dimensional finite element (FE) analysis of the human foot bone structure under mid-stance loading during race walking. A subject-specific biomodel comprising 26 bones and over 40 ligaments was reconstructed from computed tomography (CT) data using Materialise Mimics Research 21.0 and 3-Matic Research 13.0, and subsequently analyzed in ANSYS Workbench 2024 R1. The model included explicit cortical, trabecular, and ligamentous volumes, each assigned linear-elastic, isotropic material properties based on biomechanical literature data. Boundary conditions simulated the mid-stance phase of race walking, applying a distributed plantar pressure of 0.25 MPa over the metatarsal and phalangeal regions. Numerical simulations yielded maximum total displacements of 0.00018 mm, maximum von Mises stresses of 0.171 MPa, and maximum strains of 2.5 × 10⁻⁵, all remaining well within the elastic range of bone tissue. The results confirm the model’s numerical stability, geometric fidelity, and capacity to represent physiologically realistic loading responses. The developed framework demonstrates the potential of high-resolution, image-based finite element modelling for investigating stress–strain patterns of the foot during athletic gait, and establishes a reproducible reference for future analyses involving pathological gait, orthotic optimisation, and musculoskeletal load assessment in sports biomechanics.

Background/Objectives: Human gait analysis is essential for identifying biomechanical alterations associated with pathological conditions. However, conventional laboratory systems that combine optical motion capture and force plates remain costly, space-demanding, and difficult to implement in routine or accessible settings. This study proposes a dual-model artificial intelligence framework designed to bridge kinematics and dynamics and subsequently support gait health-status classification from motion data. Methods: The first model was developed to estimate ground reaction forces (GRF) and center of pressure (CoP) signals from kinematic inputs. Public datasets containing synchronized kinematics and dynamics were used to train and evaluate long short-term memory (LSTM) and one-dimensional convolutional neural network (CNN1D) architectures. A robustness stage further adapted the dynamic prediction model to markerless-like kinematic inputs through domain-adaptation training. The second model was implemented as a multichannel convolutional classifier using normalized GRF/CoP signals and metadata. Three variants were compared: signals only, signals with minimal metadata, and signals with rich metadata. Finally, a bridge block connected both models, and a proof-of-concept deployment was performed using markerless kinematics obtained with Move AI and Blender. Results: The final dynamic prediction model achieved an overall RMSE of 0.0676, with reconstructed-signal RMSE of 0.0500 and reconstructed contact accuracy of 0.9736. The best classification variant achieved a balanced accuracy of 0.9608, while the minimal-metadata variant was selected for pipeline integration due to its compatibility with accessible data acquisition. In the full pipeline evaluation, the predicted dynamics correctly classified the healthy-control samples. In the Move AI/Blender proof-of-concept, all five healthy participants were classified as healthy controls, with a mean non-pathological classification probability consistent with this outcome. Conclusions: The proposed dual-model framework demonstrates the operational feasibility of linking kinematic acquisition, dynamic prediction, and gait classification within a single artificial intelligence pipeline. The Move AI/Blender stage represents a preliminary proof of concept rather than clinical validation, and further evaluation with pathological participants and synchronized force-plate measurements is required.

The main topic of this research work is essentially about what can happen to the spine when subjected to an axial load and the damage caused when the mechanical load capacity of the intervertebral discs is exceeded, giving the focus to the analysis by introducing it in the context of sports, in this case, weight lifting. The approach focuses on biomechanics, specifically how a weight affects the biological structure during a free squat. This common weightlifting exercise involves lowering the body into a seated position and then standing back up, with an Olympic bar supported on the T1 vertebra, with the purpose of creating an approach to understanding how the spine and intervertebral discs are directly affected. For this, a methodology is presented through which the model is obtained, its validation and the application of analysis through numerical simulation. Subsequently, the behaviour of the spinal column under pressure at different levels of force is observed and qualified. Various configurations of the spine were analysed, including the distribution of forces in the intervertebral discs and adjacent joints, using a 3D biomodel subjected to analysis in the finite element software ANSYS. The results show the effect that excessive forces can cause, giving way to injuries with a significant increase in pressure on the intervertebral discs, but mainly allowing the identification of critical areas where disc herniations can originate. This numerical simulation study seeks to prevent harmful conditions in the field of fitness by providing relevant information on possible spinal column injuries in loading scenarios, which could help in a better understanding and diagnosis of unexpected injuries.

The analysis of blood flow behavior has been studied in various aspects. Recently, these studies took a turn when they were developed using Computational Fluid Dynamics (CFD), thereby advancing understanding of fluid behavior. Artery geometries and the properties of flow offer a field of interest for developing studies in which the behavior of liquid and structures can be observed. Biomodel development has evolved to allow for greater ease and precision. With a geometry similar to the real one, mechanical properties can be used to conduct studies with greater precision, yielding greater results on the behavior of a fluid and a structure. This research focuses on developing a biomodel of the right carotid artery. CFD and structural analyses are performed using the generated arterial geometry and a control volume that simulates blood flow. The structural analysis was performed using the Finite Element Method (FEM) to obtain results that provide data on the effects on the arterial walls. The results presented focus on the velocity, pressure, and wall shear stress generated by the flow. In addition, a section on displacement and Von Mises stress results is presented, demonstrating how the structure of the arterial walls is affected by these effects.