arXiv subjects

Reza Kakavand

Publications and source records attributed to Reza Kakavand.

4 recordsLinked to original sources

arXiv2025

Comparison of Kinematics and Kinetics Between OpenCap and a Marker-Based Motion Capture System in Cycling

This study evaluates the agreement of marker-based and markerless (OpenCap) motion capture systems in assessing joint kinematics and kinetics during cycling. Markerless systems, such as OpenCap, offer the advantage of capturing natural movements without physical markers, making them more practical for real-world applications. However, the agreement of OpenCap with a marker-based system, particularly in cycling, remains underexplored. Ten participants cycled at varying speeds and resistances while motion data were recorded using both systems. Key metrics, including joint angles, moments, and joint reaction loads, were computed using OpenSim and compared using root mean squared error (RMSE) per trial across participants, Pearson correlation coefficients (r) per trial across participants and repeated measures Bland-Altman to control trials dependency within subject. Results revealed very strong agreement (r GT 0.9) for hip (flexion/extension), knee (flexion/extension), and ankle (dorsiflexion/plantarflexion) joint angles.

cs.CV↗

arXiv2025

Continuum Damage Modeling of Biaxial Fatigue Failure in Whole Bone: A Hybrid Approach with Machine Learning Integration

Repetitive loading of bone is associated with microdamage accumulation and material property degradation that may ultimately result in fatigue fracture. Our previous work used continuum damage mechanics (CDM)-based finite element (FE) modeling to predict stiffness loss and fatigue failure in whole bone; however, this model did not account for inter-specimen variability in fatigue behaviour and multiaxial loading effects, which limited its applied efficacy. In this study, we refined the CDM-based FE model to predict experimental fatigue-life measurements from 21 whole rabbit tibiae subjected to cyclic axial compression with different magnitudes of superposed torsion. Machine learning (ML) methods were used to predict damage parameters from initial, undamaged, conditions, which were then used to predicted stiffness degradation and fatigue failure with the CDM-based FE modeling pipeline. Regression analysis was used to compare CDM-based FE fatigue-life predictions with experimental measurements. A random forest ML model predicted specimen-specific damage parameters with high accuracy (R2 = 0.85) and the CDM-based FE models demonstrated remarkable predictive capability, explaining up to 91% of the variance in fatigue-life measurements. Stiffness degradation profiles also followed a similar trend to experimental measurements; however, this agreement worsened with the level of superposed torsion suggesting additional refinements to the model may be necessary. These findings demonstrate the efficacy of integrating ML with CDM-based FE modeling to predict fatigue life and stiffness degradation. The observed agreement with experimental measurements suggests the modeling framework may provide valuable information regarding the mechanisms of fatigue fracture in whole bone

physics.med-ph↗

arXiv2024

Swin UNETR segmentation with automated geometry filtering for biomechanical modeling of knee joint cartilage

Simulation studies, such as finite element (FE) modeling, offer insights into knee joint biomechanics, which may not be achieved through experimental methods without direct involvement of patients. While generic FE models have been used to predict tissue biomechanics, they overlook variations in population-specific geometry, loading, and material properties. In contrast, subject-specific models account for these factors, delivering enhanced predictive precision but requiring significant effort and time for development. This study aimed to facilitate subject-specific knee joint FE modeling by integrating an automated cartilage segmentation algorithm using a 3D Swin UNETR. This algorithm provided initial segmentation of knee cartilage, followed by automated geometry filtering to refine surface roughness and continuity. In addition to the standard metrics of image segmentation performance, such as Dice similarity coefficient (DSC) and Hausdorff distance, the method's effectiveness was also assessed in FE simulation. Nine pairs of knee cartilage FE models, using manual and automated segmentation methods, were developed to compare the predicted stress and strain responses during gait. The automated segmentation achieved high Dice similarity coefficients of 89.4% for femoral and 85.1% for tibial cartilage, with a Hausdorff distance of 2.3 mm between the automated and manual segmentation. Mechanical results including maximum principal stress and strain, fluid pressure, fibril strain, and contact area showed no significant differences between the manual and automated FE models. These findings demonstrate the effectiveness of the proposed automated segmentation method in creating accurate knee joint FE models.

eess.IV↗

arXiv2023

Integration of Swin UNETR and statistical shape modeling for a semi-automated segmentation of the knee and biomechanical modeling of articular cartilage

Simulation studies like finite element (FE) modeling provide insight into knee joint mechanics without patient experimentation. Generic FE models represent biomechanical behavior of the tissue by overlooking variations in geometry, loading, and material properties of a population. On the other hand, subject-specific models include these specifics, resulting in enhanced predictive precision. However, creating such models is laborious and time-intensive. The present study aimed to enhance subject-specific knee joint FE modeling by incorporating a semi-automated segmentation algorithm. This segmentation was a 3D Swin UNETR for an initial segmentation of the femur and tibia, followed by a statistical shape model (SSM) adjustment to improve surface roughness and continuity. Five hundred and seven magnetic resonance images (MRIs) from the Osteoarthritis Initiative (OAI) database were used to build and validate the segmentation model. A semi-automated FE model was developed using this semi-automated segmentation. On the other hand, a manual FE model was developed through manual segmentation (i.e., the gold standard approach). Both FE models were subjected to gait loading. The predicted mechanical response of manual and semi-automated FE models were compared. In the result, our semi-automated segmentation achieved Dice similarity coefficient (DSC) over 98% for both femur and tibia. The mechanical results (max principal stress, max principal strain, fluid pressure, fibril strain, and contact area) showed no significant differences between the manual and semi-automated FE models, indicating the effectiveness of the proposed semi-automated segmentation in creating accurate knee joint FE models. ( https://data.mendeley.com/datasets/k5hdc9cz7w/1 ).

cs.CV↗