http://yetl.yabesh.ir/yetl1/handle/yetl/19038 2026-08-23T23:42:36Z http://yetl.yabesh.ir/yetl1/handle/yetl/4316949 Predicting the Effects of Walker Height and Weight Support on Assisted Gait Using Physics-Based Predictive Simulations Pagès Sanchis, Carlos; Maceratesi, Filippo; Febrer-Nafría, Míriam Abstract. Walker-assisted gait is widely used in clinical rehabilitation for individuals with muscle weakness and balance impairments. This study presents a first step toward developing a predictive simulation framework that integrates a 3D full-body musculoskeletal model driven by muscle torque generators within an optimal control problem. We calibrated the muscle torque generators model using experimental isometric and isokinetic data from a healthy participant, obtained from a biodex dynamometer equipment. To assess the predictive capability of the framework, we evaluated the effects of walker height and percentage of body weight support on walker-assisted gait patterns by running nine predictive simulations across varying walker configurations. Results showed that effects of walker height were well predicted (e.g., elbow flexion increased with walker height from a mean value of 81.59 deg to 93.86 deg), while effects of weight support were only partially predicted (e.g., upper body joints did not show a clear trend with changes in weight support). Results suggest that developing a detailed hand-walker interaction model would significantly improve the realism of the simulations. This study provides an important step toward optimizing walker-assisted gait through simulation-based design and personalization. 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4316946 Vertebral Body Kinematics Measured From T1-Weighted Magnetic Resonance Imaging With Optimized Rigid Registration Peloquin, John M.; Newman, Harrah R.; Elliott, Dawn M. Abstract. Magnetic resonance imaging (MRI) is a useful method to noninvasively measure vertebral kinematics (rotations and translations). Measurement of vertebral kinematics should be both fast and accurate, a need potentially satisfied by automatic registration of reference–deformed image pairs. So far, MRI registration has not been systematically optimized for this application. The objective of this study therefore was to apply automatic 3D image registration methods to the measurement of vertebral kinematics from MRI: first, to systematically optimize all registration parameters to minimize registration error across a representative dataset; second, to reanalyze a separate, previously published, MRI dataset of diurnal, flexion, and extension vertebral body (VB) mechanics using the optimized registration to reduce the dataset's measurement error and clarify its interpretation. Validation against manual registrations indicated that midsagittal vertebral body marker position error in the sagittal plane was 0.10±0.08 mm, well below the pixel size of 0.5 mm, with corresponding negligible errors in change in wedge angle (Δ wedge angle), change in disc height (Δ disc height), and A–P translation. Reanalysis of diurnal mechanics data revealed that diurnal Δ wedge angle, with subjects scanned supine, is essentially zero despite significant A–P translation and disc height loss. Distinct kinematics at the L5–S1 disc level were also observed. Relative to manual marker-based methods, use of this image registration method in future work would allow sample size to be halved with no change in statistical power. This optimized registration method will increase the efficiency of future research and may allow detection of effects that would otherwise be overlooked. 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4316942 Integrated Workflow Finite Element Modeling of the Temporomandibular Joint: Toward a Methodical and Reproducible Approach Baugnon, Lilian; Nicot, Romain; Bethune, Nicolas; Lecomte-Grosbras, Pauline; Witz, Jean-François; Mayeur, Olivier Abstract. This study presents a patient-specific parametric model of the temporomandibular joint, designed to be semi-automated, and reproducible for multiple patients. The numerical model is used to evaluate mandibular stress distribution under different interaction and loading conditions. The main contribution is to demonstrate the feasibility of an integrated, fast, and streamlined workflow for generating accurate biomechanical models tailored to each patient. The proposed method, which relies on standard clinical images complemented by artificial intelligence-assisted segmentation of bone and muscles, enables the integration of patient-specific anatomical features and mechanical variability. A finite element model of the skull, mandible, teeth, and articular disks was constructed from calibrated computed tomography data. Material properties were automatically assigned using Hounsfield units, distinguishing between cortical bone, cancellous bone, and dental tissue. Sensitivity of key modeling parameters (mesh density, material, friction coefficients, muscle force vectors) was evaluated using abaqus/standard. Hounsfield-units-driven material assignment provides a Young modulus distribution aligned with the literature, while maintaining patient specificity. Artificial intelligence-based muscle reconstruction reveals that stress fields stabilize with increased directional vector refinement, reinforcing biomechanical accuracy and confirming the necessity of multivector muscle loading. This patient-specific parametric model accurately reproduces the distribution of mandibular stresses and offers a promising tool for surgical planning, pathology simulation, and the evaluation of personalized treatment strategies. 2026-01-01T00:00:00Z http://yetl.yabesh.ir/yetl1/handle/yetl/4316936 Parameter Identification for a Four-Compartment Controller Muscle Fatigue Model Bhandari, Baivab; Rakshit, Ritwik; Yang, James Abstract. Localized muscle fatigue arises from interacting central and peripheral mechanisms whose contributions vary with contraction intensity and joint velocity. The four-compartment controller with enhanced recovery (4CCr) model captures these processes but its practical use is limited by parameter identifiability and sensitivity to optimization settings. This study systematically evaluates the robustness of 4CCr parameters across joints, velocities, optimization algorithms, and sample-size subsets. Residual capacity (RC) is extracted from peak isometric torque across five isometric–isokinetic cycles in 32 participants, and the three unknown 4CCr parameters—baseline peripheral fatigue (FPi0), baseline peripheral recovery (RPi0), and velocity coefficient (ki)—are estimated using genetic algorithm (GA) and particle swarm optimization (PSO). Comprehensive GA hyperparameter sweeps and PSO validation reveal strong equifinality in (RPi0, ki) and unexpectedly high stability in FPi0 across subjects, velocities, and solvers. Sample-size analyses (N = 10, 14, 18) further confirm that FPi0 converges rapidly with increasing dataset, whereas RPi0 and ki fluctuate substantially across datasets and therefore do not yield consistent physiological interpretations. The recovery analysis indicates that the 4CCr model reflects realistic two-phase recovery, unlike the three-compartment controller with enhanced recovery (3CCr) model which recovers rapidly. These findings demonstrate that peripheral fatigue rate is the only well-constrained parameter in the 4CCr muscle fatigue model, and that fixing FPi0 enables more reliable optimization of the remaining parameters. This work clarifies parameter identifiability within the 4CCr model and supports the development of a more stable, generalizable fatigue model for digital human simulations and velocity-dependent strength prediction. 2026-01-01T00:00:00Z