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Comparison between the physical system and cyber system in a musculoskeletal model, illustrating the digital twin framework for simulating muscle activation and force transmission. Each step in the physical system corresponds to a similar step in the cyber system, enhancing parameter identifiability through alignment. The two-headed arrows represent the bidirectional validation process between experimental data and simulations.
Published Online: May 15, 2025
Fig. 1 Comparison between the physical system and cyber system in a musculoskeletal model, illustrating the digital twin framework for simulating muscle activation and force transmission. Each step in the physical system corresponds to a similar step in the cyber system, enhancing parameter identi... More about this image found in Comparison between the physical system and cyber system in a musculoskeleta...
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Workflow for multitrajectory optimization and subject-specific modeling of musculoskeletal models. Trtarg represents the target trajectory obtained from experimental data.
Published Online: May 15, 2025
Fig. 2 Workflow for multitrajectory optimization and subject-specific modeling of musculoskeletal models. Tr targ represents the target trajectory obtained from experimental data. More about this image found in Workflow for multitrajectory optimization and subject-specific modeling of ...
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Comparison of target trajectories. Unlike Fig. 2, which uses Trtarg derived from motion capture data, this figure presents Trtarg obtained from simulated experiments. The use of simulated experiments is necessary because real human subjects do not have known musculoskeletal model parameters. By replacing real motion capture data with synthetic control signals and a target model with known parameters, the optimized parameters can later be compared to the ground truth target model, allowing for validation of the method's effectiveness.
Published Online: May 15, 2025
Fig. 4 Comparison of target trajectories. Unlike Fig. 2 , which uses Tr targ derived from motion capture data, this figure presents Tr targ obtained from simulated experiments. The use of simulated experiments is necessary because real human subjects do not have known musculoskeletal model para... More about this image found in Comparison of target trajectories. Unlike Fig. 2 , which uses Tr targ der...
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Simulated muscle activation patterns computed using sine-generated control signals applied to a musculoskeletal model in OpenSim. These results serve as a reference for validating the proposed optimization method and do not correspond to any specific subject.
Published Online: May 15, 2025
Fig. 5 Simulated muscle activation patterns computed using sine-generated control signals applied to a musculoskeletal model in OpenSim. These results serve as a reference for validating the proposed optimization method and do not correspond to any specific subject. More about this image found in Simulated muscle activation patterns computed using sine-generated control ...
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Electromyography activation patterns for subject: (a) EMG activation patterns for Subject 1, (b) EMG activation patterns for Subject 2, (c) EMG activation patterns for Subject 3, and (d) EMG activation patterns for Subject 4
Published Online: May 15, 2025
Fig. 6 Electromyography activation patterns for subject: ( a ) EMG activation patterns for Subject 1, ( b ) EMG activation patterns for Subject 2, ( c ) EMG activation patterns for Subject 3, and ( d ) EMG activation patterns for Subject 4 More about this image found in Electromyography activation patterns for subject: ( a ) EMG activation patt...
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Comparison of single-task and multitask optimization results. Top row: Single-task optimization performance across two tasks. Bottom row: Multitask optimization performance across two tasks: (a) Single-task optimization (task 1), (b) single-task optimization (task 2), (c) multi-task optimization (task 1), and (d) multi-task optimization (task 2).
Published Online: May 15, 2025
Fig. 7 Comparison of single-task and multitask optimization results. Top row: Single-task optimization performance across two tasks. Bottom row: Multitask optimization performance across two tasks: ( a ) Single-task optimization (task 1), ( b ) single-task optimization (task 2), ( c ) multi-task o... More about this image found in Comparison of single-task and multitask optimization results. Top row: Sing...
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Schematic illustration of finite element (FE) modeling and simulation. Lung lobe segmentations were obtained from CT volumes, and the thoracic cavity was defined as the boundary of the whole lung segmentation. Surface meshes were generated from thoracic cavity segmentations, and volumetric meshes were generated from lobar segmentations. Deformable image registration was performed to determine displacements at the thoracic cavity from end inspiration to end expiration. These displacements were prescribed at the end inspiration thoracic cavity surface nodes to simulate exhale motion. Two FE simulations were performed for each subject: one where frictionless sliding was allowed along lobar boundaries and one where a large friction coefficient was defined between lobes to eliminate sliding motion. For both models, sliding was allowed between the lobes and thoracic cavity.
Published Online: May 15, 2025
Fig. 1 Schematic illustration of finite element (FE) modeling and simulation. Lung lobe segmentations were obtained from CT volumes, and the thoracic cavity was defined as the boundary of the whole lung segmentation. Surface meshes were generated from thoracic cavity segmentations, and volumetric ... More about this image found in Schematic illustration of finite element (FE) modeling and simulation. Lung...
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Effect of lobar sliding on lung parenchymal distortion in each subject for (a) and (b) tidal breathing and (c) and (d) breath hold cohorts. (a) and (c) Spatial mean ADI in the left lung and right lung was lower in the sliding model than the nonsliding model for every subject indicating that lobar sliding decreased parenchymal distortion. (b) and (d) The median percent change in mean ADI from nonsliding models to sliding models is shown for both cohorts. The reduction in distortion was more pronounced in the right lung than the left lung. Left lung data adapted from our previous study [7].
Published Online: May 15, 2025
Fig. 2 Effect of lobar sliding on lung parenchymal distortion in each subject for ( a ) and ( b ) tidal breathing and ( c ) and ( d ) breath hold cohorts. ( a ) and ( c ) Spatial mean ADI in the left lung and right lung was lower in the sliding model than the nonsliding model for every subject ind... More about this image found in Effect of lobar sliding on lung parenchymal distortion in each subject for ...
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Distribution of parenchymal distortion in the right (a)–(c) and left (d)–(f) lung of a representative subject. Elements of each lobe of both the sliding and nonsliding models were sorted into bins based on their distance from the lobar fissures. Stair-step plots of the mean ADI of elements within each fissure distance bin (a) and (d) show that lobar sliding greatly reduced parenchymal distortion in the right lower lobe (RLL) and left lower lobe (LLL) near the fissure, but there was little difference far from the fissures. On the other hand, the right upper lobe (RUL) and left upper lobe (LUL) experienced very little change in ADI with lobar sliding regardless of distance from fissure. The right middle lobe (RML) had less ADI near the fissures in the sliding model than the nonsliding model but had much greater ADI in the sliding model near the tip of the lobe distal to the fissures. Contour plots of ADI in frontal (left) and sagittal (right) cross sections of the nonsliding (b) and (e) and sliding (c) and (f) models reinforce these observations. In the right lung, the most highly concentrated distortion occurs near the fissure between the RLL and RML, and in the left lung it occurs near the diaphragm in the LLL which might explain why these lobes received the most benefit from lobar sliding. Left lung data adapted from our previous study [7].
Published Online: May 15, 2025
Fig. 3 Distribution of parenchymal distortion in the right ( a )–( c ) and left ( d )–( f ) lung of a representative subject. Elements of each lobe of both the sliding and nonsliding models were sorted into bins based on their distance from the lobar fissures. Stair-step plots of the mean ADI of e... More about this image found in Distribution of parenchymal distortion in the right ( a )–( c ) and left ( ...
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The effect of greater lobar sliding on mean ADI difference between sliding and nonsliding models in each subject in both the right (a) and left (b) lungs. The strong correlation between mean fissure max shear and mean ADI difference suggests that, consistent with our hypothesis, more lobar sliding led to a greater reduction in parenchymal distortion when fissure sliding was permitted. Left lung data adapted from our previous study [7].
Published Online: May 15, 2025
Fig. 4 The effect of greater lobar sliding on mean ADI difference between sliding and nonsliding models in each subject in both the right ( a ) and left ( b ) lungs. The strong correlation between mean fissure max shear and mean ADI difference suggests that, consistent with our hypothesis, more lo... More about this image found in The effect of greater lobar sliding on mean ADI difference between sliding ...