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Amin Komeili

Publications and source records attributed to Amin Komeili.

5 recordsLinked to original sources

Quantifying lower-limb muscle coordination during cycling using electromyography-informed muscle synergies

Assessment of muscle coordination during cycling may provide insight into motor control strategies and movement efficiency. This study evaluated muscle synergies and coactivation patterns as indicators of neuromuscular coordination in lower-limb across three power levels of cycling. Twenty recreational cyclists performed a graded cycling test on a stationary bicycle ergometer. Electromyography was recorded bilaterally from seven lower-limb muscles and muscle synergies were extracted using non-negative matrix factorization. The Coactivation Index (CI), Synergy Index (SI), and Synergy Coordination Index (SCI) were calculated to assess muscle coordination patterns. Four muscle synergies were identified consistently across power levels, with changes in synergy composition and activation timing correlated with increased muscular demands. As power level increased, the CI showed reduced muscle coactivation at the knee and greater muscle coactivation at the ankle. The SI revealed a greater contribution of the synergy weights of the extensor muscles than those of the flexor muscles at the knee. In contrast, the relative EMG contribution of hip extensor and flexor muscles remained consistent with increasing power levels. The SCI increased significantly with increasing power level, suggesting a reduction in the size of the synergy space and improved neuromuscular coordination. These findings provide insight into how the central nervous system modulates its response to increasing mechanical demands. Combining synergy and coactivation indices offers a promising approach to assess motor control, inform rehabilitation, and optimize performance in cycling tasks.

physics.med-ph

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

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

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

Automatic Classification of Symmetry of Hemithoraces in Canine and Feline Radiographs

Purpose: Thoracic radiographs are commonly used to evaluate patients with confirmed or suspected thoracic pathology. Proper patient positioning is more challenging in canine and feline radiography than in humans due to less patient cooperation and body shape variation. Improper patient positioning during radiograph acquisition has the potential to lead to a misdiagnosis. Asymmetrical hemithoraces are one of the indications of obliquity for which we propose an automatic classification method. Approach: We propose a hemithoraces segmentation method based on Convolutional Neural Networks (CNNs) and active contours. We utilized the U-Net model to segment the ribs and spine and then utilized active contours to find left and right hemithoraces. We then extracted features from the left and right hemithoraces to train an ensemble classifier which includes Support Vector Machine, Gradient Boosting and Multi-Layer Perceptron. Five-fold cross-validation was used, thorax segmentation was evaluated by Intersection over Union (IoU), and symmetry classification was evaluated using Precision, Recall, Area under Curve and F1 score. Results: Classification of symmetry for 900 radiographs reported an F1 score of 82.8% . To test the robustness of the proposed thorax segmentation method to underexposure and overexposure, we synthetically corrupted properly exposed radiographs and evaluated results using IoU. The results showed that the models IoU for underexposure and overexposure dropped by 2.1% and 1.2%, respectively. Conclusions: Our results indicate that the proposed thorax segmentation method is robust to poor exposure radiographs. The proposed thorax segmentation method can be applied to human radiography with minimal changes.

eess.IV