Image-Based Musculoskeletal Models to Accurately Reproduce a Maximum Voluntary Isometric Contraction Test In Silico
Musculoskeletal models and computational simulations are increasingly employed in clinical and research settings, as they provide insights into human biomechanics by estimating quantities that cannot be easily measured in vivo (e.g., joint contact forces). However, their clinical application remains...
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MDPI AG
2024-09-01
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| Online Access: | https://www.mdpi.com/2076-3417/14/19/8678 |
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| author | Francesca Bottin Marco Viceconti Giorgio Davico |
| author_facet | Francesca Bottin Marco Viceconti Giorgio Davico |
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| collection | DOAJ |
| description | Musculoskeletal models and computational simulations are increasingly employed in clinical and research settings, as they provide insights into human biomechanics by estimating quantities that cannot be easily measured in vivo (e.g., joint contact forces). However, their clinical application remains limited by the lack of standardized protocols for developing personalized models, which in turn heavily rely on the modeler’s expertise and require task-specific validation. While motor tasks like walking and cycling have been widely studied, simulating a maximal knee extensor dynamometry test remains unexplored, despite its relevance in rehabilitation. This study aims to fill this gap by investigating the minimum amount of experimental data required to accurately reproduce a maximal voluntary contraction test in silico. For nine healthy young females, four different subject-specific musculoskeletal models with increasing levels of personalization were developed by incorporating muscle volume data from medical images and electromyographic signal envelopes to adjust, respectively, muscle maximal isometric force and tetanic activation limits. At each step of personalization, simulation outcomes were compared to experimental data. Our findings suggest that to reproduce in silico accurately the isometric dynamometry test requires information from both medical imaging and electromyography, even when dealing with healthy subjects. |
| format | Article |
| id | doaj-art-0b8d9a10eb2f4b5c87d11749140824bb |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-0b8d9a10eb2f4b5c87d11749140824bb2025-08-20T01:47:41ZengMDPI AGApplied Sciences2076-34172024-09-011419867810.3390/app14198678Image-Based Musculoskeletal Models to Accurately Reproduce a Maximum Voluntary Isometric Contraction Test In SilicoFrancesca Bottin0Marco Viceconti1Giorgio Davico2Department of Industrial Engineering, Alma Mater Studiorum—University of Bologna (IT), 40136 Bologna, ItalyDepartment of Industrial Engineering, Alma Mater Studiorum—University of Bologna (IT), 40136 Bologna, ItalyDepartment of Industrial Engineering, Alma Mater Studiorum—University of Bologna (IT), 40136 Bologna, ItalyMusculoskeletal models and computational simulations are increasingly employed in clinical and research settings, as they provide insights into human biomechanics by estimating quantities that cannot be easily measured in vivo (e.g., joint contact forces). However, their clinical application remains limited by the lack of standardized protocols for developing personalized models, which in turn heavily rely on the modeler’s expertise and require task-specific validation. While motor tasks like walking and cycling have been widely studied, simulating a maximal knee extensor dynamometry test remains unexplored, despite its relevance in rehabilitation. This study aims to fill this gap by investigating the minimum amount of experimental data required to accurately reproduce a maximal voluntary contraction test in silico. For nine healthy young females, four different subject-specific musculoskeletal models with increasing levels of personalization were developed by incorporating muscle volume data from medical images and electromyographic signal envelopes to adjust, respectively, muscle maximal isometric force and tetanic activation limits. At each step of personalization, simulation outcomes were compared to experimental data. Our findings suggest that to reproduce in silico accurately the isometric dynamometry test requires information from both medical imaging and electromyography, even when dealing with healthy subjects.https://www.mdpi.com/2076-3417/14/19/8678computational simulationsknee joint in extensionmaximum voluntary isometric contraction testmusculoskeletal modelingpersonalized muscle properties |
| spellingShingle | Francesca Bottin Marco Viceconti Giorgio Davico Image-Based Musculoskeletal Models to Accurately Reproduce a Maximum Voluntary Isometric Contraction Test In Silico Applied Sciences computational simulations knee joint in extension maximum voluntary isometric contraction test musculoskeletal modeling personalized muscle properties |
| title | Image-Based Musculoskeletal Models to Accurately Reproduce a Maximum Voluntary Isometric Contraction Test In Silico |
| title_full | Image-Based Musculoskeletal Models to Accurately Reproduce a Maximum Voluntary Isometric Contraction Test In Silico |
| title_fullStr | Image-Based Musculoskeletal Models to Accurately Reproduce a Maximum Voluntary Isometric Contraction Test In Silico |
| title_full_unstemmed | Image-Based Musculoskeletal Models to Accurately Reproduce a Maximum Voluntary Isometric Contraction Test In Silico |
| title_short | Image-Based Musculoskeletal Models to Accurately Reproduce a Maximum Voluntary Isometric Contraction Test In Silico |
| title_sort | image based musculoskeletal models to accurately reproduce a maximum voluntary isometric contraction test in silico |
| topic | computational simulations knee joint in extension maximum voluntary isometric contraction test musculoskeletal modeling personalized muscle properties |
| url | https://www.mdpi.com/2076-3417/14/19/8678 |
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