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A three-dimensional musculoskeletal model of the dog
The domestic dog is interesting to investigate because of the wide range of body size, body mass, and physique in the many breeds. In the last several years, the number of clinical and biomechanical studies on dog locomotion has increased. However, the relationship between body structure and joint l...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8166944/ https://www.ncbi.nlm.nih.gov/pubmed/34059703 http://dx.doi.org/10.1038/s41598-021-90058-0 |
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author | Stark, Heiko Fischer, Martin S. Hunt, Alexander Young, Fletcher Quinn, Roger Andrada, Emanuel |
author_facet | Stark, Heiko Fischer, Martin S. Hunt, Alexander Young, Fletcher Quinn, Roger Andrada, Emanuel |
author_sort | Stark, Heiko |
collection | PubMed |
description | The domestic dog is interesting to investigate because of the wide range of body size, body mass, and physique in the many breeds. In the last several years, the number of clinical and biomechanical studies on dog locomotion has increased. However, the relationship between body structure and joint load during locomotion, as well as between joint load and degenerative diseases of the locomotor system (e.g. dysplasia), are not sufficiently understood. Collecting this data through in vivo measurements/records of joint forces and loads on deep/small muscles is complex, invasive, and sometimes unethical. The use of detailed musculoskeletal models may help fill the knowledge gap. We describe here the methods we used to create a detailed musculoskeletal model with 84 degrees of freedom and 134 muscles. Our model has three key-features: three-dimensionality, scalability, and modularity. We tested the validity of the model by identifying forelimb muscle synergies of a walking Beagle. We used inverse dynamics and static optimization to estimate muscle activations based on experimental data. We identified three muscle synergy groups by using hierarchical clustering. The activation patterns predicted from the model exhibit good agreement with experimental data for most of the forelimb muscles. We expect that our model will speed up the analysis of how body size, physique, agility, and disease influence neuronal control and joint loading in dog locomotion. |
format | Online Article Text |
id | pubmed-8166944 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81669442021-06-02 A three-dimensional musculoskeletal model of the dog Stark, Heiko Fischer, Martin S. Hunt, Alexander Young, Fletcher Quinn, Roger Andrada, Emanuel Sci Rep Article The domestic dog is interesting to investigate because of the wide range of body size, body mass, and physique in the many breeds. In the last several years, the number of clinical and biomechanical studies on dog locomotion has increased. However, the relationship between body structure and joint load during locomotion, as well as between joint load and degenerative diseases of the locomotor system (e.g. dysplasia), are not sufficiently understood. Collecting this data through in vivo measurements/records of joint forces and loads on deep/small muscles is complex, invasive, and sometimes unethical. The use of detailed musculoskeletal models may help fill the knowledge gap. We describe here the methods we used to create a detailed musculoskeletal model with 84 degrees of freedom and 134 muscles. Our model has three key-features: three-dimensionality, scalability, and modularity. We tested the validity of the model by identifying forelimb muscle synergies of a walking Beagle. We used inverse dynamics and static optimization to estimate muscle activations based on experimental data. We identified three muscle synergy groups by using hierarchical clustering. The activation patterns predicted from the model exhibit good agreement with experimental data for most of the forelimb muscles. We expect that our model will speed up the analysis of how body size, physique, agility, and disease influence neuronal control and joint loading in dog locomotion. Nature Publishing Group UK 2021-05-31 /pmc/articles/PMC8166944/ /pubmed/34059703 http://dx.doi.org/10.1038/s41598-021-90058-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Stark, Heiko Fischer, Martin S. Hunt, Alexander Young, Fletcher Quinn, Roger Andrada, Emanuel A three-dimensional musculoskeletal model of the dog |
title | A three-dimensional musculoskeletal model of the dog |
title_full | A three-dimensional musculoskeletal model of the dog |
title_fullStr | A three-dimensional musculoskeletal model of the dog |
title_full_unstemmed | A three-dimensional musculoskeletal model of the dog |
title_short | A three-dimensional musculoskeletal model of the dog |
title_sort | three-dimensional musculoskeletal model of the dog |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8166944/ https://www.ncbi.nlm.nih.gov/pubmed/34059703 http://dx.doi.org/10.1038/s41598-021-90058-0 |
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