Abstract

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.

Details

Title
A three-dimensional musculoskeletal model of the dog
Author
Stark, Heiko 1 ; Fischer, Martin S 1 ; Hunt, Alexander 2 ; Fletcher, Young 3 ; Quinn, Roger 3 ; Andrada Emanuel 1 

 Friedrich-Schiller-University Jena, Institute of Zoology and Evolutionary Research with Phyletic Museum, Jena, Germany (GRID:grid.9613.d) (ISNI:0000 0001 1939 2794) 
 Portland State University, Department of Mechanical and Material Engineering, Portland, USA (GRID:grid.262075.4) (ISNI:0000 0001 1087 1481) 
 Case Western Reserve University, Department of Mechanical and Aerospace Engineering, Cleveland, USA (GRID:grid.67105.35) (ISNI:0000 0001 2164 3847) 
Publication year
2021
Publication date
2021
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2534802936
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.