![]() ![]() In addition, it could help to analyze how reflexes, central pattern generators, and higher locomotion centers control those adaptations. For example, in the case that one limb is injured or perturbed, a whole model can help to understand compensation mechanisms at joint and muscular levels in every limb. However, a model with all four legs and the musculoskeletal area between them is necessary to address questions about adaptivity. Specific dog musculoskeletal models exist for the hindlimbs 11, 16, 17, 18. Simulated models, rather than invasive methods, are best suited to evaluate force transmission between segmental elements 10, 11, 12, 13, 14, 15. In order to quantify the joint load, the internal transmission of force through the skeleton, and consequently the generation of force in the muscles is required 10. Inverse dynamics analysis is a method of the engineering sciences that combines kinetic, kinematic, and morphometric data to provide an indirect way to describe the causes of movement patterns. ![]() To analyze joint mechanics, inverse dynamic analysis is typically used 6, 7, 8, 9. To investigate how body size, physique, agility, and diseases influence joint control and load in dogs, it is necessary to model the morphology with the external and internal forces that produce locomotion. dysplasia), are not sufficiently understood. 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. There exists an important body of work related to kinematic and dynamical differences between healthy dogs and dogs with musculoskeletal diseases 2, 3, 4, 5. familiaris) is interesting to investigate because of the wide ranges of body size, body mass, and physique of their more than 400 globally recognized breeds 1. 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. ![]() The activation patterns predicted from the model exhibit good agreement with experimental data for most of the forelimb muscles. We identified three muscle synergy groups by using hierarchical clustering. We used inverse dynamics and static optimization to estimate muscle activations based on experimental data. We tested the validity of the model by identifying forelimb muscle synergies of a walking Beagle. Our model has three key-features: three-dimensionality, scalability, and modularity. We describe here the methods we used to create a detailed musculoskeletal model with 84 degrees of freedom and 134 muscles. The use of detailed musculoskeletal models may help fill the knowledge gap. Collecting this data through in vivo measurements/records of joint forces and loads on deep/small muscles is complex, invasive, and sometimes unethical. In the last several years, the number of clinical and biomechanical studies on dog locomotion has increased. The domestic dog is interesting to investigate because of the wide range of body size, body mass, and physique in the many breeds. ![]()
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