| Περίληψη: | The principles of musculoskeletal model and simulation have received much attention over the last decades, enabling the prediction of surgical treatments related to different motion limiting disorders. However, their application in clinical practice is still limited partly because the experimental equipment used for measuring the kinematics and kinetics required for the analysis is expensive and obtrusive. The assessment of the internal state (e.g., muscle forces and joint reaction loads) from those measurements does not lead to a unique solution, due to the inherit redundancy of the musculoskeletal system. More specifically, there are more degrees of freedom than those required to perform certain tasks and each degree of freedom is actuated by multiple muscles, leading to infinite combinations of muscle forces that satisfy the movement. Unfortunately, this raises the following questions: which is the true solution employed by the central nervous system and whether the choice of a particular solution can lead to misinterpretation of results?
Coordinate projection methods and their extension for musculoskeletal modeling and simulation are the topic of this thesis. These methods transform the equations of motion into a space of low- or high-dimensionality according to the projection operator. Five different subspaces are studied, namely task, joint, muscle, constraint and null space as well as their relationship. Task space projection simplifies the motion planning problem and the process of synthesizing virtual simulations. This is of great importance, since simulations can be arranged effortlessly and intuitively. Joint space representation is the de facto standard for formulating the underlying equations of motion and dynamics simulation methods. Muscle space projection provides a convenient representation for interfacing musculoskeletal and segmental level models and forms a basis for practical control applications. Constraint projection serves to incorporate the kinematic constraints into the inverse dynamics model. Null space projection can be used to model the redundancy effects of the musculoskeletal system consistently and identify the feasible solution that satisfy the movement and physiological muscle constraints. The redundant nature of the musculoskeletal system introduces variability/uncertainty in simulated quantities leading to misinterpretation of the results if ignored. Therefore, this groundwork provides the appropriate formalization to successfully address these issues, facilitating the application of broader types of studies in the realm of motor coordination.
|
|---|
