![]() ![]() Neuroscience provides several interesting ways to circumvent this issue, such as the theory of muscle synergies, that tries to reduce redundancy by identifying a simple and generic control representation of given tasks. Thus, it is necessary to define compact control schemes reducing the complexity of the control for such applications. ![]() Furthermore, computationally expensive optimization-based solutions, which are unlikely to represent how humans control motions, are used to compute a high number of control signals. However, the use of muscles complicates the control problem by augmenting nonlinearity and redundancy, as at least two muscles are necessary to actuate each degree of freedom. The use of muscle-based characters entails several advantages such as smoother torque generation, more realistic responses to perturbations, and an ease to simulate pathologies and fatigue. In the animation field, characters with more detailed actuators (or muscles) are starting to be used for motion synthesis. Our motivation lies in the domains of neuroscience and animation. More realistic motions imply a higher degree of similarity to humans, at the visual, kinematic, and dynamic level. In animation and robotics, identifying such mechanisms is the key to enhance the realism and efficiency of the motions in virtual humans and robots, since it would allow the development of more realistic motion controllers, reflecting a global control of motion. In neuroscience and biomechanics, some of the objectives of identifying such mechanisms are to validate an existing motor control theory, to diagnose and treat pathologies, or to enhance athletic performance. Several theories have been proposed which aim at unveiling the efficient and powerful mechanisms behind human motion generation. Understanding how humans control motion is an important aspect in a variety of fields, ranging from neuroscience to robotics and animation. Such results are useful to better represent mechanisms hidden behind such dynamical motions and could offer a promising control representation for synthesizing motions with muscle-driven characters. For the task features, the degrees of freedom (DoF), and the muscles under study, the results can be summarized as (1) a control representation across subjects consisting of only two synergies at the activation level and of representative features in the task and joint spaces, (2) a reduction of control redundancy (since the number of synergies are less than the number of actions to be controlled), (3) links between the synergies triggering intensity and the throwing distance, and finally (4) consistency of the extraction methods. Two synergy extraction methods were tested to assess their consistency. Features were extracted using factorization and clustering techniques from the muscle data of unexperienced subjects (with different morphologies and physical conditions) during a series of throwing tasks. Representative features of throwing motions in all of these spaces were chosen to be investigated. The control representation was identified at the kinematic level in task and joint spaces, respectively, and at the muscle activation level using the theory of muscle synergies. In this study, we identified a low-dimensional representation of control mechanisms in throwing motions from a variety of subjects and target distances. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |