Abstract
In this study, we identified a low-dimensional representation of control mechanisms in throwing motions from a variety of subjects and target distances. 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. Representative features of throwing motions in all of these spaces were chosen to be investigated. 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. Two synergy extraction methods were tested to assess their consistency. 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. 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.
Highlights
Understanding how humans control motion is an important aspect in a variety of fields, ranging from neuroscience to robotics and animation [1]
A database containing synergies and their relationships with task space goals, for a richer variety of motions, degrees of freedom, and muscles, which could serve as a basis to synthesize motions in physics-based animation. It seems that motion control can be encapsulated through lower dimensional control representation of each task we perform, to achieve fast, efficient, and coordinated movements
Our study has found common control features among subjects in the task, joint, and activation spaces, especially through the extraction of muscle synergies from a set of EMG signals, for a dynamic and acyclic motion
Summary
Understanding how humans control motion is an important aspect in a variety of fields, ranging from neuroscience to robotics and animation [1]. 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. 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 [2]. Characters with more detailed actuators (or muscles) are starting to be used for motion synthesis. The use of muscle-based characters entails several advantages such as smoother torque generation [3], more realistic responses to perturbations [4, 5], and an ease to simulate pathologies and fatigue [6, 7]. Computationally expensive optimization-based solutions, which are unlikely to represent how humans control motions, are used to compute a high number of control signals
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