Humanoid muscle movement representation

The importance of understanding human movement has spurred efforts to build systems with similar capabilities and has led to the construction of actuators, such as pneumatic artificial muscles, that have properties similar to those of human muscles. However, muscles are far more complex than these robotic actuators and will require new control perspectives. Our research aims to develop primitives that lead to various smooth, natural human movements. In this paper we propose a sparse-code representation for muscle fiber length activations obtained by applying Matching Pursuit on a parameterized representation of such movements. We employ accurate three-dimensional musculoskeletal models to simulate the lower body muscle fiber length changes for multiple cyclic movements that are captured from a human subject. We recreate the muscle fiber length changes and show that the signal can be economically encoded in terms of discrete movement elements. Each movement can thus be visualized as a sequence of coefficients for temporally displaced motor primitives.