Model Reduction for Human and Animal Locomotion

Complexity of existing mathematical models inhibits the analysis and the automatic animation of human and animal motion. Current techniques rely on numerical simulations of highly nonlinear differential equations in highdimensional spaces. A motion with such large-scale dynamics is difficult to control because of the sheer size of its control space. We are developing a method for constructing simpler mathematical models of lower dimensionality. The simplification consists of two steps: state-space reduction and model projection. The state-space reduction relies on statistical analysis of real-world observations to reduce the dimensionality of the original state space. The model projection constructs a new mathematical model on the reduced subspace by projecting the original equations of motion. The simplification will enable automated animation of complex mechanical systems.