Parameter optimization model of learning in stepping motion

In this study we combine the representation of motion by a finite number of hardwired functions with parameter optimization to model learning during a stepping motion. Representation of experimental kinematic data by a finite number of predetermined functions and undetermined coefficients was analyzed. Least squares approximation was used to represent experimental data of stepping motions over obstacles of different heights. Functional relationships between coefficients and obstacles heights were also obtained. Learning of stepping over an obstacle was then formulated as a finite dimensional optimization problem. The pattern of foot path, and joint angles trajectories obtained by this learning model, were then compared to the experimental data. The results of the data fitting analysis and of the optimization process as a model for motion learning, indicate that motion can be adequately represented by a set of hardwired functions, and a finite number of task dependent coefficients.