Generation of human walking paths

This work investigates the way humans plan their paths in a goal-directed motion. The person can be viewed as an optimal controller that plans the path minimizing a certain (unknown) cost function. Taking this viewpoint, the problem can be formulated as an inverse optimal control one, i.e., starting from control and state trajectories we want to figure out the cost function used by a person while planning the path. To test the envisaged ideas, a set of walking paths of different volunteers were recorded using a motion capture facility. The collected data have been used to compare a solution to the inverse control problem coming from the literature to a novel one. The obtained results, ranked using the discrete Fréchet distance, show the effectiveness of the proposed approach.