Modeling Running via Optimal Control for Shoe Design.

Abstract

Shoe manufacturing technology is advancing faster than new shoe designs can viably be evaluated in human subject trials. To aid in the design process, this paper presents a model for estimating how new shoe properties will affect runner performance. This model assumes runners choose their gaits to optimize an intrinsic, unknown objective function. To learn this objective function, a simple two-dimensional mechanical model of runners was used to predict their gaits under different objectives, and the resulting gaits were compared to data from real running trials. The most realistic model gaits, i.e., the ones that best matched the data, were obtained when the model runners minimized the impulse they experience from the ground as well as the mechanical work done by their leg muscles. Using this objective function, the gait and thus performance of running under different shoe conditions can be predicted. The simple model is sufficiently sensitive to predict the difference in performance of shoes with disruptive designs but cannot distinguish between existing shoes whose properties are fairly similar. This model therefore is a viable tool for coarse-grain exploration of the design space and identifying promising behaviors of truly novel shoe materials and designs.