Modeling Running via Optimal Control for Shoe Design.

Area of Science:

• Biomechanics
• Sports Engineering
• Human Movement Science

Background:

• Shoe technology advances rapidly, outpacing human trial evaluations.
• Predicting the performance impact of new shoe designs is challenging.

Purpose of the Study:

• To develop a model estimating how novel shoe properties affect runner performance.
• To understand the objective function runners optimize during gait.

Main Methods:

• A 2D mechanical runner model was used to predict gaits under various objective functions.
• Model-predicted gaits were compared against real running trial data.
• The objective function minimizing ground impulse and muscle work best matched empirical data.

Main Results:

• The model successfully predicted gaits when runners minimized ground impulse and muscle work.
• This objective function allows prediction of gait and performance changes with different shoe conditions.
• The model can differentiate performance impacts of disruptive shoe designs but not subtle variations.

Conclusions:

• The developed model serves as a viable tool for coarse-grain exploration of shoe design spaces.
• It can identify promising performance characteristics of novel shoe materials and designs.
• This approach aids the shoe design process by predicting performance before human trials.