New Model Developed at MIT Predicts How Shoe Properties Affect a Runner’s Performance

How it was developed: The team’s new model grew out of talks with collaborators in the sneaker industry, where designers have started to 3D print shoes at commercial scale.

• These designs incorporate 3D-printed midsoles that resemble intricate scaffolds, the geometry of which can be tailored to give a certain bounce or stiffness in specific locations across the sole.
• Fay and Hosoi looked first to represent a runner’s dynamics using a simple model. They drew inspiration from Thomas McMahon, a leader in the study of biomechanics at Harvard University, who in the 1970s used a very simple “spring and damper” model to model a runner’s essential gait mechanics.
• Using this gait model, McMahon predicted how fast a person could run on various track types, from traditional concrete surfaces to more rubbery material. The model showed that runners should run faster on softer, bouncier tracks that supported a runner’s natural gait.
• The insight was a revelation at the time, prompting Harvard to revamp its indoor track—a move that quickly accumulated track records, as runners found they could run much faster on the softer, springier surface.
• Fay and Hosoi developed a similar, simplified model of a runner’s dynamics. The model represents a runner as a centre of mass, with a hip that can rotate and a leg that can stretch. The leg is connected to a box-like shoe, with springiness and shock absorption that can be tuned, both vertically and horizontally.
• They reasoned that they should be able to input into the model a person’s basic dimensions, such as their height, weight, and leg length, along with a shoe’s material properties, such as the stiffness of the front and back midsole, and use the model to simulate what a person’s gait is likely to be when running in that shoe.
• The researchers realised that a person’s gait can depend on a less definable property, which they call the “biological cost function” — a quality that a runner might not consciously be aware of but nevertheless may try to minimise whenever they run.
• The team reasoned that if they can identify a biological cost function that is general to most runners, then they might predict not only a person’s gait for a given shoe but also which shoe produces the gait corresponding to the best running performance.
• The team then looked to a previous treadmill study, which recorded detailed measurements of runners, such as the force of their impacts, the angle and motion of their joints, the spring in their steps, and the work of their muscles as they ran, each in the same type of running shoe.
• Fay and Hosoi hypothesised that each runner’s actual gait arose not only from their personal dimensions and shoe properties, but also a subconscious goal to minimise one or more biological measures, yet unknown.
• To reveal these measures, the team used their model to simulate each runner’s gait multiple times. Each time, they programmed the model to assume the runner minimised a different biological cost, such as the degree to which they swing their leg or the impact that they make with the treadmill.
• They then compared the modelled gait with the runner’s actual gait to see which modelled gait — and assumed cost — matched the actual gait.
• In the end, the team found that most runners tend to minimise two costs: the impact their feet make with the treadmill and the amount of energy their legs expend.
• As a final step, the researchers simulated a wide range of shoe styles and used the model to predict a runner’s gait and how efficient each gait would be for a given type of shoe.