Engineers at MIT have developed a model that predicts how shoe properties affect a runner’s performance and could be used as a tool by designers looking to innovate in running shoe design.
A good shoe can make a huge difference for runners, but every runner is unique and a shoe that works for one might be less successful for another. At present, however, there’s no quick and easy way to know which shoe best suits a person’s particular running style.
The MIT engineer hope to change that with their new model, which predicts how certain shoe properties will affect a runner’s performance. The simple model incorporates a person’s height, weight and other general dimensions, along with shoe properties such as stiffness and springiness along the midsole. Using these inputs, the model then simulates a person’s running gait in a particular shoe. The researchers can then simulate how the gait changes with different shoe types and determine which shoe produces the best performance, which they define as the degree to which a runner’s expended energy is minimised.
While the model can accurately simulate changes in a runner’s gait when comparing two very different shoe types, it’s less discerning when comparing relatively similar designs. For this reason, the researchers believe that the current model would be best used as a tool for shoe designers looking to push the boundaries of sneaker design.
‘Shoe designers are starting to 3D print shoes, meaning they can now make them with a much wider range of properties than with just a regular slab of foam,’ said Sarah Fay, a postdoc in MIT’s Sports Lab and the Institute for Data, Systems, and Society. ‘Our model could help them design really novel shoes that are also high-performing.’
The 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.
‘With 3D printing, designers can tune everything about the material response locally,’ said Anette ‘Peko’ Hosoi, professor of mechanical engineering at MIT. ‘And they came to us and essentially said, “We can do all these things. What should we do?”’
‘Part of the design problem is to predict what a runner will do when you put an entirely new shoe on them,’ Fay added. ‘You have to couple the dynamics of the runner with the properties of the shoe.’
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, he predicted how fast a person could run on various track types, from traditional concrete surfaces to more rubbery material.
‘McMahon’s work showed that, even if we don’t model every single limb and muscle and component of the human body, we’re still able to create meaningful insights in terms of how we design for athletic performance,’ Fay said.
Following McMahon’s lead, 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 a person’s basic dimensions, along with a shoe’s material properties, and use the model to simulate what a person’s gait is likely to be when running in that shoe.
But they also realised that a person’s gait can depend on a less definable property, which they call the ‘biological cost function’ – a quality of which a runner might not consciously be aware but nevertheless may try to minimise whenever they run. The team reasoned that if they could 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.
With this in mind, the team 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. 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. ‘If we tell our model, “Optimise your gait on these two things,” it gives us really realistic-looking gaits that best match the data we have,’ Fay explained. ‘This gives us confidence that the model can predict how people will actually run, even if we change their shoe.’
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.
‘In some ways, this gives you a quantitative way to design a shoe for a 10K versus a marathon shoe,’ Hosoi says. ‘Designers have an intuitive sense for that. But now we have a mathematical understanding that we hope designers can use as a tool to kickstart new ideas.’
The research has been published in the Journal of Biomechanical Engineering.