A recent study shows how skateboarders can use mathematical insights to increase their speed and height on the half-pipe. Mathematician Florian Kogelbauer and his research team at ETH Zurich have investigated how specific movements affect a skateboarder’s performance on a U-shaped ramp. By alternating between bending and standing in certain areas, skaters can generate additional momentum, leading to higher jumps and faster speeds. The research, published in Physical Review Research, could lead to more efficient techniques for skaters aiming to improve their skills.
Modeling Momentum on Half-Pipes
This research was published in the American Physical Society Journal. The technique of “pumping”, or alternating between bending and standing, is essential to increasing speed on the half-pipe. Kogelbauer’s team created a model to show how the body’s center of mass affects speed on a ramp, much like the mechanics of a swing. In his calculations, he found that bending when moving downwards and standing when moving upward helps skaters gain height more effectively. The team suggests that this rhythm may help skaters reach greater heights on ramps at lower speeds.
Testing the Theory with Real Skaters
To test the validity of the model, researchers observed two skateboarders navigating a half-pipe. They were asked to reach a specific height as quickly as possible. Video analysis revealed that more experienced skaters naturally followed the model’s suggested pattern, and reached the target height with less speed. Less experienced skaters, who did not follow the pattern exactly, required more time to reach the same height. This paradox suggests that experienced skaters intuitively apply these principles for better performance.
Wide applications beyond skateboarding
According to Sorina Lupu, an engineer at the California Institute of Technology, this simplified model may also have applications in robotics. By demonstrating how minimal adjustments to body position can affect speed and height, this study provides insights that could make robotic locomotion more efficient. For engineers, this research indicates that straightforward models of human movement can be used to enhance robotic performance, providing an alternative to the complex machine-learning models often used in robotics.
