A recent study reveals how skateboarders can use mathematical insights to increase their speed and height on half-pipes. Florian Kogelbauer, a mathematician from ETH Zurich, and his research team have examined how specific movements impact a skateboarder’s performance on U-shaped ramps. By alternating between crouching and standing in certain areas, skaters can generate extra momentum, leading to higher jumps and faster speeds. This research, published in Physical Review Research, could lead to more efficient techniques for skaters aiming to improve their skills.
Modelling Momentum on Half-Pipes
The research was published in American Physical Society Journal. The technique of “pumping,” or alternating between crouching and standing, is essential for building speed on half-pipes. Kogelbauer’s team created a model to show how the body’s centre of mass affects movement on a ramp, much like the mechanics of a swing. In their calculations, they found that crouching while moving downhill and standing while moving uphill helps skaters gain height more effectively. This rhythm, the team suggests, could help skaters reach higher elevations on the ramp in fewer motions.
Testing the Theory with Real Skaters
To test the model’s validity, researchers observed two skateboarders as they navigated a half-pipe. They were asked to reach a specific height as quickly as possible. Video analysis revealed that the more experienced skater naturally followed the model’s suggested pattern, reaching the target height with fewer motions. The less experienced skater, who did not follow the pattern as precisely, required more time to reach the same height. This contrast suggests that experienced skaters intuitively apply these principles for better performance.
Broader 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 in body position can impact speed and height, this study offers insights that could make robotic movement more efficient. For engineers, this research indicates that straightforward models of human movement could be used to enhance robotic performance, providing an alternative to complex machine-learning models often used in robotics.
