New 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 examine how specific movements impact a skateboarder’s performance on U-shape ramps.
By alternating between crouching and standing in certain areas, skaters can generate extra momentum, leading to higher jumps and faster speeds.
This research, publish in Physical Review Research, could lead to more efficient techniques for skaters aiming to improve their skills.
The research was publish in American Physical Society Journal.
The new technique of “pumping,” or alternating between crouching and standing, is essential for building speed on half-pipes.
Florian Kogelbauer’s team create 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 find 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.
To test the model’s validity, researchers observe two skateboarders as they navigate a half-pipe.
They were ask to reach a specific height as quickly as possible.
Video analysis reveal that the more experience skater naturally follow the model’s suggest pattern, reaching the target height with fewer motions.
The less experience skater, who did not follow the pattern as precisely, require more time to reach the same height.
This contrast suggests that experience skaters intuitively apply these principles for better performance.
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 use to enhance robotic performance, providing an alternative to complex machine-learning models often use in robotics.
