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Chen Yin-Chen

Business Design Department, Chung Yuan Christian University
Zishen Technology

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⚖️ Physics Principles🏂 3D Tricks Analyzer

Application · Human Motion System

Snowboarding is a
Control TheoryLesson

Treat the human body as a robot, and the slope as a disturbing environment. Every movement in snowboarding is a sophisticated sensor-compute-execute closed-loop system.

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System Perspective

Four Core Principles of Human Snowboarding System

Automatic Control Theory describes not just machines, but all systems capable of 'sensing—reasoning—acting' including your body.

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Center of Gravity Control System

The human body on a slope is a 'closed-loop control system' continuously fighting gravity. The brain acts as the controller, vestibular system and foot pressure are sensors, muscle tension is the output adjustment.

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Angular Momentum Conservation

During aerial rotation (540°, 720°...), angular momentum is conserved. Tuck arms→moment of inertia decreases→angular velocity increases; extend body→slow down to align for landing—perfectly follows L = Iω.

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Sensor Fusion

Inner ear (gyroscope) + Eyes (vision) + Joint proprioception (IMU) → Brain uses a mechanism similar to Kalman Filter to merge multi-source sensor data and estimate 3D body posture.

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Snow Surface as Disturbance Input

Bumpy ice, powder, hard pack are all external disturbances to the system. Advanced snowboarding = improving system robustness, minimizing deviation correction time to steady state.

Closed-Loop Control

Brain's Real-Time Feedback Loop

1

Sense

Vestibular system measures acceleration and rotational velocity; feet sense ground pressure distribution

2

Process

Cerebellum integrates multi-source signals, predicts next-moment posture error (Predictive Model)

3

Execute

Brain commands core muscles, quadriceps, calves to output corrective torque

4

Feedback

New posture becomes next time step input, forming closed-loop

System Maturity

Snowboarding Proficiency = System Complexity

Beginner

System 'gain' is too low, sluggish error response. Stiff body = feedback delay. Learning goal: reduce response delay, increase sensor sensitivity.

Intermediate

Moderate gain, handles routine disturbances. Starting to show 'feedforward control' - predict based on terrain, adjust center of gravity before error occurs.

Advanced

System has complete 'internal model'. Brain accurately simulates physics, predicts action results, achieving near-zero-error stable control.

Physics Model

Core Formulas at a Glance

Angular Momentum Conservation

L = I · ω = 常數

In air rotation: tuck body→I decreases→ω increases

Newton's Second Law (Rotation)

τ = I · α

Torque = Moment of Inertia × Angular Acceleration

Friction (Snow Surface)

f = μ · N · cos θ

μ is snow condition coefficient, θ is board cutting angle

Use Real World as Teaching Material

In 'Natural Science and Artificial Intelligence' course, all these physics models have corresponding program simulations and interactive experiments.

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