Wearable Biomechanics and Video-Based Trajectory Analysis for Improving Performance in Alpine Skiing.

Area of Science:

• Sports Science
• Biomechanics
• Performance Analysis

Background:

• Current alpine skiing diagnostics often analyze trajectory geometry and biomechanics separately.
• This separation limits the comprehensive understanding of technical inefficiencies and performance optimization.
• There is a need for integrated approaches to enhance real-time training feedback.

Purpose of the Study:

• To evaluate an integrated analysis framework combining AI-powered trajectory analysis (OptiPath) with wearable inertial sensors (XSensDOT).
• To identify technical inefficiencies in giant slalom skiing by correlating spatial trajectory deviations with biomechanical execution.
• To determine the relationship between trajectory geometry, biomechanical variables, and skiing performance outcomes.

Main Methods:

• Thirty competitive youth alpine skiers (14-16 years) performed giant slalom runs with controlled lateral offsets.
• Skier trajectories were analyzed using computer vision (OptiPath), and lower-limb/trunk kinematics and acceleration were recorded via inertial measurement units (XSensDOT).
• Deviations from ideal trajectories were quantified, and biomechanical execution was assessed for coordination, loading, and acceleration.

Main Results:

• OptiPath accurately detected trajectory variations, but shorter paths did not consistently correlate with faster run times.
• Superior skiing performance was linked to efficient biomechanical execution, including coordinated trunk-lower limb motion and controlled vertical loading.
• Higher forward acceleration and reduced lateral corrections were key indicators of better performance, irrespective of trajectory length.

Conclusions:

• Trajectory geometry alone is insufficient for explaining alpine skiing performance outcomes.
• Integrating wearable biomechanics with trajectory modeling provides a practical, field-deployable tool for diagnosing technical inefficiencies.
• This combined approach offers a low-cost, effective method for supporting technique optimization in alpine skiing training.