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Estimation of alpine skier posture using machine learning techniques.

Bojan Nemec¹, Tadej Petrič², Jan Babič³

¹Department of Automation, Biocybernetics and Robotics, Jožef Stefan Institute, Ljubljana 1000, Slovenia. bojan.nemec@ijs.si.

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Area of Science:

Biomechanics
Sports Technology
Machine Learning

Background:

Global Navigation Satellite System (GNSS) offers high-precision measurements in alpine skiing.
GNSS provides single-point data, necessitating methods to estimate skier's body parameters like Center of Mass (COM) and ski trajectories.
Traditional inverted-pendulum models oversimplify skier biomechanics.

Purpose of the Study:

To develop and evaluate machine learning models for estimating COM and ski trajectories in alpine skiing.
To overcome the limitations of simplified biomechanical models in previous studies.
To provide a more accurate approximation of the skier's body with nine degrees-of-freedom.

Main Methods:

Proposed two machine learning approaches: artificial neural networks and a statistical generalization method.
Utilized a nine degrees-of-freedom model for a more faithful approximation of the skier's body.
Evaluated methods using reference measurements from a giant slalom course.

Main Results:

Both proposed machine learning methods outperformed the traditional inverted-pendulum method in accuracy.
Demonstrated superior estimation of COM and ski trajectories compared to existing models.
Validated the effectiveness of machine learning in complex biomechanical analyses.

Conclusions:

Machine learning techniques provide a significant advancement for biomechanical measurements in alpine skiing.
The proposed methods offer a more accurate and reliable approach to analyzing skier dynamics.
Highlights the potential of AI in enhancing sports performance analysis and understanding.