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Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on

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This study accurately classifies cross-country skiing sub-techniques using wearable inertial measurement units (IMUs) and machine learning. This innovation offers new insights into biomechanics for athletes and coaches.

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Area of Science:

  • Sports Science
  • Biomechanics
  • Machine Learning

Background:

  • Wearable inertial measurement units (IMUs) enable field-based biomechanical analysis of outdoor skiing.
  • Automatic classification of cross-country skiing sub-techniques offers unique analytical possibilities.

Purpose of the Study:

  • To optimize the accuracy of automatic classification of classical cross-country skiing sub-techniques.
  • To utilize two IMUs and a machine learning algorithm for enhanced classification.

Main Methods:

  • Employing a gyroscope on the skier's arm for reliable cycle detection.
  • Utilizing a neural network machine learning algorithm with chest-mounted accelerometer data for sub-technique classification.
  • Analyzing 24 datasets from 10 participants, with data divided into training, validation, and test sets.

Main Results:

  • Achieved an overall classification accuracy of 93.9% on test data.
  • Demonstrated the integration of sub-technique classification with standard sports equipment data (position, altitude, speed, heart rate).

Conclusions:

  • The developed method accurately classifies cross-country skiing sub-techniques.
  • Combining classified sub-technique data with physiological and biomechanical data provides novel insights for coaches, athletes, and researchers.