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GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
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Related Experiment Video

Updated: Jun 11, 2025

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults
09:37

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Cross-Country Ski Skating Style Sub-Technique Detection and Skiing Characteristic Analysis on Snow Using

Shunya Uda1,2, Naoto Miyamoto2, Kiyoshi Hirose2,3

  • 1Graduate School of Health and Sports Science, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe, Kyoto 610-0394, Japan.

Sensors (Basel, Switzerland)
|September 28, 2024
PubMed
Summary

High-precision Global Navigation Satellite System (GNSS) devices accurately classify cross-country skiing sub-techniques (G2, G3, G4, G6P) during races. This technology offers valuable insights for training and tactical strategies in skating style skiing.

Keywords:
cross-country skiinghigh-precision GNSSskating techniquessub-technique classification

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Area of Science:

  • Sports Science
  • Biomechanics
  • Performance Analysis

Background:

  • Effective training and tactical strategies in cross-country skiing require detailed race analysis.
  • Understanding skiing sub-techniques is crucial for optimizing performance.

Purpose of the Study:

  • To develop a method for classifying cross-country skiing skating style sub-techniques using high-precision kinematic Global Navigation Satellite System (GNSS) devices.
  • To analyze skiing characteristics during timed races on snow.

Main Methods:

  • Two athletes were equipped with GNSS devices on their heads during skating style timed races.
  • GNSS data (position, head movements, velocity over ground) were used to define waveform patterns for sub-techniques (G2, G3, G4, G6P).
  • Classification accuracy was validated against video analysis.

Main Results:

  • High classification accuracies (95.0%–98.8%) were achieved for sub-techniques G2, G3, G4, and G6P.
  • The G4 sub-technique was identified as the fastest.
  • Sub-technique selection varied among skiers and was influenced by skiing velocity and course inclination.

Conclusions:

  • High-precision kinematic GNSS devices can accurately classify sub-techniques in skating style cross-country skiing.
  • These devices provide valuable data for detecting skiing characteristics.
  • Findings offer practical insights for athletes and coaches to enhance training and strategy development.