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Context-Aware Winter Sports Based on Multivariate Sequence Learning.

Byung-Kil Han1, Je-Kwang Ryu2, Seung-Chan Kim3

  • 1Telerobotics and Control Laboratory, Korea Advanced Institute of Science Technology, Daejeon 34141, Korea.

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Summary
This summary is machine-generated.

This study introduces an intelligent system for estimating winter sports status using sensor data. It accurately identifies activities like skiing and snowboarding, and conditions such as snow or ice.

Keywords:
deep learningmotion contextmultivariate signalsrecurrent neural networksequence model

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Area of Science:

  • Sports Science
  • Artificial Intelligence
  • Sensor Technology

Background:

  • Winter sports like skiing and snowboarding generate unique sensor signals.
  • Understanding player context (e.g., ski lift vs. skiing, snow vs. ice) is crucial for performance and safety.

Purpose of the Study:

  • To develop an intelligent system for estimating player status in downhill winter sports.
  • To categorize motion context using multivariate time-series sensor signals.

Main Methods:

  • Utilized a custom embedded system with motion sensors and piezo transducers.
  • Captured inertial and vibration signals during winter activities.
  • Employed supervised learning with artificial neural networks (1D CNN, GRU, LSTM).

Main Results:

  • The system accurately estimates player status in various winter sports contexts.
  • Distinguishes between different activities (e.g., skiing, snowboarding) and ground conditions (e.g., wet, snowy).
  • Demonstrated feasibility of using sensor data for context-aware analysis.

Conclusions:

  • The proposed intelligent system effectively estimates player status in downhill winter sports.
  • Sensor-based motion context categorization is feasible and valuable.
  • This technology has potential applications in sports analytics and safety.