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On-Line Detection and Segmentation of Sports Motions Using a Wearable Sensor.

Woosuk Kim1, Myunggyu Kim2

  • 1Creative Content Research Division, Electronics and Telecommunications Research Institute, 218 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea. airegin@etri.re.kr.

Sensors (Basel, Switzerland)
|March 23, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel wearable sensor system for automatically detecting and segmenting sports motions. The deep learning approach enables precise, temporally classified motion data for enhanced sports analysis.

Keywords:
deep neural networksdetectionsegmentationsports motionwearable sensor

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Area of Science:

  • Biomechanics and Sports Science
  • Wearable Sensor Technology
  • Machine Learning in Sports

Background:

  • Accurate observation of sports motions is crucial for performance analysis.
  • Current methods for motion analysis can be time-consuming and subjective.
  • Systematic, data-driven approaches are needed to support coaches and athletes.

Purpose of the Study:

  • To develop a novel method for automatic detection and segmentation of sports motions using wearable sensors.
  • To enable convenient, temporally classified motion data analysis.
  • To support systematic observation in sports motion analysis.

Main Methods:

  • Defined a motion model as a sequence of sub-motions with boundary states for explicit segmentation.
  • Designed a sequence classifier based on deep neural networks to detect sports motions from continuous sensor data.
  • Evaluated the method on soccer kicking and two-handed ball throwing motions.

Main Results:

  • The proposed method successfully achieved accurate detection and segmentation of sports motions.
  • The system automatically provides relevant motion data, classified by temporal phases.
  • Demonstrated utility in a sports motion analysis system for observational support.

Conclusions:

  • The novel wearable sensor approach effectively detects and segments sports motions.
  • Deep neural network-based sequence classification enables automated, precise motion analysis.
  • This method offers a valuable tool for systematic sports observation and performance enhancement.