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Baseball Player Behavior Classification System Using Long Short-Term Memory with Multimodal Features.

Shih-Wei Sun1,2, Ting-Chen Mou3, Chih-Chieh Fang4

  • 1Department of New Media Art, Taipei National University of the Arts, Taipei 112, Taiwan. swsun@newmedia.tnua.edu.tw.

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Summary
This summary is machine-generated.

This study introduces a novel system using IoT sensors and cameras for accurate baseball player behavior classification. The deep learning model achieves over 95% accuracy in recognizing player actions.

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Area of Science:

  • Sports Science
  • Computer Vision
  • Machine Learning

Background:

  • Accurate player behavior analysis is crucial in sports science for performance evaluation and injury prevention.
  • Existing methods for human behavior recognition often lack the precision required for complex sports like baseball.

Purpose of the Study:

  • To develop and validate a preliminary system for classifying baseball player behaviors using heterogeneous sensor data.
  • To leverage advanced machine learning techniques for enhanced accuracy in sports analytics.

Main Methods:

  • Acquisition and segmentation of signals from depth cameras and multiple inertial sensors.
  • Feature extraction using time-variant skeleton vector projection and statistical inertial sensor data.
  • Implementation of a deep learning-based scheme for behavior classifier training.

Main Results:

  • The proposed system accurately recognizes numerous baseball player behaviors by analyzing heterogeneous sensor signals.
  • The deep learning behavior classification system demonstrated an accuracy exceeding 95% on the developed dataset.

Conclusions:

  • The developed system provides a robust and accurate method for baseball player behavior classification.
  • This approach shows significant potential for applications in sports performance analysis and training.