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LSTM-Guided Coaching Assistant for Table Tennis Practice.

Se-Min Lim1, Hyeong-Cheol Oh2, Jaein Kim3

  • 1Department of Electronic and Information Engineering, Korea University, 2511 Sejong-ro, Sejong-City 30016, Korea. jaewoong819@korea.ac.kr.

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Summary
This summary is machine-generated.

This study introduces a deep learning coaching assistant for table tennis players using wearable sensors. The method analyzes high-dimensional time series data to provide skill assessments and personalized feedback for improvement.

Keywords:
LSTMdeep learninglatent featuresprobabilistic inferenceskill assessmentstate space modelwearable sensors

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Area of Science:

  • Sports Science
  • Machine Learning
  • Wearable Technology

Background:

  • Wearable devices are increasingly used in healthcare and performance analysis.
  • Skill assessment from sensor data is crucial for personalized coaching.
  • Table tennis coaching can benefit from advanced data analysis techniques.

Purpose of the Study:

  • To develop a deep learning coaching assistant for table tennis practice.
  • To analyze high-dimensional time series data from wearable sensors.
  • To extract low-dimensional representations for effective coaching insights.

Main Methods:

  • Utilized Long Short-Term Memory (LSTM) networks for time series data.
  • Integrated deep state space models and probabilistic inference.
  • Applied the method to wearable Inertial Measurement Unit (IMU) sensor data.

Main Results:

  • Successfully characterized complex high-dimensional time series patterns.
  • Demonstrated the extraction of low-dimensional latent representations.
  • Provided valuable information for table tennis skill assessment and coaching.

Conclusions:

  • The deep learning approach effectively supports table tennis practice.
  • Wearable IMU sensors combined with advanced algorithms offer promising coaching solutions.
  • The method aids in differentiating player skill levels and identifying practiced skills.