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Badminton Activity Recognition Using Accelerometer Data.

Tim Steels1, Ben Van Herbruggen1, Jaron Fontaine1

  • 1IDLab, Department of Information Technology, Ghent University-imec, 9000 Ghent, Belgium.

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Summary
This summary is machine-generated.

Analyzing badminton movements is crucial for player development. This study uses low-cost sensors and a novel neural network to classify nine badminton activities with high precision, offering an accessible alternative to video analysis.

Keywords:
CNNDNNaccelerometeractivity recognitionbadmintongyroscopemachine learningneural network

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Area of Science:

  • Sports Science
  • Biomechanical Analysis
  • Machine Learning in Sports

Background:

  • Traditional video analysis for badminton training is expensive and intrusive.
  • Accurate analysis of player movements is vital for performance enhancement.
  • There is a need for cost-effective and user-friendly motion analysis tools.

Purpose of the Study:

  • To classify badminton movements using accelerometer and gyroscope data.
  • To develop a novel neural network for activity recognition in badminton.
  • To provide a low-cost, easy-to-use solution for badminton game analysis.

Main Methods:

  • Collected data using off-the-shelf accelerometer and gyroscope sensors.
  • Designed and implemented a novel convolutional neural network with variable frame sizes.
  • Organized a data capturing campaign to record badminton movements.
  • Evaluated the impact of sensor placement and sampling frequencies.

Main Results:

  • The novel neural network achieved 86% precision classifying nine activities using only accelerometer data at 50 Hz.
  • Incorporating gyroscope data increased precision to 99%.
  • The proposed method outperformed traditional convolutional neural networks (79% and 88% precision).

Conclusions:

  • The developed system offers a low-cost and user-friendly method for analyzing badminton games.
  • Sensor-based motion classification provides valuable insights for training and performance.
  • The novel neural network architecture effectively distinguishes various badminton activities.