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Classification of Tennis Shots with a Neural Network Approach.

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This study introduces a novel system for tennis analytics, combining sensor wristbands for data collection with deep convolutional neural networks (CNNs) for accurate shot classification. This innovation enhances sports data analysis for improved training methods.

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Area of Science:

  • Sports Science
  • Machine Learning
  • Biomechanical Analysis

Background:

  • Data analysis is crucial for optimizing sports training methods.
  • Existing tennis analytics lack integrated data generation and advanced classification.
  • Sensor technology and deep learning offer potential for enhanced sports performance insights.

Purpose of the Study:

  • To develop a reliable shot detection trigger and a deep neural network for classifying tennis shots.
  • To create a comprehensive dataset for training machine learning models in tennis.
  • To compare the performance of fully convolutional networks (FCN) and residual networks (ResNet) for time series classification of tennis strokes.

Main Methods:

  • Utilized a sensor wristband with an inertial measurement unit (IMU) to record 11 signals for data generation.
  • Developed a deep convolutional neural network (CNN) incorporating advanced machine learning techniques (Mish activation, Ranger optimizer).
  • Compared two state-of-the-art time series classification architectures: FCN and ResNet.

Main Results:

  • Generated a dataset of 5682 labelled tennis shots from 16 players across a wide age range.
  • Achieved high classification accuracy: 96% F1 score for main shots and 94% for expanded categories.
  • Demonstrated the effectiveness of the developed system in classifying diverse tennis shots.

Conclusions:

  • The study provides a robust foundation for advanced tennis performance analysis tools.
  • The developed system enables potential future applications like indicating shot success rates.
  • This integrated approach of data generation and deep learning classification represents a significant advancement in tennis analytics.