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Prototype Machine Learning Algorithms from Wearable Technology to Detect Tennis Stroke and Movement Actions.

Thomas Perri1,2, Machar Reid2, Alistair Murphy2

  • 1School of Sport, Exercise and Rehabilitation, Faculty of Health, University of Technology Sydney, Ultimo, NSW 2007, Australia.

Sensors (Basel, Switzerland)
|November 26, 2022
PubMed
Summary

Wearable sensors accurately detect tennis serves (98%) and groundstrokes (94%), aiding player training. Movement detection needs refinement for better tennis-specific footwork analysis.

Keywords:
accelerometerymachine learningracquet sportswearable technology

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Area of Science:

  • Sports Science
  • Biomechanics
  • Wearable Technology

Background:

  • Accurate monitoring of tennis performance is crucial for player development.
  • Wearable sensors offer a potential solution for objective data collection during training and matches.

Purpose of the Study:

  • To evaluate the accuracy of algorithms for detecting tennis strokes and movements using a wearable sensor.
  • To assess the feasibility of using wearable technology for detailed, long-term monitoring of tennis players.

Main Methods:

  • A cervically mounted wearable sensor (accelerometer, gyroscope, magnetometer) was used.
  • Data from high-performance tennis players during match-play and drills were collected.
  • Prototype algorithms classified strokes (forehand, backhand, serve) and movements, compared against manual coding.

Main Results:

  • High accuracy for serves (98%) and groundstrokes (94%).
  • Moderate accuracy for backhand slice (74%), with low detection for volleys (41-44%).
  • Tennis footwork predominantly classified as "Dynamic" (63%), with "Running" events accurately identified (74%).

Conclusions:

  • Wearable sensor data enables detailed monitoring of tennis training.
  • Algorithm improvements are needed for enhanced sensitivity in classifying tennis-specific movements.