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Tennis Timing Assessment by a Machine Learning-Based Acoustic Detection System: A Pilot Study.

Lucio Caprioli1, Amani Najlaoui1, Francesca Campoli1,2

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Summary

This study introduces an acoustic detection system using machine learning to analyze tennis timing. The system accurately measures stroke timing, offering objective data for player improvement and training strategies.

Keywords:
acoustic detectionmachine learningperformance analysistennistiming

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Area of Science:

  • Sports Science
  • Biomechanics
  • Acoustic Analysis

Background:

  • Tennis performance is heavily influenced by player timing, affecting stroke effectiveness and match outcomes.
  • Traditional evaluation methods often overlook temporal aspects, relying on subjective or spatial analysis.
  • Objective measurement of timing is needed for enhanced tennis training and performance analysis.

Purpose of the Study:

  • To evaluate the reliability of an acoustic detection system for analyzing temporal elements in tennis.
  • To assess the system's accuracy in measuring rally rhythm and stroke timing.
  • To explore the practical applications of acoustic analysis in tennis player development.

Main Methods:

  • Development of a machine learning algorithm to classify acoustic signals of ball impact.
  • Measurement of time intervals between racket and ground impacts for timing analysis.
  • On-court trials with expert and amateur players to validate system performance.

Main Results:

  • The machine learning algorithm achieved over 95% detection accuracy.
  • The acoustic system demonstrated an average on-court accuracy of 85%.
  • The system effectively evaluated player technical skills and identified areas for improvement.

Conclusions:

  • Quantitatively assessing tennis timing provides a novel approach for performance enhancement.
  • Objective data from acoustic analysis can inform training regimens and optimize game strategies.
  • The system shows significant potential for practical application in tennis coaching and analysis.