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Self-powered acceleration sensors arrayed by swarm intelligence for table tennis umpiring system.

Ke Lu1, Chaoran Liu1, Haiyang Zou2

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Summary

This study introduces an intelligent umpiring system using self-powered sensors to improve table tennis accuracy. The novel system precisely detects ball collision points, reducing human error in competitive sports officiating.

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Area of Science:

  • Sports Technology
  • Sensor Networks
  • Optimization Algorithms

Background:

  • Table tennis umpiring relies on subjective visual observation, leading to potential inaccuracies.
  • Existing methods lack precision in determining ball collision points during matches.

Purpose of the Study:

  • To develop a novel intelligent umpiring system for table tennis using arrayed self-powered acceleration sensor nodes.
  • To enhance the accuracy and reliability of officiating in table tennis competitions.

Main Methods:

  • Established a sensor node array model for detecting ball collision points on the table.
  • Applied level-based competitive swarm optimization to optimize sensor node distribution.
  • Reduced sensor count from 58 to 51 through optimization.

Main Results:

  • Achieved precise detection of ball collision points with uniform error distances below 3.5 mm.
  • Verified the theoretical reliability of the optimized sensor node distribution.
  • Demonstrated the effectiveness of the intelligent umpiring system in enhancing accuracy.

Conclusions:

  • The proposed system significantly improves the accuracy of table tennis umpiring.
  • Arrayed self-powered sensor nodes offer a reliable solution for automated sports officiating.
  • This research provides a foundation for advanced intelligent umpiring systems in competitive sports.