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Updated: Sep 7, 2025

Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation
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Machine Vision-Based Ping Pong Ball Rotation Trajectory Tracking Algorithm.

Yilei Wang1, Ling Wang2

  • 1Sports Department, Hangzhou Medical College, Hangzhou 310053, China.

Computational Intelligence and Neuroscience
|June 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an automatic ping pong ball detection system using deep neural networks for tracking and rotation analysis. The system accurately measures ball trajectory and rotation, crucial for developing advanced sports training robots.

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Area of Science:

  • Computer Vision
  • Sports Technology
  • Robotics

Background:

  • The sports industry is rapidly integrating intelligent upgrading driven by computer vision.
  • Automated analysis of sports videos aids coaches and can inform robot-assisted training.
  • Extracting technical and tactical data from sports videos is crucial for performance analysis.

Purpose of the Study:

  • To design and examine an automatic detection system for ping pong balls.
  • To detect the motion trajectory and rotation information of ping pong balls.
  • To lay the foundation for table tennis robot design through trajectory prediction.

Main Methods:

  • Utilized a deep neural network for ping pong ball detection and tracking.
  • Implemented Fourier transform for speed measurement and CNN for rotation direction detection.
  • Developed an LSTM-based algorithm for ping pong ball trajectory prediction.

Main Results:

  • Achieved better results in ping pong ball detection and tracking on a custom dataset and system tests.
  • Demonstrated effective rotation measurement and speed calculation, particularly for lower-speed datasets.
  • Successfully predicted table tennis trajectories, validating the system's potential for robot assistance.

Conclusions:

  • The developed system effectively addresses ping pong ball tracking and rotation measurement challenges.
  • The integration of deep learning methods enhances the accuracy and efficiency of sports video analysis.
  • This research provides a foundational system for intelligent sports training and robotic applications.