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[Development and application of computer vision-based acupuncture manipulation classification system].

Tao Tu1, Ye-Hao Su1, Chong Su1

  • 1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.

Zhen Ci Yan Jiu = Acupuncture Research
|June 30, 2021
PubMed
Summary
This summary is machine-generated.

This study uses computer vision to automatically classify acupuncture manipulations like "twirling" and "lifting and thrusting". The developed deep learning model achieved over 95% accuracy, enhancing acupuncture data inheritance.

Keywords:
3D convolutional neural networkAcupuncture manipulationsComputer visionDeep learningLong-short term memory network

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Area of Science:

  • Integrative and Complementary Medicine
  • Biomedical Engineering
  • Computer Science

Background:

  • Acupuncture manipulation techniques require precise modeling and inheritance for accurate practice.
  • Traditional methods for documenting and analyzing acupuncture manipulations are often manual and subjective.
  • Developing automated methods is crucial for standardizing and advancing acupuncture research.

Purpose of the Study:

  • To investigate the feasibility of using computer vision for automatic classification of acupuncture manipulations.
  • To differentiate between two fundamental techniques: "twirling" and "lifting and thrusting".

Main Methods:

  • A hybrid deep learning model combining 3D Convolutional Neural Network (3D CNN) and Long Short-Term Memory (LSTM) was developed.
  • The model extracts spatio-temporal features from video sequences of acupuncture manipulations.
  • These features are then used for classification of the manipulation techniques.

Main Results:

  • The classification system successfully distinguished between "twirling" and "lifting and thrusting" manipulations.
  • The model achieved high accuracy rates: 95.4% for training and 95.3% for verification.
  • Performance was evaluated on a dataset of 200 videos.

Conclusions:

  • Computer vision technology offers an effective approach for classifying acupuncture manipulations.
  • This system facilitates automated data extraction and improves the inheritance of acupuncture techniques.
  • The findings support the integration of AI in acupuncture research and practice.