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Related Experiment Video

Updated: Sep 2, 2025

Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation

Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation

Published on: October 28, 2021

An action recognition method for manual acupuncture techniques using a tactile array finger cot.

Chong Su¹, Chen Wang¹, Shengyi Gou¹

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

Computers in Biology and Medicine

|August 5, 2022

Summary

This summary is machine-generated.

This study introduces a new tactile sensor for Traditional Chinese Medicine (TCM) acupuncture, accurately recognizing four manual techniques using piezoelectric signals and advanced AI. This innovation improves objective assessment in acupuncture practice.

Keywords:

Ensemble learning Modeling and recognition Polyvinylidene fluoride (PVDF)Tactile sensors

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Last Updated: Sep 2, 2025

Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation

Three-Dimensional Finger Motion Tracking during Needling: A Solution for the Kinematic Analysis of Acupuncture Manipulation

Published on: October 28, 2021

Visualizing Motion Patterns in Acupuncture Manipulation

Visualizing Motion Patterns in Acupuncture Manipulation

Published on: July 16, 2016

Author Spotlight: Development of a Standardized Acupuncture Tool Inspired by Advanced Techniques for Improved Safety and Precision

Author Spotlight: Development of a Standardized Acupuncture Tool Inspired by Advanced Techniques for Improved Safety and Precision

Published on: January 10, 2025

Area of Science:

Biomedical Engineering
Traditional Chinese Medicine
Machine Learning

Background:

Existing systems for measuring Traditional Chinese Medicine (TCM) manual acupuncture parameters lack accuracy and cause clinical inconvenience.
Objective classification of fundamental TCM manual acupuncture techniques (e.g., reinforcing, reducing) is underdeveloped in current literature.

Purpose of the Study:

To develop a novel tactile sensing system for capturing acupuncturist finger-needle interaction during manual acupuncture.
To create and validate an AI-driven method for recognizing four basic TCM manual acupuncture techniques based on piezoelectric signals.

Main Methods:

A multi-PVDF film-based tactile array finger cot was developed to collect piezoelectric signals.
Signal processing involved capturing piezoelectric data within specific 'windows' and extracting relevant features.
An ensemble learning model, specifically CatBoost, was implemented for action classification of acupuncture techniques.

Main Results:

The developed tactile array finger cot successfully collected piezoelectric signals during manual acupuncture.
The feature-based CatBoost ensemble learning model achieved high accuracy: 99.63% validation and 92.45% test accuracy.
The system demonstrated effective recognition of four distinct manual acupuncture techniques performed by 15 TCM physicians.

Conclusions:

The proposed tactile array finger cot and CatBoost-based recognition method offer a promising solution for objective assessment of TCM manual acupuncture.
This technology can enhance the standardization and understanding of TCM manual acupuncture techniques.
Further research should explore the efficiency and limitations of this action recognition approach in diverse clinical settings.