An Initial Study on Automated Acupoint Positioning for Laser Acupuncture

Kun Chan Lan, Chang Yin Lee, Guan Sheng Lee, Tzu Hao Tsai, Yu Chen Lee, Chih Yu Wang

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Acupuncture plays an important role in traditional Chinese medicine (TCM) and is one kind of an inexpensive and effective treatment. However, some people might be reluctant to receive acupuncture treatment due to fear of pain. Laser acupuncture, thanks to its painless and infection-free advantages, has recently become an alternative choice to traditional acupuncture. The accuracy of acupuncture point positioning has a decisive influence on the quality of laser acupuncture. In this study, built on top of our prior work, we proposed a low-cost automated acupoint positioning system for laser acupuncture. By integrating several machine learning algorithms and computer vision techniques, we design and implement a robot-assisted laser acupuncture system on top of a smartphone. Our contributions include the following: (a) development of an effective acupoint estimation algorithm with a localization error less than 5 mm; (b) implementation of a smartphone-controlled automated laser acupuncture system with lift-thrust function, as a point-of-care device, that can be used by patients to relieve their symptoms at home.

Original languageEnglish
Article number8997051
JournalEvidence-based Complementary and Alternative Medicine
Volume2022
DOIs
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • Complementary and alternative medicine

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