Real-time location of acupuncture points based on anatomical landmarks and pose estimation models

Hadi Sedigh Malekroodi; Seon-Deok Seo; Jinseong Choi; Chang-Soo Na; Byeong-Il Lee; Myunggi Yi

doi:10.3389/fnbot.2024.1484038

Real-time location of acupuncture points based on anatomical landmarks and pose estimation models

Front Neurorobot. 2024 Nov 8:18:1484038. doi: 10.3389/fnbot.2024.1484038. eCollection 2024.

Authors

Hadi Sedigh Malekroodi¹, Seon-Deok Seo², Jinseong Choi², Chang-Soo Na³, Byeong-Il Lee^{1

4

5}, Myunggi Yi^{1

2

5}

Affiliations

¹ Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan, Republic of Korea.
² Major of Biomedical Engineering, Division of Smart Healthcare, Pukyong National University, Busan, Republic of Korea.
³ College of Korean Medicine, Dongshin University, Naju, Republic of Korea.
⁴ Major of Human Bioconvergence, Division of Smart Healthcare, Pukyong National University, Busan, Republic of Korea.
⁵ Digital of Healthcare Research Center, Institute of Information Technology and Convergence, Pukyong National University, Busan, Republic of Korea.

Abstract

Introduction: Precise identification of acupuncture points (acupoints) is essential for effective treatment, but manual location by untrained individuals can often lack accuracy and consistency. This study proposes two approaches that use artificial intelligence (AI) specifically computer vision to automatically and accurately identify acupoints on the face and hand in real-time, enhancing both precision and accessibility in acupuncture practices.

Methods: The first approach applies a real-time landmark detection system to locate 38 specific acupoints on the face and hand by translating anatomical landmarks from image data into acupoint coordinates. The second approach uses a convolutional neural network (CNN) specifically optimized for pose estimation to detect five key acupoints on the arm and hand (LI11, LI10, TE5, TE3, LI4), drawing on constrained medical imaging data for training. To validate these methods, we compared the predicted acupoint locations with those annotated by experts.

Results: Both approaches demonstrated high accuracy, with mean localization errors of less than 5 mm when compared to expert annotations. The landmark detection system successfully mapped multiple acupoints across the face and hand even in complex imaging scenarios. The data-driven approach accurately detected five arm and hand acupoints with a mean Average Precision (mAP) of 0.99 at OKS 50%.

Discussion: These AI-driven methods establish a solid foundation for the automated localization of acupoints, enhancing both self-guided and professional acupuncture practices. By enabling precise, real-time localization of acupoints, these technologies could improve the accuracy of treatments, facilitate self-training, and increase the accessibility of acupuncture. Future developments could expand these models to include additional acupoints and incorporate them into intuitive applications for broader use.

Keywords: acupuncture; computer vision; deep learning; pose estimation; traditional medicine.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (No. 2022M3A9B6082791).