Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (No. 2022M3A9B6082791).
DOI QR Code
Acupuncture and acupressure apply needles or pressure to anatomical points for therapeutic benefit. The over 350 mapped acupuncture points in the human body can each treat various conditions, but anatomical variations make precisely locating these acupoints difficult. We propose a computer vision technique using the real-time hand and face tracking capabilities of the MediaPipe framework to identify acupoint locations. Our model detects anatomical facial and hand landmarks, and then maps these to corresponding acupoint regions. In summary, our proposed model facilitates precise acupoint localization for self-treatment and enhances practitioners' abilities to deliver targeted acupuncture and acupressure therapies.
This work was supported by the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (No. 2022M3A9B6082791).