A machine learning-based trajectory predictive modeling method for manual acupuncture manipulation.
10.13703/j.0255-2930.20250117-0001
- Author:
Jian KANG
1
;
Li LI
2
;
Shu WANG
3
;
Xiaonong FAN
4
;
Jie CHEN
5
;
Jinniu LI
5
;
Wenqi ZHANG
1
;
Yuhe WEI
1
;
Ziyi CHEN
1
;
Jingqi YANG
1
;
Jingwen YANG
6
;
Chong SU
1
Author Information
1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
2. First Teaching Hospital of Tianjin University of TCM; National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion; Tianjin Key Laboratory of Acupuncture and Moxibustion.
3. National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion; Affiliated Hospital of Tianjin Academy of TCM.
4. First Teaching Hospital of Tianjin University of TCM; Tianjin Key Laboratory of Acupuncture and Moxibustion.
5. Department of TCM, Beijing Zhongguancun Hospital.
6. School of Acupuncture-Moxibustion and Tuina, Beijing University of CM.
- Publication Type:Journal Article
- Keywords:
hand micromotion;
machine learning;
manual acupuncture manipulation;
skill transmission of acupuncture manipulation;
trajectory prediction
- MeSH:
Humans;
Acupuncture Therapy/instrumentation*;
Machine Learning;
Adult;
Male;
Female
- From:
Chinese Acupuncture & Moxibustion
2025;45(9):1221-1232
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To propose a machine learning-based method for predicting the trajectories during manual acupuncture manipulation (MAM), aiming to improve the precision and consistency of acupuncture practitioner' operation and provide the real-time suggestions on MAM error correction.
METHODS:Computer vision technology was used to analyze the hand micromotion when holding needle during acupuncture, and provide a three-dimensional coordinate description method of the index finger joints of the holding hand. Focusing on the 4 typical motions of MAM, a machine learning-based MAM trajectory predictive model was designed. By integrating the changes of phalangeal joint angle and hand skeletal information of acupuncture practitioner, the motion trajectory of the index finger joint was predicted accurately. Besides, the roles of machine learning-based MAM trajectory predictive model in the skill transmission of acupuncture manipulation were verified by stratified randomized controlled trial.
RESULTS:The performance of MAM trajectory predictive model, based on the long short-term memory network (LSTM), obtained the highest stability and precision, up to 98%. The learning effect was improved when the model applied to the skill transmission of acupuncture manipulation.
CONCLUSION:The machine learning-based MAM predictive model provides acupuncture practitioner with precise action prediction and feedback. It is valuable and significant for the inheritance and error correction of manual operation of acupuncture.
