OBJECTIVE: To develop an objective quantitative evaluative model of manual acupuncture manipulation (MAM) using three-dimensional motion tracking technology and machine learning, so as to provide a new approach to the study on acupuncture and moxibustion education and manipulation standardization. METHODS: A total of 120 undergraduate students in the major of acupuncture-moxibustion and tuina were recruited. The Simi Motion Ver.8.5 motion tracking system was used to collect the data of three types of MAM, balanced reinforcing and reducing by twisting, reinforcing technique by twisting and reducing technique by twisting. Eight quantitative parameters covering movement performance and stability were established. With 5 types of machine learning algorithms (logistic regression, random forest, support vector machine, K-nearest neighbor, and decision tree) adopted, the evaluative model was constructed, and the feature importance analyzed. RESULTS: In the evaluation of different types of MAM, the support vector machine presented the best for the effects of the balanced reinforcing and reducing by twisting, and the reducing by twisting (accuracy rates were both 0.88); and the logistic regression algorithm showed the optimal performance in evaluating the reinforcing by twisting (1.00 of accuracy rate). Feature importance analysis revealed that twisting velocity was the dominant parameter for evaluating the balanced reinforcing-reducing manipulation. The reinforcing and reducing of acupuncture techniques were more dependent on the left-hand twisting parameters and comprehensive performances, respectively. CONCLUSION The objective evaluative model of MAM based on three-dimensional motion tracking technology and machine learning demonstrates a reliable evaluative performance, providing a new technical approach to standardized assessment in acupuncture and moxibustion education.
Humans ; Male ; Acupuncture Therapy/instrumentation* ; Machine Learning ; Female ; Young Adult ; Adult ; Motion

With the rapid development of generative artificial intelligence(GAI),its application in TCM research has attracted increasing attention.This article explored the application prospects of GAI in TCM research,analyzed its specific application scenarios in TCM clinical research,intelligent device development,and herbal medicine research,and identified the main challenges currently faced,including insufficient logical rigor,lack of high-quality clinical data,and shortage of interdisciplinary talents.This article proposed solutions such as constructing TCM knowledge graphs,standardizing TCM data collection,enhancing objective measurement and evaluation,and cultivating artificial intelligence talents with a background in TCM,in order to o provide references for the objective and intelligent research and application of TCM based on GAI,and further improve the clinical efficacy and quality of TCM.

With the rapid development of generative artificial intelligence(GAI),its application in TCM research has attracted increasing attention.This article explored the application prospects of GAI in TCM research,analyzed its specific application scenarios in TCM clinical research,intelligent device development,and herbal medicine research,and identified the main challenges currently faced,including insufficient logical rigor,lack of high-quality clinical data,and shortage of interdisciplinary talents.This article proposed solutions such as constructing TCM knowledge graphs,standardizing TCM data collection,enhancing objective measurement and evaluation,and cultivating artificial intelligence talents with a background in TCM,in order to o provide references for the objective and intelligent research and application of TCM based on GAI,and further improve the clinical efficacy and quality of TCM.