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

Objective To study the relationship between environment selenium distribution characteristic and Kaschin-Beck disease (KBD) in the Yarlung Zangbo River banks and to provide some measures for prevention and control of KBD in the north side of the river bank.Methods Considering the geography and KBD distribution,we made a survey in Xietongmen (KBD area),Lazi and Sajia (non-KBD area) counties in 2013 and 2015.Water,soil,herbage,grain food and children hair samples were collected.Selenium of the samples was measured by hydride generation-atomic fluorescence spectrometry.Results A total of 246 samples of natural soil,cultivated soil,drinking water,food and forage,and 103 samples of children hair were collected.The selenium in natural soil,cultivated soil,herbage,barley,tsampa,self-produced wheat in non-KBD area in the south side of Yarlung Zangbo River bank were significantly higher than those in KBD areas in the north side [mean (μg/kg):288.62 vs 134.90,228.26 vs 160.28,41.85 vs 5.10,13.99 vs 4.02,12.64 vs 8.07,27.44 vs 13.56,U =7,23,0,19,62,0,P ＜ 0.05].Hair selenium in school children in KBD area in the north side was higher than that previously reported,but still significantly lower than that in non-KBD areas in the south side of the river bank [mg/kg:0.221 vs 0.306,U =650,P ＜ 0.01],and about 65.45％ (36/55)of school children in KBD area were at a risk of selenium deficiency in the north of the Yarlung Zangbo River bank.Conclusions The selenium contem in the food chain of soilplants-animals (human being) is significantly lower in KBD area in the north side of Yarlung Zangbo River bank than that in non-KBD areas in the south side.It's still a key factor for the occurrence and prevalence of KBD that low selenium in environment in KBD areas in the north side.