Objective

2

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

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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

2

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

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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.