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.
Humans ; Acupuncture Therapy/instrumentation* ; Machine Learning ; Adult ; Male ; Female

OBJECTIVE: To investigate the effect of manual acupuncture manipulations (MAMs) on subcutaneous muscle tissue, by developing quantitative models of "lifting and thrusting" and "twisting and rotating", based on machine learning techniques. METHODS: A depth camera was used to capture the acupuncture operator's hand movements during "lifting and thrusting" and "twisting and rotating" of needle. Simultaneously, the ultrasound imaging was employed to record the muscle tissue responses of the participants. Amplitude and angular features were extracted from the movement data of operators, and muscle fascicle slope features were derived from the data of ultrasound images. The dynamic time warping barycenter averaging algorithm was adopted to align the dual-source data. Various machine learning techniques were applied to build quantitative models, and the performance of each model was compared. The most optimal model was further analyzed for its interpretability. RESULTS: Among the quantitative models built for the two types of MAMs, the random forest model demonstrated the best performance. For the quantitative model of the "lifting and thrusting" technique, the coefficient of determination (R²) was 0.825. For the "twisting and rotating" technique, R² reached 0.872. CONCLUSION Machine learning can be used to effectively develop the models and quantify the effects of MAMs on subcutaneous muscle tissue. It provides a new perspective to understand the mechanism of acupuncture therapy and lays a foundation for optimizing acupuncture technology and designing personalized treatment regimen in the future.
Humans ; Acupuncture Therapy/methods* ; Machine Learning ; Male ; Adult ; Female ; Subcutaneous Tissue/diagnostic imaging* ; Young Adult

This study organizes and classifies acupuncture-related standards, and analyzes the current status and existing problems. At present, there are 63 international standards, 40 national standards, 6 industry standards, 43 local standards, and 194 group standards related to acupuncture. The current situation is characterized by an irrational standard structure and incomplete coverage of relevant fields; insufficient coordination mechanisms, with overlapping and redundant standards; slow updating of standards, lacking timeliness. It is suggested that the development of acupuncture-related standards should be promoted through the following measures: improving the structure of the standard system and establishing a sound collaborative management mechanism; implementing full-cycle management of standards in line with technological advancements; and building an acupuncture standards information platform to provide one-stop services.
Acupuncture Therapy/standards* ; Humans ; Acupuncture/standards*

Objective:To construct an analytic method aimed at the characteristics of the commonly method of supplementing and pouring of acupuncture based on the analysis and modeling of ultrasound images around acupoint region in the process of acupuncture.Methods:A total of 7 healthy subjects who underwent physical examination in Beijing Zhongguancun Hospital from June,2022 to June,2023 were selected,and their Kongzui acupoints were acupunctured by 10 acupuncturists with associate senior title as 4 kinds of acupuncture manipulations included reinforcing by twisting and rotating(RFTR),reducing by twisting and rotating(RDTR),reinforcing by lifting and thrusting(RFLT),and reducing by lifting and thrusting(RDLT).The B-ultrasound diagnostic device was used to collect the images of muscle and fascial tissue below the acupoint,so as to construct the model of images.The definition of virtual acupuncture point was adopted to study the regulation of perturbation of subcutaneous tissue that was caused after the skin was acupunctured by needle.The change regulation of the virtual acupuncture point of muscle bundle below skin at Zuikong acupoint of subjects was analyzed.Results:The difference value of average absolution value between peak and trough of the trajectory of virtual acupuncture point of twisting and rotating was 0.066±0.045,and the average value of amplitude of this method was less than that(0.428±0.276)of lifting and thrusting method,and the twisting and rotating method was uniform and symmetrical,and there was difference between two kinds of acupuncture methods.The characteristics of computer graphics was used to qualify the work effect of lifting and thrusting,and reinforcing and reducing,which showed the heavy insertion and light lifting of RFLT,and showed heavy lifting and light insertion of RDLT,thus distinguished the two methods[(RFLT)and(RDLT)].Conclusions:The ultrasound imaging and computer graphics can be used to analyze the regularity of the common"reinforcing and reducing"method of acupuncture and moxibustion.