Objective: To improve the accuracy of acupuncture manipulation modeling and inheritance, this article explores the feasibility of automatically classifying "twirling" and "lifting and thrusting", two basic acupuncture manipulations in science of acupuncture and moxibustion, with the computer vision technology.
Methods: A hybrid deep learning network model was designed based on 3D convolutional neural network and long-short term memory neural network to extract the spatial-temporal features of video frame sequences, which were then input into the classifier for classification.
Results: The model discriminated between "twirling" and "lifting and thrusting" manipulations in 200 videos, with the training and verification accuracy reaching up to 95.4% and 95.3%, respectively.
Conclusion: This computer vision-based acupuncture manipulation classification system provides an effective way for the data extraction and inheritance of acupuncture manipulations.
目的：为了提升针刺手法建模与传承的准确性，面向针刺实践中的手法视频，本文探讨利用计算机视觉技术对中医针灸学中“捻转”和“提插”这两类基本针刺手法进行分类的可行性。方法：构建一种计算机视觉下的基于三维卷积神经网络和长短时记忆网络的混合深度学习网络模型，提取针刺手法视频帧序列的时空特征，将其输入分类器中实现分类。结果：针对200组录制的医师针刺手法视频，应用所提混合网络模型对“捻转”和“提插”两类手法进行分类，训练准确率达到95.4%，验证准确率达到95.3%。结论：本系统可为针刺手法的数据提取与传承提供一条有效途径。.
Keywords: 3D convolutional neural network; Acupuncture manipulations; Computer vision; Deep learning; Long-short term memory network.