Tao Tu1, Ye-Hao Su1, Chong Su1, Lei Wang2, Ya-Nan Zhao2, Jie Chen3. 1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China. 2. Institute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing 100700. 3. Department of Traditional Chinese Medicine, Beijing Zhongguancun Hospital, Beijing 100190.
Abstract
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.
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.
Keywords:
3D convolutional neural network; Acupuncture manipulations; Computer vision; Deep learning; Long-short term memory network