School of Nutrition Clinic, The Third Hospital of Jinan Municipality, Jinan 250132, China.
Comput Intell Neurosci. 2022 Sep 5;2022:8488167. doi: 10.1155/2022/8488167. eCollection 2022.
Acupuncture and massage are among the oldest medical treatments in China. During the acupuncture process, as well as the subsequent needle extraction process, there are differences in the acupuncture intensity, treatment duration, and acupuncture depth. For both medical treatments of acupuncture and massage, this article learns and analyzes a large amount of literature and applies DBN neural network method to build a human skeletal model to simulate and identify medical professional steps such as acupuncture therapy. The research results show that the recognition rate of DBN reaches 92.1% after the training of 1000 samples. After learning all the training samples, the DBN model achieved feature recognition accuracy of 96.4%, 97.68%, 96.66%, and 92.27% for the test samples of mixed needling process, needle insertion operation, needle extraction operation, and rotary needle handling process, respectively. The research in this article can contribute to the modernization of Chinese medicine by maximizing the simulation of the force on the human body when receiving needling and tui-na, as well as the clinical treatment effect.
针灸和按摩是中国最古老的医疗方法之一。在针灸过程中，以及随后的针拔出过程中，针灸强度、治疗持续时间和针灸深度都存在差异。对于针灸和按摩这两种医疗治疗方法，本文学习和分析了大量文献，并应用 DBN 神经网络方法构建了一个人体骨骼模型，以模拟和识别针灸治疗等医疗专业步骤。研究结果表明，在经过 1000 个样本的训练后，DBN 的识别率达到 92.1%。在学习了所有的训练样本后，DBN 模型对混合针刺过程、针刺操作、针刺拔出操作和旋转针处理的测试样本的特征识别准确率分别达到 96.4%、97.68%、96.66%和 92.27%。本文的研究可以通过最大限度地模拟人体接受针刺和推拿时的受力以及临床治疗效果，为中医的现代化做出贡献。
