当前测量系统的物理参数(旋转频率,和振幅)的传统中医(TCM)手动针灸在临床应用中往往会造成干扰和不便,并且在手动针灸操作过程中无法准确捕获医生手指的触觉信号。此外,文献很少讨论四种基本的手动针灸技术的分类(通过旋转和旋转(RFTR)加强,通过旋转和旋转(RDTR)减少,通过提升和推力(RFLT)加固,并通过提升和推力(RDLT)减少)。为了解决这个问题,我们开发了一种基于多PVDF薄膜的触觉阵列指床,用于在手动针灸操作过程中从针灸师的指针接触中收集压电信号。为了认识四种典型的中医手法针刺技术,我们开发了一种在相关的“窗口”中捕获压电信号的方法,并随后提取特征以模拟针灸技术。接下来,我们创建了一个基于集成学习的动作分类器,用于手动针灸技术识别。最后,所提出的分类器用于识别由15名中医医生基于使用触觉阵列指床收集的压电信号执行的四种类型的手动针灸技术。在所有的方法中,我们提出的基于特征的CatBoost集成学习模型实现了99.63%的最高验证准确率和92.45%的最高测试准确率.此外,我们提供了使用这种动作识别方法的效率和局限性。
The current measurement systems for the physical parameters (rotation frequency, and amplitude) of Traditional Chinese Medicine (TCM) manual acupuncture tend to cause disturbance and inconvenience in clinical application and do not accurately capture the tactile signals from the physician\'s finger during manual acupuncture operations. In addition, the literature rarely discusses classification of the four basic manual acupuncture techniques (reinforcing by twirling and rotating (RFTR), reducing by twirling and rotating (RDTR), reinforcing by lifting and thrusting (RFLT), and reducing by lifting and thrusting (RDLT)). To address this problem, we developed a multi-PVDF film-based tactile array finger cot to collect piezoelectric signals from the acupuncturist\'s finger-needle contact during manual acupuncture operations. In order to recognize the four typical TCM manual acupuncture techniques, we developed a method to capture piezoelectric signals in related \"windows\" and subsequently extract features to model acupuncture techniques. Next, we created an ensemble learning-based action classifier for manual acupuncture technique recognition. Finally, the proposed classifier was employed to recognize the four types of manual acupuncture techniques performed by 15 TCM physicians based on the piezoelectric signals collected using the tactile array finger cot. Among all the approaches, our proposed feature-based CatBoost ensemble learning model achieved the highest validation accuracy of 99.63% and the highest test accuracy of 92.45%. Moreover, we provide the efficiency and limitations of using this action recognition method.