基于微小运动放大技术的三维脉搏图像检测与脉象识别

      3D Pulse Image Detection and Pulse Pattern Recognition Based on Subtle Motion Magnification Technology

      • 摘要: 针对已有中医指端触觉双目视觉检测技术中因接触膜离面位移过小导致三维脉搏信号重构误差较大的问题,该研究提出一种基于微小运动放大技术的三维脉搏图像检测方法,并探究其在脉象识别中的应用。首先,研制基于双目视觉的三维脉搏图像检测系统,获取脉搏图像信号作为实验数据。然后,利用相位运动视频放大算法对原始信号进行放大,对放大后的信号进行三维重构,得到三维脉搏信号。在此基础上,从三维脉搏信号中提取了9个特征,并用双样本Kolmogorov-Smirnov检验进行特征选择。最后,通过决策树、随机森林等机器学习算法对沉、代、洪、滑、疾5种脉象进行识别。实验结果表明:相比未使用微小运动放大技术,所提方法明显提高了波形的清晰度、振幅的稳定性及周期的规律性。同时,在脉象识别中,平均准确率达到96.29%±0.26%。

         

        Abstract: To address the problem of large reconstruction errors in 3D pulse signals caused by excessively small out-of-plane displacement of the contact membrane in the existing traditional Chinese medicine fingertip tactile binocular vision detection technology, this study proposes a 3D pulse image detection method based on subtle motion magnification technology and explores its application in pulse pattern recognition. Firstly, a 3D pulse image detection system based on binocular vision to obtain pulse image signals is developed as experimental data. Then, the phase motion video magnification algorithm is used to amplify the original signals, and the amplified signals are reconstructed in three dimensions to obtain 3D pulse signals. On this basis, nine features are extracted from the 3D pulse signals and features selection is performed using a two-sample Kolmogorov-Smirnov test. Finally, machine learning algorithms such as decision trees and random forests are used to identify the five types of pulse conditions: deep pulse, intermittent pulse, flooding pulse, slippery pulse, and rapid pulse. The experimental results show that compared to the methods without subtle motion magnification technology, the proposed method significantly improves waveform clarity, amplitude stability, and periodic regularity. Meanwhile, the average accuracy in pulse pattern recognition reaches 96.29%±0.26%.

         

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