3D Pulse Image Detection and Pulse Pattern Recognition Based on Subtle Motion Magnification Technology
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...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Chinese Journal of Medical Instrumentation
2025-05-01
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| Series: | Zhongguo yiliao qixie zazhi |
| Subjects: | |
| Online Access: | https://zgylqxzz.xml-journal.net/article/doi/10.12455/j.issn.1671-7104.240567 |
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| author | Chongyang YAO Yongxin CHOU Zhiwei LIANG Haiping YANG Jicheng LIU Dongmei LIN |
| author_facet | Chongyang YAO Yongxin CHOU Zhiwei LIANG Haiping YANG Jicheng LIU Dongmei LIN |
| author_sort | Chongyang YAO |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-9d5ac1d3f4da43c49c3536f11e26991b |
| institution | Kabale University |
| issn | 1671-7104 |
| language | zho |
| publishDate | 2025-05-01 |
| publisher | Editorial Office of Chinese Journal of Medical Instrumentation |
| record_format | Article |
| series | Zhongguo yiliao qixie zazhi |
| spelling | doaj-art-9d5ac1d3f4da43c49c3536f11e26991b2025-08-20T03:47:19ZzhoEditorial Office of Chinese Journal of Medical InstrumentationZhongguo yiliao qixie zazhi1671-71042025-05-0149325526210.12455/j.issn.1671-7104.2405672024-05673D Pulse Image Detection and Pulse Pattern Recognition Based on Subtle Motion Magnification TechnologyChongyang YAO0Yongxin CHOU1Zhiwei LIANG2Haiping YANG3Jicheng LIU4Dongmei LIN5Yancheng Institute of Technology, Yancheng, 224007Suzhou University of Technology, Suzhou, 215500Yancheng Institute of Technology, Yancheng, 224007Suzhou University of Technology, Suzhou, 215500Suzhou University of Technology, Suzhou, 215500Lanzhou University of Technology, Lanzhou, 730050To 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%.https://zgylqxzz.xml-journal.net/article/doi/10.12455/j.issn.1671-7104.240567pulse detectionbinocular vision reconstructionpulse pattern recognitionmachine learning |
| spellingShingle | Chongyang YAO Yongxin CHOU Zhiwei LIANG Haiping YANG Jicheng LIU Dongmei LIN 3D Pulse Image Detection and Pulse Pattern Recognition Based on Subtle Motion Magnification Technology Zhongguo yiliao qixie zazhi pulse detection binocular vision reconstruction pulse pattern recognition machine learning |
| title | 3D Pulse Image Detection and Pulse Pattern Recognition Based on Subtle Motion Magnification Technology |
| title_full | 3D Pulse Image Detection and Pulse Pattern Recognition Based on Subtle Motion Magnification Technology |
| title_fullStr | 3D Pulse Image Detection and Pulse Pattern Recognition Based on Subtle Motion Magnification Technology |
| title_full_unstemmed | 3D Pulse Image Detection and Pulse Pattern Recognition Based on Subtle Motion Magnification Technology |
| title_short | 3D Pulse Image Detection and Pulse Pattern Recognition Based on Subtle Motion Magnification Technology |
| title_sort | 3d pulse image detection and pulse pattern recognition based on subtle motion magnification technology |
| topic | pulse detection binocular vision reconstruction pulse pattern recognition machine learning |
| url | https://zgylqxzz.xml-journal.net/article/doi/10.12455/j.issn.1671-7104.240567 |
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