Manual Acupuncture Manipulation Recognition Method via Interactive Fusion of Spatial Multiscale Motion Features
Manual acupuncture manipulation (MAM) is essential in traditional Chinese medicine treatment. MAM action recognition is important for junior acupuncturists’ training and education; however, there are obvious personalized differences in hand gestures among expert acupuncturists for the same type of M...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/2024/2124139 |
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| _version_ | 1849685560647483392 |
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| author | Jiyu He Chong Su Jie Chen Jinniu Li Jingwen Yang Cunzhi Liu |
| author_facet | Jiyu He Chong Su Jie Chen Jinniu Li Jingwen Yang Cunzhi Liu |
| author_sort | Jiyu He |
| collection | DOAJ |
| description | Manual acupuncture manipulation (MAM) is essential in traditional Chinese medicine treatment. MAM action recognition is important for junior acupuncturists’ training and education; however, there are obvious personalized differences in hand gestures among expert acupuncturists for the same type of MAM. In addition, during the MAM operations, the magnitude and frequency of the expert acupuncturists’ hand shape and relative needle-holding finger position changes are tiny and fast, resulting in difficulties in observing MAM action details. Thus, we propose a Spatial Multiscale Interactive Fusion MAM Recognition Network to solve the difficulties in MAM recognition. First, this paper presents an optical flow-based hand motion contour global feature extraction method for acupuncture hand shape. Second, to explore the motion rule between the needle-holding fingers during the MAM operations, we design a quantitative description method of the relative motion of the needle-holding fingers: an “interactive attention module,” which achieves feature fusion and mines the correlation between different scales of MAM action features. Finally, the proposed MAM recognition method was validated by 20 acupuncturists from the Beijing University of Traditional Chinese Medicine and 10 from the Beijing Zhongguancun Hospital who participated in the MAM video signal collection. The proposed recognition method achieves the highest average validation accuracy of 95.3% and the highest test accuracy of 96.0% for four typical MAMs, proving its feasibility and effectiveness. |
| format | Article |
| id | doaj-art-c4bf7909d2bf4b4f88adccb2bcb0b890 |
| institution | DOAJ |
| issn | 1751-9683 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-c4bf7909d2bf4b4f88adccb2bcb0b8902025-08-20T03:23:06ZengWileyIET Signal Processing1751-96832024-01-01202410.1049/2024/2124139Manual Acupuncture Manipulation Recognition Method via Interactive Fusion of Spatial Multiscale Motion FeaturesJiyu He0Chong Su1Jie Chen2Jinniu Li3Jingwen Yang4Cunzhi Liu5College of Information Science and TechnologyCollege of Information Science and TechnologyDepartment of Traditional Chinese MedicineDepartment of Traditional Chinese MedicineCollege of Acupuncture-Moxibustion and TuinaCollege of Acupuncture-Moxibustion and TuinaManual acupuncture manipulation (MAM) is essential in traditional Chinese medicine treatment. MAM action recognition is important for junior acupuncturists’ training and education; however, there are obvious personalized differences in hand gestures among expert acupuncturists for the same type of MAM. In addition, during the MAM operations, the magnitude and frequency of the expert acupuncturists’ hand shape and relative needle-holding finger position changes are tiny and fast, resulting in difficulties in observing MAM action details. Thus, we propose a Spatial Multiscale Interactive Fusion MAM Recognition Network to solve the difficulties in MAM recognition. First, this paper presents an optical flow-based hand motion contour global feature extraction method for acupuncture hand shape. Second, to explore the motion rule between the needle-holding fingers during the MAM operations, we design a quantitative description method of the relative motion of the needle-holding fingers: an “interactive attention module,” which achieves feature fusion and mines the correlation between different scales of MAM action features. Finally, the proposed MAM recognition method was validated by 20 acupuncturists from the Beijing University of Traditional Chinese Medicine and 10 from the Beijing Zhongguancun Hospital who participated in the MAM video signal collection. The proposed recognition method achieves the highest average validation accuracy of 95.3% and the highest test accuracy of 96.0% for four typical MAMs, proving its feasibility and effectiveness.http://dx.doi.org/10.1049/2024/2124139 |
| spellingShingle | Jiyu He Chong Su Jie Chen Jinniu Li Jingwen Yang Cunzhi Liu Manual Acupuncture Manipulation Recognition Method via Interactive Fusion of Spatial Multiscale Motion Features IET Signal Processing |
| title | Manual Acupuncture Manipulation Recognition Method via Interactive Fusion of Spatial Multiscale Motion Features |
| title_full | Manual Acupuncture Manipulation Recognition Method via Interactive Fusion of Spatial Multiscale Motion Features |
| title_fullStr | Manual Acupuncture Manipulation Recognition Method via Interactive Fusion of Spatial Multiscale Motion Features |
| title_full_unstemmed | Manual Acupuncture Manipulation Recognition Method via Interactive Fusion of Spatial Multiscale Motion Features |
| title_short | Manual Acupuncture Manipulation Recognition Method via Interactive Fusion of Spatial Multiscale Motion Features |
| title_sort | manual acupuncture manipulation recognition method via interactive fusion of spatial multiscale motion features |
| url | http://dx.doi.org/10.1049/2024/2124139 |
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