ArticleBiomedical optics express2023
Hand acupuncture point localization method based on a dual-attention mechanism and cascade network model.
Article in Biomedical optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Sparse Image Registration-Based Marker-Free Hand Acupoint Localization Using TCM Template Queries.Sensors (Basel, Switzerland) · 2026Article
- A Two-Stage Cascaded Regression Framework for Automatic Facial Acupoint Localization in Infrared Thermal Images.Sensors (Basel, Switzerland) · 2026Article
- Artificial intelligence guided acupuncture decision making and treatment: a review of research.Frontiers in medicine · 2026Review
- MetaAcuPoint: MetaHuman-Generated Synthetic Data for Hand Acupoint Localization.Healthcare (Basel, Switzerland) · 2025Article
- A systematic review of computer-aided acupoint localization.iScience · 2025Article
- A review of acupoint localization based on deep learning.Chinese medicine · 2025Review
- Structure-guided deep learning for back acupoint localization via bone-measuring constraints.Frontiers in physiology · 2025Article
- A novel intelligent physiotherapy robot based on dynamic acupoint recognition method.Frontiers in neurorobotics · 2025Article
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Authors and funding
4 authors.
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Abstract
Deep learning techniques have, to a certain extent, solved the problem of overreliance on clinical experience for traditional acupoint localization, but the accuracy and repetition rate of its localization still need to be improved. This paper proposes a hand acupoint localization method based on the dual-attention mechanism and cascade network model. First, by superimposing the dual-attention mechanism SE and CA in the YOLOv5 model and calculating the prior box size using K-means++ to optimize the hand location, we cascade the heatmap regression algorithm with HRNet as the backbone network to detect 21 predefined key points on the hand. Finally, "MF-cun" is combined to complete the acupoint localization. The FPS value is 35 and the average offset error value is 0.0269, which is much lower than the error threshold through dataset validation and real scene testing. The results show that this method can reduce the offset error value by more than 40% while ensuring real-time performance and can combat complex scenes such as unequal lighting, occlusion, and skin color interference.
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