{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T05:16:22Z","timestamp":1788412582539,"version":"build-2803163510"},"reference-count":20,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2022YFB4703100"],"award-info":[{"award-number":["2022YFB4703100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003453","name":"Guangdong Natural Science Foundation","doi-asserted-by":"crossref","award":["2024A1515010540"],"award-info":[{"award-number":["2024A1515010540"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shenzhen Science and Technology Program","award":["ZDCY20250901104359008"],"award-info":[{"award-number":["ZDCY20250901104359008"]}]},{"name":"University-Industry Collaborative Education Program","award":["240506429202244"],"award-info":[{"award-number":["240506429202244"]}]},{"DOI":"10.13039\/501100002402","name":"Sun Yat-sen University","doi-asserted-by":"crossref","award":["2025"],"award-info":[{"award-number":["2025"]}],"id":[{"id":"10.13039\/501100002402","id-type":"DOI","asserted-by":"crossref"}]},{"award":["2025"],"award-info":[{"award-number":["2025"]}],"id":[{"id":"https:\/\/ror.org\/0064kty71","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2024B1111060004"],"award-info":[{"award-number":["2024B1111060004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Deep learning for sensor-based medical imaging provides a non-contact and data-driven route for anatomical surface analysis and personalized traditional Chinese medicine (TCM) applications. However, accurate hand-acupoint localization from camera-acquired hand images remains challenging because expert-annotated acupoint datasets are limited and inter-subject anatomical variations are significant. This paper proposes a marker-free hand-acupoint localization method based on deep feature correspondence learning and sparse image registration. The task is formulated as template-to-target correspondence estimation, in which expert-annotated acupoints in a TCM template image are used as query points and mapped to a target hand image acquired by an optical imaging sensor. A Transformer-based architecture is employed to correlate multi-scale image features, and an uncertainty-aware matching formulation is used to estimate both acupoint positions and unreliable matches. Unlike conventional keypoint detection networks, the proposed method exploits TCM template priors and reduces the dependence on dense target-image acupoint annotations. The constructed dataset contains 1400 images from 378 participants. Participant-level partitioning was performed before image-pair generation, yielding a held-out test set of 38 participants (140 images). Palm and dorsal-hand views were evaluated separately against keypoint-detection baselines and COTR. On this participant-independent internal test set, the proposed method achieved AAPE values of 18.42 pixels for palm images and 12.60 pixels for dorsal-hand images, while reducing inference time from 11,000 ms for COTR to 600 ms. These results indicate the feasibility of template-guided image-based hand-acupoint localization with reference to expert annotations.<\/jats:p>","DOI":"10.3390\/s26175552","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T10:32:18Z","timestamp":1788258738000},"page":"5552","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Sparse Image Registration-Based Marker-Free Hand Acupoint Localization Using TCM Template Queries"],"prefix":"10.3390","volume":"26","author":[{"given":"Shujian","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Shuyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9777-842X","authenticated-orcid":false,"given":"Jianqing","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China"},{"name":"Guangdong Provincial Key Laboratory of Fire Science and Technology, Guangzhou 510006, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s13020-018-0167-z","article-title":"Internationalization of traditional Chinese medicine: Current international market, internationalization challenges and prospective suggestions","volume":"13","author":"Lin","year":"2018","journal-title":"Chin. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"5965","DOI":"10.1364\/BOE.501663","article-title":"Hand acupuncture point localization method based on a dual-attention mechanism and cascade network model","volume":"14","author":"Wang","year":"2023","journal-title":"Biomed. Opt. Express"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.joim.2023.05.002","article-title":"Similarities and differences between kaiy in Persian medicine and moxibustion in Chinese medicine","volume":"21","author":"Jaladat","year":"2023","journal-title":"J. Integr. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1109\/TBME.1975.324477","article-title":"Electrical correlates of acupuncture points","volume":"22","author":"Reichmanis","year":"1975","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"181889","DOI":"10.1109\/ACCESS.2024.3509026","article-title":"Research on human acupoint detection by integrating key point information and acupoint theory","volume":"12","author":"Zhang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, C., He, Z., Wang, X., Yang, B., Guo, J., Liang, H., Lin, Z., and Zhang, L. (2024, January 28\u201330). An acupoint location method based on lightweight efficient channel attention network. Proceedings of the 2024 6th International Conference on Electronic Engineering and Informatics (EEI), Chongqing, China.","DOI":"10.1109\/EEI63073.2024.10696105"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, Y., Yang, H., Chen, D., and Chen, X. (2021, January 10\u201313). Facial acupoints location using transfer learning on deep residual network. Proceedings of the 2021 7th International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/ICCC54389.2021.9674348"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wu, W., Qian, C., Yang, S., Wang, Q., Cai, Y., and Zhou, Q. (2018, January 18\u201323). Look at boundary: A boundary-aware face alignment algorithm. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00227"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sun, S., Xu, H., Sun, L., Fu, Y., Zhang, Y., and Zhao, X. (2022, January 18\u201323). Hand acupoint detection from images based on improved HRNet. Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy.","DOI":"10.1109\/IJCNN55064.2022.9892098"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yuan, Z., Shao, P., Li, J., Wang, Y., Zhu, Z., Qiu, W., Chen, B., Tang, Y., and Han, A. (2024). YOLOv8-ACU: Improved YOLOv8-pose for facial acupoint detection. Front. Neurorobot., 18.","DOI":"10.3389\/fnbot.2024.1355857"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Malekroodi, H.S., Seo, S., Choi, J., Na, C.S., Lee, B.I., and Yi, M. (2024). Real-time location of acupuncture points based on anatomical landmarks and pose estimation models. Front. Neurorobot., 18.","DOI":"10.3389\/fnbot.2024.1484038"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102344","DOI":"10.1016\/j.inffus.2024.102344","article-title":"Local feature matching using deep learning: A survey","volume":"107","author":"Xu","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., and Rabinovich, A. (2018, January 18\u201322). SuperPoint: Self-supervised interest point detection and description. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jiang, W., Trulls, E., Hosang, J., Tagliasacchi, A., and Yi, K.M. (2021, January 10\u201317). COTR: Correspondence transformer for matching across images. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00615"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tan, D., Liu, J., Chen, X., Chen, C., Zhang, R., Shen, Y., Ding, S., and Ji, R. (2022, January 23\u201327). ECO-TR: Efficient correspondences finding via coarse-to-fine refinement. Proceedings of the 17th European Conference on Computer Vision (ECCV 2022), Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-20080-9_19"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"20835","DOI":"10.1007\/s11042-019-7424-8","article-title":"11K Hands: Gender recognition and biometric identification using a large dataset of hand images","volume":"78","author":"Afifi","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, J., and Qiu, J. (2025, January 23\u201325). Improved lightweight human posture recognition based on YOLOv11-pose. Proceedings of the 2025 6th International Conference on Computer Vision, Image and Deep Learning (CVIDL), Ningbo, China.","DOI":"10.1109\/CVIDL65390.2025.11086073"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3065","DOI":"10.1109\/TNSRE.2026.3705649","article-title":"Cross-view multimodal vision-based assessment framework for traditional Chinese medicine rehabilitation training","volume":"34","author":"Zhang","year":"2026","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5001110","DOI":"10.1109\/TIM.2022.3201202","article-title":"A mobile natural human\u2013robot interaction method for virtual Chinese acupuncture","volume":"72","author":"Du","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Sun, Q., Huang, J., Zhang, H., Craig, P., Yu, L., and Lim, E.G. (2023, January 25\u201329). Design and development of a mixed reality acupuncture training system. Proceedings of the 2023 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), Shanghai, China.","DOI":"10.1109\/VR55154.2023.00042"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5552\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T04:32:32Z","timestamp":1788409952000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5552"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,1]]},"references-count":20,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175552"],"URL":"https:\/\/doi.org\/10.3390\/s26175552","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,1]]}}}