收藏切换
Highly Adaptive Method for Automatic Localization of Human Acupoints Based on Back Morphology Classification
收藏切换
PDF
Ying-yu CAO1, Shao-wei GAO1, Chao-fei WANG1, Jun-fen HUANG1, Chuang LU2
Science Technology and Engineering | 2025, 25(21) : 8814 - 8822
Less
收藏切换
Science Technology and Engineering | 2025, 25(21): 8814-8822
Papers·Medicine
Highly Adaptive Method for Automatic Localization of Human Acupoints Based on Back Morphology Classification
Full
Ying-yu CAO1, Shao-wei GAO1, Chao-fei WANG1, Jun-fen HUANG1, Chuang LU2
Affiliations
  • 1 Beijing Institute of Petrochemical Technology, Beijing 102617, China
  • 2 Xiangyu Medical Eguipment Co., Ltd., Anyang 456399, China
Published: 2025-07-28 doi: 10.12404/j.issn.1671-1815.2405162
Outline
收藏切换

The current acupoint automatic positioning technology for massage robots is faced with issues such as dress restrictions, limited application scope, and poor positioning accuracy. A new method for human back acupoint positioning, based on back morphology classification and a multilayer perceptron network morphology classification-multilayer perceptron-based accurate point location(BMC-MPAPL) has been proposed. A substantial collection of diverse human back images, along with skeletal key point positioning techniques, kernel density estimation, and the maximum interclass variance approach, was used to investigate the statistical distribution and effective categorization of back morphologies. To counteract the restrictions imposed by clothing on positioning, a dataset encompassing key back points and the Dazhui acupoint was developed based on classification outcomes, and the automatic positioning of the Dazhui acupoint was accomplished through a deep learning model of the multi-layer perceptron network. Utilizing the Dazhui acupoint's positioning results, a human coordinate system was established, and the automatic positioning of 60 common back acupoints was achieved with the integration of ancient Chinese bone measurement methods. The results show that the Dazhui acupoint positioning model, tailored to various back morphologies, has realized high-precision positioning, with an average accuracy of 94.87% at an allowable error of 20 mm, marking a 13.37% enhancement over models without back classification. For other common back acupoints, the positioning accuracy stands at 91.58% within an allowable error of 20 mm, irrespective of patient attire or background constraints. It is concluded that the method presented effectively enhances the accuracy and applicability of acupoint automatic positioning.

acupoint positioning  /  back shape  /  classification threshold  /  bone measurement method
Ying-yu CAO, Shao-wei GAO, Chao-fei WANG, Jun-fen HUANG, Chuang LU. Highly Adaptive Method for Automatic Localization of Human Acupoints Based on Back Morphology Classification[J]. Science Technology and Engineering, 2025 , 25 (21) : 8814 -8822 . DOI: 10.12404/j.issn.1671-1815.2405162
Year 2025 volume 25 Issue 21
PDF
223
103
Cite this Article
BibTeX
Article Info
doi: 10.12404/j.issn.1671-1815.2405162
  • Receive Date:2024-07-10
  • Online Date:2026-01-13
  • Published:2025-07-28
Article Data
Affiliations
History
  • Received:2024-07-10
  • Revised:2025-04-09
Funding
Affiliations
    1 Beijing Institute of Petrochemical Technology, Beijing 102617, China
    2 Xiangyu Medical Eguipment Co., Ltd., Anyang 456399, China
References
Share
https://castjournals.cast.org.cn/joweb/kxjsygc/EN/10.12404/j.issn.1671-1815.2405162
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT