Portable Moxibustion Robot With Intelligent Acupoint Positioning Guided by Visual Sensing

BACKGROUND: To overcome key challenges in traditional moxibustion, such as the shortage of skilled practitioners, time-consuming and labour-intensive procedures, this study developed an intelligent robotic system for automated acupoint positioning and moxibustion execution. METHODS: The system established a standardised acupoint coordinate map utilising DensePose-based body normalisation and statistical modelling from 50 subjects. A hybrid serial-parallel robot design enabled large workspace and high precision for bedside application. Safety was ensured by a Model Predictive Controller utilising real-time thermal feedback for dynamic distance adjustment. RESULTS: mm (lower limbs). The system responded to temperature changes within 0.25 s, limiting overshoot to below 0.8°C. CONCLUSIONS: This integrated robotic system provides a precise and scalable solution for automated moxibustion, particularly valuable for resource-limited clinical environments. TRIAL REGISTRATION: It was an investigational clinical study, not a registrational clinical trial.

Authors

Institutions

Publication Details

Journal
International Journal of Medical Robotics and Computer Assisted Surgery
Published
2026-05-09
DOI
https://doi.org/10.1002/rcs.70177
Primary Topic
Acupuncture Treatment Research Studies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Portable Moxibustion Robot With Intelligent Acupoint Positioning Guided by Visual Sensing

Mingrui Hao, Lanhui Wang, Zhaokun Deng, Zi Wang et al.
International Journal of Medical Robotics and Computer Assisted Surgery
Acupuncture Treatment Research Studies
article

Portable Moxibustion Robot With Intelligent Acupoint Positioning Guided by Visual Sensing

Mingrui Hao, Lanhui Wang, Zhaokun Deng, Zi Wang, S Wang, Kairou Guo, Ying Li, Pengcheng Zhang, Ling Guan, Taizhong Wang
article en

Abstract

BACKGROUND: To overcome key challenges in traditional moxibustion, such as the shortage of skilled practitioners, time-consuming and labour-intensive procedures, this study developed an intelligent robotic system for automated acupoint positioning and moxibustion execution. METHODS: The system established a standardised acupoint coordinate map utilising DensePose-based body normalisation and statistical modelling from 50 subjects. A hybrid serial-parallel robot design enabled large workspace and high precision for bedside application. Safety was ensured by a Model Predictive Controller utilising real-time thermal feedback for dynamic distance adjustment. RESULTS: mm (lower limbs). The system responded to temperature changes within 0.25 s, limiting overshoot to below 0.8°C. CONCLUSIONS: This integrated robotic system provides a precise and scalable solution for automated moxibustion, particularly valuable for resource-limited clinical environments. TRIAL REGISTRATION: It was an investigational clinical study, not a registrational clinical trial.

International Journal of Medical Robotics and Computer Assisted SurgeryVol. 22(3)
Chinese University of Hong Kong (HK), Chinese PLA General Hospital (CN), Shandong Institute of Automation (CN), Beijing Academy of Artificial Intelligence (CN), Institute of Automation (CN), China Association of Acupuncture-Moxibustion (CN), Beijing Hospital of Traditional Chinese Medicine (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China
Decent work and economic growth
Openalex Percentile: Top 4%
Acupuncture Treatment Research Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.