Research on acupuncture robots based on the OptiTrack motion capture system and a robotic arm

被引:0
|
作者
He, Ling [1 ]
Yang, Hui [1 ]
Li, Kang [2 ]
Wang, Junwen [3 ]
Sun, Zhibo [3 ]
Yang, Jinsheng [4 ]
Zhang, Jing [1 ]
机构
[1] Sichuan Univ, Coll Biomed Engn, Chengdu 610065, Peoples R China
[2] Sichuan Univ, West China Biomed Big Data Ctr, Chengdu 610065, Peoples R China
[3] Inst Basic Theory Chinese Med, Beijing 100700, Peoples R China
[4] China Acad Chinese Med Sci, Inst Basic Theory Chinese Med, Beijing 100700, Peoples R China
关键词
acupuncture robot; acupuncture quantification; acupoint location; De Qi detection; QUANTIFICATION;
D O I
10.19852/j.cnki.jtcm.2025.01.020
中图分类号
R [医药、卫生];
学科分类号
10 ;
摘要
OBJECTIVE: To propose an automatic acupuncture robot system for performing acupuncture operations. METHODS: The acupuncture robot system consists three components: automatic acupoint localization, acupuncture manipulations, and De Qi sensation detection. The OptiTrack motion capture system is used to locate acupoints, which are then translated into coordinates in the robot control system. A flexible collaborative robot with an intelligent gripper is then used to perform acupuncture manipulations with high precision. In addition, a De Qi sensation detection system proposed to evaluate the effect of acupuncture. To verify the stability of the designed acupuncture robot, acupoints' coordinates localized by the acupuncture robot are compared with the Gold Standard labeled by a professional acupuncturist using significant level tests. RESULTS: Through repeated experiments for eight acupoints, the acupuncture robot achieved a positioning error within 3.3 mm, which is within the allowable range of needle extraction and acupoint insertion. During needle insertion, the robot arm followed the prescribed trajectory with a mean deviation distance of 0.02 mm and a deviation angle of less than 0.15 degrees. The results of the lifting thrusting operation in the Xingzhen process show that the mean acupuncture depth error of the designed acupuncture robot is approximately 2 mm, which is within the recommended depth range for the Xingzhen operation. In addition, the average detection accuracy of the De Qi keywords is 94.52%, which meets the requirements of acupuncture effect testing for different dialects. CONCLUSION: The proposed acupuncture robot system streamlines the acupuncture process, increases efficiency, and reduces practitioner fatigue, while also allowing for the quantification of acupuncture manipulations and evaluation of therapeutic effects. The development of an acupuncture robot system has the potential to revolutionize low back pain treatment and improve patient outcomes.
引用
收藏
页码:201 / 212
页数:12
相关论文
共 50 条
  • [31] Research on Motion Capture System of Motor Skill Based on Computer Vision Technology
    Shi, Qingfeng
    Cui, Yunkun
    JOURNAL OF ELECTRICAL SYSTEMS, 2024, 20 (02) : 1181 - 1191
  • [32] Research Methods for Human Activity Space Based on Vicon Motion Capture System
    Bai, Yaihui
    Hu, Huimin
    Li, Yinxia
    Zhao, Chaoyi
    Luo, Ling
    Wang, Rui
    2017 5TH INTERNATIONAL CONFERENCE ON ENTERPRISE SYSTEMS (ES), 2017, : 202 - 206
  • [34] A Therapeutic Robotic System for the Upper Body Based on the Proficio Robotic Arm
    Saraee, Elham
    Joshi, Ajjen
    Betke, Margrit
    2017 INTERNATIONAL CONFERENCE ON VIRTUAL REHABILITATION (ICVR), 2017,
  • [35] Operational kinematic parameter identification of industrial robots based on a motion capture system through the recurrence way
    Gao, Tianchi
    Meng, Fan
    Zhang, Xiaoyu
    Chen, Wei
    Song, Hanwen
    MECHANISM AND MACHINE THEORY, 2022, 172
  • [36] VFI-based Robotic Arm Control for Natural Adaptive Motion
    Yang, Woosung
    Bae, Ji-Hun
    Kim, Hyungjoo
    INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2014, 11
  • [37] Research and Design of Wearable Human Motion Capture System
    Qin, Gang
    Chen, Yi-Xian
    PROCEEDINGS OF THE 2017 7TH INTERNATIONAL CONFERENCE ON SOCIAL NETWORK, COMMUNICATION AND EDUCATION (SNCE 2017), 2017, 82 : 287 - 290
  • [38] Humanoid motion planning of robotic arm based on human arm action feature and reinforcement learning
    Yang, Aolei
    Chen, Yanling
    Naeem, Wasif
    Fei, Minrui
    Chen, Ling
    MECHATRONICS, 2021, 78
  • [39] The Research on the Motion Control of Industrial Robots based on LabVIEW
    Tao, Fei
    Mu, Pingan
    Dai, Shuguang
    ADVANCES IN MECHATRONICS AND CONTROL ENGINEERING II, PTS 1-3, 2013, 433-435 : 117 - 120
  • [40] SMART LASER BASED TRACKING SYSTEM FOR ROBOTIC ARM
    Karthikeyan, R.
    Mahalakshmi, P.
    GowriShankar, N.
    2013 INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND SIGNAL PROCESSING (ICCSP), 2013, : 903 - 907