Journal of Acupuncture Research

Korean Acupuncture & Moxibustion Medicine Society (대한침구의학회)
권호 표지
DOI QR코드

DOI QR Code

Development and Validation of a Vision-Based Needling Training System for Acupuncture on a Phantom Model

  • Trong Hieu, Luu (Department of Automation Technology, College of Engineering, Can Tho University) ;
  • Hoang-Long, Cao (Department of Automation Technology, College of Engineering, Can Tho University) ;
  • Duy Duc, Pham (Faculty of Traditional Medicine, Can Tho University of Medicine and Pharmacy) ;
  • Le Trung Chanh, Tran (Department of Automation Technology, College of Engineering, Can Tho University) ;
  • Tom, Verstraten (Brubotics and Flanders Make, Vrije Universiteit Brussel)
  • Received : 2022.11.04
  • Accepted : 2022.11.25
  • Published : 2023.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Background: Previous studies have investigated technology-aided needling training systems for acupuncture on phantom models using various measurement techniques. In this study, we developed and validated a vision-based needling training system (noncontact measurement) and compared its training effectiveness with that of the traditional training method. Methods: Needle displacements during manipulation were analyzed using OpenCV to derive three parameters, i.e., needle insertion speed, needle insertion angle (needle tip direction), and needle insertion length. The system was validated in a laboratory setting and a needling training course. The performances of the novices (students) before and after training were compared with the experts. The technology-aided training method was also compared with the traditional training method. Results: Before the training, a significant difference in needle insertion speed was found between experts and novices. After the training, the novices approached the speed of the experts. Both training methods could improve the insertion speed of the novices after 10 training sessions. However, the technology-aided training group already showed improvement after five training sessions. Students and teachers showed positive attitudes toward the system. Conclusion: The results suggest that the technology-aided method using computer vision has similar training effectiveness to the traditional one and can potentially be used to speed up needling training.

Keywords

Acknowledgement

The authors would like to thank Tran Minh Man and Le Hong Phuoc for their support in organizing the validation and the experts and students for their participation in this work.

References

  1. Kaptchuk TJ. Acupuncture: theory, efficacy, and practice. Ann Intern Med 2002;136:374-383. doi: 10.7326/0003-4819-136-5-200203050-00010
  2. White A, Ernst E. A brief history of acupuncture. Rheumatology (Oxford) 2004;43:662-663. doi: 10.1093/rheumatology/keg005
  3. WHO. Acupuncture review and analysis of reports on controlled clinical trials. World Health Organization; 2002.
  4. Liu T. Acupuncture: what underlies needle administration? Evid Based Complement Alternat Med 2009;6:185-193. doi: 10.1093/ecam/nen002
  5. Nasir LS. Acupuncture. Prim Care 2002;29:393-405. doi: 10.1016/s0095-4543(01)00007-0
  6. Ernst E. Acupuncture--a critical analysis. J Intern Med 2006;259:125-137. doi: 10.1111/j.1365-2796.2005.01584.x
  7. Miao P, Zhang Q, Ji YY, Jiang J, Guo HD, Wang FB, et al. Quantitative assessment of expert's lifting-thrusting manipulation in acupuncture based on haptic device. Appl Mech Mater 2013;397-400:2507-2510. doi: 10.4028/www.scientific.net/AMM.397-400.2507
  8. Li J, Wu MX. [Ideas of standardization evaluation on acupuncture skills: enlightened by quantitative appraisal of surgical skills in Europe and North America]. Zhen Ci Yan Jiu 2011;36:449-452. Chinese.
  9. Li J, Grierson LE, Wu MX, Breuer R, Carnahan H. Perceptual motor features of expert acupuncture lifting-thrusting skills. Acupunct Med 2013;31:172-177. doi: 10.1136/acupmed-2012-010265
  10. Shin SG, Im IC. Satisfaction level of clinical practice and related variables for students in the department of radiology. J Korea Contents Assoc 2010;10:276-284. doi: 10.5392/JKCA.2010.10.9.276
  11. Jang JE, Lee YS, Jang WS, Sung WS, Kim EJ, Lee SD, et al. Trends in acupuncture training research: focus on practical phantom models. J Acupunct Res 2022;39:77-88. doi:10.13045/jar.2022.00024
  12. Lee IS, Lee YS, Park HJ, Lee H, Chae Y. Evaluation of phantom-based education system for acupuncture manipulation. PLoS One 2015;10:e0117992. doi: 10.1371/journal.pone.0117992
  13. Lim JW, Jung WM, Lee IS, Seo YJ, Ryu HS, Ryu YH, et al. Development of acupuncture manipulation education system. The Acupuncture 2014;31:11-19. doi: 10.13045/acupunct.2014049
  14. Jung WM, Lim J, Lee IS, Park HJ, Wallraven C, Chae Y. Sensorimotor learning of acupuncture needle manipulation using visual feedback. PLoS One 2015;10:e0139340. doi: 10.1371/journal.pone.0139340
  15. Lyu R, Gao M, Yang H, Wen Z, Tang W. Stimulation parameters of manual acupuncture and their measurement. Evid Based Complement Alternat Med 2019;2019:1725936. doi: 10.1155/2019/1725936
  16. Davis RT, Churchill DL, Badger GJ, Dunn J, Langevin HM. A new method for quantifying the needling component of acupuncture treatments. Acupunct Med 2012;30:113-119. doi: 10.1136/ acupmed-2011-010111
  17. Tang WC, Yang HY, Liu TY, Gao M, Xu G. Motion video-based quantitative analysis of the 'lifting-thrusting' method: a comparison between teachers and students of acupuncture. Acupunct Med 2018;36:21-28. doi: 10.1136/acupmed-2016-011348
  18. Xu LL, Wang F, Yang HY, Tang WC. Three-dimensional finger motion tracking during needling: a solution for the kinematic analysis of acupuncture manipulation. J Vis Exp 2021;(176):e62750. doi: 10.3791/62750
  19. Xu LL, Xie J, Yang HY, Wang F, Tang WC. Operation stability analysis of basic acupuncture manipulation based on three-dimensional motion tracking data. Wirel Commun Mob Comput 2022;2022:1958984. doi: 10.1155/2022/1958984
  20. Zhang A, Yan XK, Liu AG. [An introduction to a newly-developed "Acupuncture Needle Manipulation Training-evaluation System" based on optical motion capture technique]. Zhen Ci Yan Jiu 2016;41:556-559. Chinese.
  21. Liu TT, Wang ZH, Zhang G, Li H. Application research of neural network in acupuncture training system. Comput Technol Autom 2020;39:102-107.
  22. Leow MQ, Cao T, Lee SH, Cui SL, Tay SC, Ooi CC. Ultrasonography in acupuncture: potential uses for education and research. Acupunct Med 2016;34:320-322. doi: 10.1136/acupmed-2016-011182
  23. Langevin HM, Konofagou EE, Badger GJ, Churchill DL, Fox JR, Ophir J, et al. Tissue displacements during acupuncture using ultrasound elastography techniques. Ultrasound Med Biol 2004;30:1173-1183. doi: 10.1016/j.ultrasmedbio.2004.07.010
  24. Konofagou EE, Langevin HM. Using ultrasound to understand acupuncture. Acupuncture needle manipulation and its effect on connective tissue. IEEE Eng Med Biol Mag 2005;24:41-46. doi:10.1109/memb.2005.1411347
  25. Chen YL, Hou MC, Chang SC, Chuang KW, Lee PY, Huang CC. Development and evaluation of inexpensive ultrasound using A-mode and M-mode signals to identify lung depth and avoid risk of pneumothorax in acupuncture. J Med Biol Eng 2021;41: 251-259. doi: 10.1007/s40846-020-00584-x
  26. Chou L, Hong JY, Hong C. A new technique for acupuncture therapy and its effectiveness in treating fibromyalgia syndrome: a case report. J Musculoskelet Pain 2008;16:193-198. doi: 10.1080/10582450802161978
  27. Chou LW, Hsieh YL, Kuan TS, Hong CZ. Needling therapy for myofascial pain: recommended technique with multiple rapid needle insertion. Biomedicine (Taipei) 2014;4:13. doi: 10.7603/s40681-014-0013-2
  28. Sanders EBN, Stappers PJ. Co-creation and the new landscapes of design. CoDesign 2008;4:5-18. doi: 10.1080/15710880701875068
  29. Bradski G, Kaehler A. Learning OpenCV: computer vision with the OpenCV library. O'Reilly Media; 2008.
  30. Zhang Z. Flexible camera calibration by viewing a plane from unknown orientations. Paper presented at: Proceedings of the Seventh IEEE International Conference on Computer Vision; 1999 Sep 20-27; Kerkyra, Greece. IEEE, 1999. p. 666-673.
  31. Lewis JP. Fast normalized cross-correlation. In: Denis Laurendeau editor. Vision Interface. Canadian Image Processing and Pattern Recognition Society; 1995;120-123.
  32. Ikai Y, Migaki M, Kida N, Iwamoto H, Hamada H. Analysis of the skills to acupuncture. In: Duffy V editor. Digital Human Modeling. Applications in Health, Safety, Ergonomics and Risk Management: Human Modeling. Springer; 2015;66-73.
  33. Yin CS, Kim JH, Park HJ. High-velocity insertion of acupuncture needle is related to lower level of pain. J Altern Complement Med 2011;17:27-32. doi: 10.1089/acm.2010.0120
  34. Lao L. Acupuncture techniques and devices. J Altern Complement Med 1996;2:23-25. doi: 10.1089/acm.1996.2.23
  35. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Q 2003;27:425-478. doi: 10.2307/30036540
  36. Wright DB. Comparing groups in a before-after design: when t test and ANCOVA produce different results. Br J Educ Psychol 2006;76(Pt 3):663-675. doi: 10.1348/000709905X52210
  37. Clark RE. Reconsidering research on learning from media. Rev Educ Res 1983;53:445-459. doi: 10.3102/00346543053004445
  38. Howard NM, Cook DA, Hatala R, Pusic MV. Learning curves in health professions education simulation research: a systematic review. Simul Healthc 2021;16:128-135. doi: 10.1097/SIH.0000000000000477
  39. Pusic MV, Boutis K, Hatala R, Cook DA. Learning curves in health professions education. Acad Med 2015;90:1034-1042. doi: 10.1097/ACM.0000000000000681