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Motion patterns in acupuncture needle manipulation
  1. Yoonjeong Seo1,2,
  2. In-Seon Lee1,
  3. Won-Mo Jung1,3,
  4. Ho-Sun Ryu1,4,
  5. Jinwoong Lim1,
  6. Yeon-Hee Ryu5,
  7. Jung-Won Kang6,
  8. Younbyoung Chae1,3
  1. 1Acupuncture and Meridian Science Research Center, College of Korean Medicine, Kyung Hee University, Seoul, Korea
  2. 2College of Korean Medicine, Semyung University, Jecheon, Korea
  3. 3Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
  4. 4College of Korean Medicine, Wonkwang University, Iksan, Korea
  5. 5Acupuncture, Moxibustion and Meridian Research Center, Division of Standard Research, Korea Institute of Oriental Medicine, Daejeon, South Korea
  6. 6Department of Acupuncture and Moxibustion, College of Korean Medicine, Kyung Hee University, Seoul, Korea
  1. Correspondence to Professor Younbyoung Chae, Acupuncture and Meridian Science Research Center, Kyung Hee University, 1 Hoegi-dong, Dongdaemun-gu, Seoul, 130-701, Republic of Korea; ybchae{at}khu.ac.kr

Abstract

Background In clinical practice, acupuncture manipulation is highly individualised for each practitioner. Before we establish a standard for acupuncture manipulation, it is important to understand completely the manifestations of acupuncture manipulation in the actual clinic. To examine motion patterns during acupuncture manipulation, we generated a fitted model of practitioners’ motion patterns and evaluated their consistencies in acupuncture manipulation.

Methods Using a motion sensor, we obtained real-time motion data from eight experienced practitioners while they conducted acupuncture manipulation using their own techniques. We calculated the average amplitude and duration of a sampled motion unit for each practitioner and, after normalisation, we generated a true regression curve of motion patterns for each practitioner using a generalised additive mixed modelling (GAMM).

Results We observed significant differences in rotation amplitude and duration in motion samples among practitioners. GAMM showed marked variations in average regression curves of motion patterns among practitioners but there was strong consistency in motion parameters for individual practitioners. The fitted regression model showed that the true regression curve accounted for an average of 50.2% of variance in the motion pattern for each practitioner.

Conclusions Our findings suggest that there is great inter-individual variability between practitioners, but remarkable intra-individual consistency within each practitioner.

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