Scholarlink

<p>Qigong is a meditative exercise form that originates in traditional Chinese practice. Shibashi, one form of Qigong, consists of 18 exercises, the so-called <i>18 Forms of Harmony</i>. To facilitate self-guided learning and practicing, feedback on the quality of the execution is essential. This research aims to evaluate the technological feasibility of capturing Qigong exercises and provide appropriate feedback to the trainee. This work comprises four studies (S1–S4) that explore methods to capture Qigong motions, detail the development of a system for quantifying Qigong exercises, and investigate ways to present feedback to the trainees in an appropriate and meaningful way. A total of 58 participants took part in the four studies, among them six Qigong masters. A motion capture technology was developed to extract parameters for Qigong motion quantification, analyze joint angles, and enable comparative assessments of limb movements, phase durations, and extremes of motion. We also considered the needs of Qigong beginners through data collected using inertial measurement units, heart-, eye-, and breath-tracking, and qualitative interviews. Findings suggest the feasibility of identifying certain Qigong forms using deep learning. Furthermore, the studies identified meaningful feedback parameters for quantifying Qigong motions, highlighting essential feedback parameters related to <i>Uniformity</i>, <i>Smoothness</i>, and <i>Flow</i>. Qualitative data revealed user needs, emphasizing a user-centered feedback design without disrupting the meditative flow.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development of a motion capture and feedback system for Qigong

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月11日

查看全文
  • Melanie Baldinger,
  • Kevin Lippmann,
  • Gheorghe Lisca,
  • Veit Senner

摘要

Qigong is a meditative exercise form that originates in traditional Chinese practice. Shibashi, one form of Qigong, consists of 18 exercises, the so-called 18 Forms of Harmony. To facilitate self-guided learning and practicing, feedback on the quality of the execution is essential. This research aims to evaluate the technological feasibility of capturing Qigong exercises and provide appropriate feedback to the trainee. This work comprises four studies (S1–S4) that explore methods to capture Qigong motions, detail the development of a system for quantifying Qigong exercises, and investigate ways to present feedback to the trainees in an appropriate and meaningful way. A total of 58 participants took part in the four studies, among them six Qigong masters. A motion capture technology was developed to extract parameters for Qigong motion quantification, analyze joint angles, and enable comparative assessments of limb movements, phase durations, and extremes of motion. We also considered the needs of Qigong beginners through data collected using inertial measurement units, heart-, eye-, and breath-tracking, and qualitative interviews. Findings suggest the feasibility of identifying certain Qigong forms using deep learning. Furthermore, the studies identified meaningful feedback parameters for quantifying Qigong motions, highlighting essential feedback parameters related to Uniformity, Smoothness, and Flow. Qualitative data revealed user needs, emphasizing a user-centered feedback design without disrupting the meditative flow.