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Generation of visualized medical rehabilitation exercise prescriptions with diffusion models

  • Juewen Ni
    ,
  • Peng Du
    ,
  • Qihan Hu
    ,
  • Zhenghui Xu
    ,
  • Hao Zeng
    ,
  • Hao Xie
  • Communication University of Zhejiang
    ,
  • Uber Technologies, Inc.
    ,
  • Hangzhou Jiuselu Medical Technology Co. Ltd
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Abstract

Visualization of medical rehabilitation exercise prescriptions is to provide a more intuitive and understandable way of conveying medical guidance through visual means. Currently, the generation of visualized medical rehabilitation exercise prescriptions is largely based on the manual use of software for hand drawing. However, not only does this production method exhibit the drawbacks of complexity and high labor costs, but it also suffers from low production efficiency. In this study, we present four novel methods that aim to harness the potential of existing Stable Diffusion to generate visualized medical rehabilitation exercise prescription outputs, as well as to exemplify the generation of visualized rehabilitation exercise prescriptions for frozen shoulders. Experimental results demonstrate that our approaches achieve high-quality and more precise visualized rehabilitation exercise prescriptions.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 237-247 (11 pages)

Publication milestones

  • Published - 02/11/2023

Publication status

Published - 02/11/2023

Publisher

Springer, Japan, India, Australia, Germany, United States, United Arab Emirates, Austria, Switzerland, Italy, China, United Kingdom, Netherlands, Brazil, France, Singapore

Publication series

  • Publication series name: Communications in Computer and Information Science
    ISSN (Print): 1865-0929
    ISSN (Electronic): 1865-0937
    Volume: 1946 CCIS

ISBN (Print)

9789819975860

ISBN (Electronic)

9789819975877

Publication IDs

  • handle.net: 10547/626110
  • Scopus: 85177206397

Host publication title

AI-generated Content - 1st International Conference, AIGC 2023, Revised Selected Papers

Host publication editors

  • Feng Zhao
  • Duoqian Miao

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