Thermal-Comfort Design of Personalized Casts

Xiaoting Zhang, Guoxin Fang, Chengkai Dai, Jouke Verlinden, Jun Wu, Emily Whiting, Charlie Wang

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

26 Citations (Scopus)


This paper introduces a novel method for designing personalized orthopedic casts which are aware of thermal-comfort while satisfying mechanical requirements. Our pipeline starts from thermal images taken by an infrared camera, by which the distribution of thermal-comfort sensitivity is generated on the surface of a 3D scanned model. We formulate a hollowed Voronoi tessellation pattern to represent the covered region for a web-like cast design. The pattern is further optimized according to the thermal-comfort sensitivity calculated from thermal images. Working together with a thickness variation method, we generate a solid model for a personalized cast maximizing both thermal comfort and mechanical stiffness. To demonstrate the effectiveness of our approach, 3D printed models of personalized casts are tested on body parts of different individuals.
Original languageEnglish
Title of host publicationUIST'17
Subtitle of host publicationProceedings of the 30th Annual ACM Symposium on User Interface Software and Technology
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages12
ISBN (Print)978-1-4503-4981-9
Publication statusPublished - 2017
EventUIST'17: The 30th Annual ACM Symposium on User Interface Software and Technology - Québec City, Canada
Duration: 22 Oct 201725 Oct 2017


CityQuébec City


  • personalized cast
  • thermal-comfort
  • 3D printing
  • pattern optimization
  • structural analysis


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