The Anatomical Edutainer

Marwin Schindler, Hsiang-Yun Wu, and Renata Raidou
(In Proceedings of IEEE Vis 2020 (Best Short Paper Award), pages 1-5; arXiv)

Physical visualizations (i.e., data representations by means of physical objects) have been used for many centuries in medical and anatomical education. Recently, 3D printing techniques started also to emerge. Still, other medical physicalizations that rely on affordable and easy-to-find materials are limited, while smart strategies that take advantage of the optical properties of our physical world have not been thoroughly investigated.

In our paper, which received the Best Short Paper Award at IEEE Vis 2020, we propose the Anatomical Edutainer, a workflow to guide the easy, accessible, and affordable generation of physicalizations for tangible, interactive anatomical edutainment. Our workflow consists of two main components:

(1) the generation of 2D printable physicalizations, which exhibit different visual properties (i.e., hues of the visible spectrum) under colored lenses or colored lights, and reveal distinct anatomical structures, and

(2) the generation of 3D foldable physicalizations, where anatomical structures undergo unfolding, to ensure that they can be printed and assembled to a 3D papercraft. The assembled papercraft can be subsequently explored under colored lenses or lights, similarly to its 2D counterpart.

Anyone with access to a computer and a common printer can create our proposed physicalizations, while the colored filters or lights are widely available and affordable. Additionally, the templates of our physicalizations need to be created only once and can be easily reprinted, which makes them an affordable and accessible tool for educational purposes, such as at art exhibitions or science museums. The tangible character of the 3D papercraft assembly adds to the enjoyment of the process, making them especially suitable for children’s anatomical edutainment.

BibTeX

@article{Schindler:Edutainer:2020,
title = {{The Anatomical Edutainer}},
author = {Marwin Schindler, Hsiang-Yun Wu, and Renata G. Raidou},
year = {2020},
journal = {Proceedings of IEEE Vis 2020 (Best Short Paper Award)},
pages = {1--5},
DOI = {10.1109/VIS47514.2020.00007}
}