A Fabric that Remembers

FinalWeave

As a next phase in ongoing explorations of sensing textiles, We have created this exhibition piece of a fabric that remembers where and how it was pressed. The fabric is currently on display at Accenture Labs in San Francisco and uses both the fabric and tablet to visualize touch in realtime. My personal constraint that guided the work was to get as much of the circuitry as possible embedded into the fabric. Thus, for the e-textiles nerds out there, you might be happy to know that all of the wiring for the resistive sensing and voltage dividing is embedded into the fabric by way of using different resistance yarns. I have included all of the swatches I made in preparation for the final design for reference. A full description can be found at my lab webpage, unstable.design. Special thanks to Sasha De Koninck for introducing me to the wonderful world of 2-pic weave structures, and stocking the lab with every conductive material I could have ever wanted and to Shanel Wu and Emma Goodwill for their masterful work programming the ESP board to collect, store, and communicate to the visualization server in realtime.

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  • Instructions to be Posted Shortly

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AdaCAD: A Tool for Designing Woven Smart Textiles

AdaCAD is a software platform that we built to support designing woven drafts and circuitry schematics in tandem. The tool is open-source and we are still working out the final design details so it is more broadly usable. Mainly, AdaCAD offers support for commonly used features in smart textiles weaving like multilayer weaves and viewing your weave in terms of the draft as well as the paths of the individual yarn types within the design.  Currently, it is designed to output files for weaving an a TC2 loom. Future updates include supports for harness looms.

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Read Mikhaila’s Presentation from CHI 2019

In May 2019, Mikhaila presented AdaCAD at the annual conference on Human Computer Interaction (CHI) describing our web application for designing smart woven textiles. The work was a collaborative project between Mikhaila Friske, Shanel Wu, and Laura Devendorf. The conference talks were not recorded, but you can view the transcript of our presentation below or read the paper here. 

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