Capafoldable: self-tracking foldable smart textiles with capacitive
  sensing
Lala Shakti Swarup Ray Daniel Geißler Bo Zhou Paul Lukowicz Berit Greinke
Folding is an unique structural technique to enable planer materials with
motion or 3D mechanical properties. Textile-based capacitive sensing has shown
to be sensitive to the geometry deformation and relative motion of conductive
textiles. In this work, we propose a novel self-tracking foldable smart textile
by combining folded fabric structures and capacitive sensing to detect the
structural motions using state-of-the-art sensing circuits and deep learning
technologies. We created two folding patterns, Accordion and Chevron, each with
two layouts of capacitive sensors in the form of thermobonded conductive
textile patches. In an experiment of manually moving patches of the folding
patterns, we developed deep neural network to learn and reconstruct the
vision-tracked shape of the patches. Through our approach, the geometry
primitives defining the patch shape can be reconstructed from the capacitive
signals with R-squared value of up to 95\% and tracking error of 1cm for 22.5cm
long patches. With mechanical, electrical and sensing properties, Capafoldable
could enable a new range of smart textile applications.