  In this work, we propose a novel single-end morphing capacitive sensing
method for shape tracking, FxC, by combining Folding origami structures and
Capacitive sensing to detect the morphing structural motions using
state-of-the-art sensing circuits and deep learning. It was observed through
embedding areas of origami structures with conductive materials as single-end
capacitive sensing patches, that the sensor signals change coherently with the
motion of the structure. Different from other origami capacitors where the
origami structures are used in adjusting the thickness of the dielectric layer
of double-plate capacitors, FxC uses only a single conductive plate per
channel, and the origami structure directly changes the geometry of the
conductive plate. We examined the operation principle of morphing single-end
capacitors through 3D geometry simulation combined with physics theoretical
deduction, which deduced similar behaviour as observed in experimentation. Then
a software pipeline was developed to use the sensor signals to reconstruct the
dynamic structural geometry through data-driven deep neural network regression
of geometric primitives extracted from vision tracking. We created multiple
folding patterns to validate our approach, based on folding patterns including
Accordion, Chevron, Sunray and V-Fold patterns with different layouts of
capacitive sensors using paper-based and textile-based materials.
Experimentation results show that the geometry primitives predicted from the
capacitive signals have a strong correlation with the visual ground truth with
R-squared value of up to 95% and tracking error of 6.5 mm for patches. The
simulation and machine learning constitute two-way information exchange between
the sensing signals and structural geometry.
