TY - JOUR
T1 - Deformation capture via soft and stretchable sensor arrays
AU - Glauser, Oliver
AU - Panozzo, Daniele
AU - Hilliges, Otmar
AU - Sorkine-Hornung, Olga
N1 - Funding Information:
This work was supported in part by the SNF Grant No. 200021-162958, the NSF CAREER Award No. IIS-1652515, the NSF Grant No. OAC:1835712, and a gift from Adobe. Authors’ addresses: O. Glauser, O. Hilliges, and O. Sorkine-Hornung, ETH Zurich, Department of Computer Science, Zürich, Switzerland; emails: {oliver.glauser, otmar. hilliges, olga.sorkine}@inf.ethz.ch; D. Panozzo, New York University, Courant Institute of Mathematical Sciences, New York, USA; email: panozzo@nyu.edu.
Funding Information:
This work was supported in part by the SNF Grant No. 200021-162958, the NSF CAREER Award No. IIS-1652515, the NSF Grant No. OAC:1835712, and a gift from Adobe. We thank Denis Butscher, Christine de St. Aubin, Raoul Hopf, Manuel Kaufmann, Roi Poranne, Samuel Rosset, Michael Rabi-novich, Riccardo Roveri, Herbert Shea, Rafael Wampfler, Yifan Wang, Wilhelm Woigk, Shihao Wu, and Ji Xiabon for their assistance in the fabrication, with the experiments, and for insightful discussions, and we thank Seonwook Park, Velko Vechev, and Katja Wolff for their help with the video.
Publisher Copyright:
© 2019 Copyright held by the owner/author(s).
PY - 2019/4
Y1 - 2019/4
N2 - We propose a hardware and software pipeline to fabricate flexible wearable sensors and use them to capture deformations without line-of-sight. Our first contribution is a low-cost fabrication pipeline to embed multiple aligned conductive layers with complex geometries into silicone compounds. Overlapping conductive areas from separate layers form local capacitors that measure dense area changes. Contrary to existing fabrication methods, the proposed technique only requires hardware that is readily available in modern fablabs. While area measurements alone are not enough to reconstruct the full 3D deformation of a surface, they become sufficient when paired with a data-driven prior. A novel semi-automatic tracking algorithm, based on an elastic surface geometry deformation, allows us to capture ground-truth data with an optical mocap system, even under heavy occlusions or partially unobservable markers. The resulting dataset is used to train a regressor based on deep neural networks, directly mapping the area readings to global positions of surface vertices. We demonstrate the flexibility and accuracy of the proposed hardware and software in a series of controlled experiments and design a prototype of wearable wrist, elbow, and biceps sensors, which do not require line-of-sight and can be worn below regular clothing.
AB - We propose a hardware and software pipeline to fabricate flexible wearable sensors and use them to capture deformations without line-of-sight. Our first contribution is a low-cost fabrication pipeline to embed multiple aligned conductive layers with complex geometries into silicone compounds. Overlapping conductive areas from separate layers form local capacitors that measure dense area changes. Contrary to existing fabrication methods, the proposed technique only requires hardware that is readily available in modern fablabs. While area measurements alone are not enough to reconstruct the full 3D deformation of a surface, they become sufficient when paired with a data-driven prior. A novel semi-automatic tracking algorithm, based on an elastic surface geometry deformation, allows us to capture ground-truth data with an optical mocap system, even under heavy occlusions or partially unobservable markers. The resulting dataset is used to train a regressor based on deep neural networks, directly mapping the area readings to global positions of surface vertices. We demonstrate the flexibility and accuracy of the proposed hardware and software in a series of controlled experiments and design a prototype of wearable wrist, elbow, and biceps sensors, which do not require line-of-sight and can be worn below regular clothing.
KW - Capacitive
KW - Deformation capture
KW - Sensor array
KW - Stretchable
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U2 - 10.1145/3311972
DO - 10.1145/3311972
M3 - Article
AN - SCOPUS:85065774574
VL - 38
JO - ACM Transactions on Graphics
JF - ACM Transactions on Graphics
SN - 0730-0301
IS - 2
M1 - 16
ER -