Artificial Skin Sensor Using 2D Electrical Impedance Tomography: The Sensitivity Volume Method

The sensitivity volume (SV) method for electrical impedance tomography allows for noninvasive, real-time electri-cal imaging using noise-robust resistance measurements. With the SV figure of merit, a set of highly sensitive measurements are optimized for the problem at hand. This method, applied to multi-walled carbon nanotube infused elastomers produces artificial skin sensors whose electrical readout is robust against wear and deformation. This work demonstrates an experimental implementation of the SV method for 2D tomography to demonstrate a 3 x 3 keypad sensor. The result exhibits high pattern fidelity, improved resolution, a minimal number of measurements, and high signal to noise.

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Published
2024-10-15
DOI
https://doi.org/10.1109/bsn63547.2024.10780762
Primary Topic
Energy Efficient Wireless Sensor Networks
Type
article
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article

Artificial Skin Sensor Using 2D Electrical Impedance Tomography: The Sensitivity Volume Method

Claire C. Onsager, Noa Lachman, Lev Rovinsky, Can Aygen et al.
1 links
Energy Efficient Wireless Sensor Networks
article

Artificial Skin Sensor Using 2D Electrical Impedance Tomography: The Sensitivity Volume Method

Claire C. Onsager, Noa Lachman, Lev Rovinsky, Can Aygen, M. Grayson, Shira Cohen
article en

Abstract

The sensitivity volume (SV) method for electrical impedance tomography allows for noninvasive, real-time electri-cal imaging using noise-robust resistance measurements. With the SV figure of merit, a set of highly sensitive measurements are optimized for the problem at hand. This method, applied to multi-walled carbon nanotube infused elastomers produces artificial skin sensors whose electrical readout is robust against wear and deformation. This work demonstrates an experimental implementation of the SV method for 2D tomography to demonstrate a 3 x 3 keypad sensor. The result exhibits high pattern fidelity, improved resolution, a minimal number of measurements, and high signal to noise.

Northwestern University (US), Tel Aviv University (IL)
Affordable and clean energy
Openalex Percentile: Top 74%
Energy Efficient Wireless Sensor Networks
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