Minimally produced inkjet-printed tactile sensor model for improved data reliability

Steven D. Gardner, J. Iwan D. Alexander, Yehia Massoud, Mohammad R. Haider

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Inkjet-printing as an on-the-go, inexpensive, and green method of creating instant flexible sensors and circuits will not proliferate until reliable device fabrication is possible outside the research environment. Shortfalls exist due to non-uniform fabrication/curing, environmental humidity/temperature influence, and uncontrollable deposition conditions, particularly in low-production setups. Electrical non-uniformity and variations from low-quality prints made by a minimally produced inkjet-printed sensor may be overcome by training a machine learning model to interpret the variabilities and output a high-confidence prediction of the signal. In this report, an inkjet-printed tactile sensor is modeled to simulate generate a rich data-set for training and testing an echo state network. The end goal of the reported work is to attach the echo state network to the imperfect, on-the-go, inkjet-printed sensor as an edge computing device, transforming the unreliable data into a more stable readout. In this way, the sensor design may be printed using any suitable inkjet-printer with minimal production effort and still extract reliable data. This enables inkjet-printers to be used at home by those in isolated/restrictive settings, poor communities, resource starved environments, or by enthusiasts. Applications include biometric, environmental, electro-chemical and -mechanical sensing, and the concept may be extended to inkjet-printed circuits for signal stabilization.
Original languageEnglish (US)
Title of host publicationProceedings of 2020 11th International Conference on Electrical and Computer Engineering, ICECE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages49-52
Number of pages4
ISBN (Print)9781665422543
DOIs
StatePublished - Dec 17 2020
Externally publishedYes

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