Single to Multi: Data-Driven High Resolution Calibration Method for Piezoresistive Sensor Array

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Accurately detecting multiple simultaneous touches is crucial for various applications using piezoresistance sensor arrays. However, calibrating them is difficult due to their nonlinearity and hysteresis. While data-driven deep learning approaches could model complex sensor patterns, the required amount of labeled data increases exponentially as the number of contact points or sensor subelements increases. In this letter, we propose a novel supervised learning framework, Local Message Passing Network, that only needs single touch data to calibrate multiple contact points into a high resolution pressure map. The individual sub-local networks eliminate domain shift problems, while a message passing mechanism enables them to correctly learn correlations between neighboring sensor subelements. The performances of the proposed model were tested on labeled single- and double-pressure data and compared with previous deep learning calibration methods. Experimental results show that our framework can expand prior knowledge of single touch data to calibrate multi-touch sensor inputs into high resolution pressure maps.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021-07
Language
English
Article Type
Article
Citation

IEEE ROBOTICS AND AUTOMATION LETTERS, v.6, no.3, pp.4970 - 4977

ISSN
2377-3766
DOI
10.1109/LRA.2021.3070823
URI
http://hdl.handle.net/10203/285315
Appears in Collection
CS-Journal Papers(저널논문)
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