Electronic Modeling for Resistive Textile Matrices

Textile pressure sensing has been proved to be useful for activity recognition in both wearable and ambient intelligence domains. The sensors are organized in a matrix format and their resistances are linked to the pressure asserted onto them. While there have been many works exploring its applicati...

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Bibliographic Details
Published in:IEEE sensors journal 2022-08, Vol.22 (16), p.16081-16088
Main Authors: Cheng, Jingyuan, Xie, Fangting, Liang, Zhen
Format: Article
Language:English
Subjects:
Activity recognition
Ambient intelligence
Electrical measurement
Electrical resistance measurement
forward and backward deduction
Immune system
Mathematical models
Modeling
Resistance
Sensors
textile resistive matrix
Textiles
Voltage measurement
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Summary:Textile pressure sensing has been proved to be useful for activity recognition in both wearable and ambient intelligence domains. The sensors are organized in a matrix format and their resistances are linked to the pressure asserted onto them. While there have been many works exploring its applications, the relationship between the resistance distribution and the numeric data output, has not been thoroughly studied. In this paper, we first demonstrate that there is not a simple, point-by-point mapping between the resistance distribution and the output voltage distribution, then build a generalized model to describe this relation, viz. to provide the answers to the forward and backward questions. The model's correctness is verified using the data measured from a rheostats prototype board. We then demonstrate the model's applications in helping 1) obtain the resistance distribution from the measured voltage matrix, 2) decide the proper analog settings to match the textile matrix, and 3) understand the effects of damages from the long-term usage. The methods and conclusions in this paper are also general for the other resistive textile matrices and could serve as a useful tool in both the design and application phases.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3188490