A Method of Tactile Resistive Sensor Array Calibration

A Method of Tactile Resistive Sensor Array Calibration

Paper in proceedings (conference paper)

Resistive sensor arrays (RSA) comprising of pressure-sensitive elements find broad use in pressure-sensing applications. However, sensor manufacturing inevitably yields a number of non-ideal properties. The gain, offset or non-linearities of an RSA’s elements may vary between individual sensors (taxels). Hence a simple calibration procedure to suppress these effects is desirable. The paper presents a method of and apparatus for the calibration of a pressure-sensing RSA. A forward mathematical model for each sensor is obtained during the calibration phase. In subsequent online measurement, the inverse model is utilised to compensated for the non-uniformity in sensor gain and offset. This approach leads to an appreciable improvement in picture quality, which can be mathematically quantified using classification accuracy in our tactile anti-decubitus platform for human monitoring. Namely, in a four-class classification experiment involving a support vector machine, the classification error decreased from 11 % before calibration to 3.5 % after calibration. Owing to the calibration procedure, a classifier trained using a calibrated RSA can be deployed to another calibrated RSA, without further data collection.

Resistive sensor arrays (RSA) comprising of pressure-sensitive elements find broad use in pressure-sensing applications. However, sensor manufacturing inevitably yields a number of non-ideal properties. The gain, offset or non-linearities of an RSA’s elements may vary between individual sensors (taxels). Hence a simple calibration procedure to suppress these effects is desirable. The paper presents a method of and apparatus for the calibration of a pressure-sensing RSA. A forward mathematical model for each sensor is obtained during the calibration phase. In subsequent online measurement, the inverse model is utilised to compensated for the non-uniformity in sensor gain and offset. This approach leads to an appreciable improvement in picture quality, which can be mathematically quantified using classification accuracy in our tactile anti-decubitus platform for human monitoring. Namely, in a four-class classification experiment involving a support vector machine, the classification error decreased from 11 % before calibration to 3.5 % after calibration. Owing to the calibration procedure, a classifier trained using a calibrated RSA can be deployed to another calibrated RSA, without further data collection.

calibration, pressure, resistive sensor array, tactile platform, robustness

calibration, pressure, resistive sensor array, tactile platform, robustness

HUSÁK, M.; MIHÁLIK, O.; DVORSKÝ, P.; BRADÁČ, Z.

2025

14.08.2024

Elsevier

Brno, Czechia

18th IFAC Conference on Programmable Devices and Embedded Systems – PDeS 2024

2405-8963

IFAC-PapersOnLine

58

9

United Kingdom of Great Britain and Northern Ireland

37

42

6

https://doi.org/10.1016/j.ifacol.2024.07.368

http://hdl.handle.net/11012/250195

1-s2.0-S2405896324004579-main