Electrical impedance tomography-based pressure-sensing using conductive membrane

TL;DR

This paper develops a mathematical framework and reconstruction algorithm for pressure sensing using electrical impedance tomography on a conductive membrane, validated through numerical simulations.

Contribution

It introduces a novel inverse problem formulation linking pressure-induced deformation to EIT data and provides a theoretical basis and algorithm for reconstruction.

Findings

Reconstruction algorithm successfully estimates pressure distribution from EIT data.

Theoretical analysis supports the inverse problem formulation.

Numerical simulations validate the feasibility of the proposed method.

Abstract

This paper presents a mathematical framework for a flexible pressure-sensor model using electrical impedance tomography (EIT). When pressure is applied to a conductive membrane patch with clamped boundary, the pressure-induced surface deformation results in a change in the conductivity distribution. This change can be detected in the current-voltage data ({\it i.e.,} EIT data) measured on the boundary of the membrane patch. Hence, the corresponding inverse problem is to reconstruct the pressure distribution from the data. The 2D apparent conductivity (in terms of EIT data) corresponding to the surface deformation is anisotropic. Thus, we consider a constrained inverse problem by restricting the coefficient tensor to the range of the map from pressure to 2D-apparent conductivity. This paper provides theoretical grounds for the mathematical model of the inverse problem. We develop a reconstruction algorithm based on a careful sensitivity analysis. We demonstrate the performance of the reconstruction algorithm through numerical simulations to validate its feasibility for future experimental studies.