Convolutional Neural Network With Learnable Masks For EIT Based Tactile Sensing

Electrical Impedance Tomography based sensors have emerged as a promising approach in tactile sensing, offering notable advantages such as affordability, portability, and low power consumption. However, the inherently ill-posed nature of the inverse problem often results in reconstruction errors, impacting on the accuracy of tactile information retrieval. In this work, an effective deep learning approach for tactile sensing is proposed, leveraging the concept of learnable masks, incorporated within a Convolutional Neural Network. The learnable masks support the selection of the most informative feature subsets from the associated voltage inputs, enabling the network to reconstruct conductivity distributions precisely. The proposed approach exhibited outstanding performance in image reconstruction, achieving a mean square error of 0.000041, a structural similarity index of 98.28, and a peak signal-to-noise ratio of 42.35 dB.