Tactile sensing using machine learning-driven electrical impedance tomography

Zainab Husain, Nadya Abdel Madjid, Panos Liatsis

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Electrical Impedance Tomography (EIT) tactile sensors have limited success in equipping robots with tactile sensing capabilities due to the low spatial resolution of the resulting tactile images and the presence of image artifacts. To address these limitations, we propose a modular framework for invariant recognition of objects, within the context of an EIT artificial skin sensor. Three interconnected problems, i.e., EIT image reconstruction, segmentation and object recognition, are tackled in this work with the aid of machine learning. A novel conductivity surface decomposition approach, based on low order bivariate polynomials and RBF networks is introduced for the efficient solution of the EIT inverse problem. Next, segmentation of the reconstructed images is performed using a convolutional neural network and transfer learning. Finally, a subspace KNN ensemble classifier is trained on the set of object descriptors extracted from the segmented inhomogeneities to classify the objects. The proposed framework provides an accuracy of 97.5% on unseen data.

Original languageBritish English
Article number9336698
Pages (from-to)11628-11642
Number of pages15
JournalIEEE Sensors Journal
Issue number10
StatePublished - 15 May 2021


  • Electrical impedance tomography
  • Image reconstruction
  • Object recognition
  • Segmentation
  • Tactile sensing


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