Artificial skin through super-sensing method and electrical impedance data from conductive fabric with aid of deep learning

Xi Duan, S Taurand, Manuchehr Soleimani

Research output: Contribution to journalArticlepeer-review

33 Citations (SciVal)


Sense of touch is a major part of man’s communication with their environment. Artificial skins can help robots to have the same sense of touch, especially for their social interactions. This paper presents a pressure mapping sensing using piezo-resistive fabric to represent aspects of the sense of touch. In past few years’ electrical impedance tomography (EIT) is considered to be able offer a good alternative for artificial skin in particular for its ease of adaptation for large area skin compared to individual matrix based sensors. The EIT has also very good temporal performance in data collection allowing for monitoring of fast responses to touch stimulation, enabling a truly real time touch sensing. Electromechanical responses of a conductive fabric can be exploited using EIT to create a low cost and large area touch sensing. Such electromechanical properties are often very complex, so to improve the imaging resolution and touch visibility an artificial intelligent (AI) was used in addition to the state of the art spatio-temporal imaging algorithm. This work demonstrates a step towards an integrated seamless skin with large area sensing in dynamical settings, closer to natural human skin’s behaviour. For the first time a dynamical touch sensing are studies by means of a spatio-temporal based electrical impedance tomography (EIT) imaging on a conductive fabric. The experimental results demonstrated the successful results by a combined AI with dynamical EIT imaging results in single and multiple points of touch.

Original languageEnglish
Article number8831
JournalScientific Reports
Issue number1
Publication statusPublished - 20 Jun 2019

ASJC Scopus subject areas

  • General


Dive into the research topics of 'Artificial skin through super-sensing method and electrical impedance data from conductive fabric with aid of deep learning'. Together they form a unique fingerprint.

Cite this