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

Abstract

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.

Artificial Sensitive Skin for Robotics Based on Electrical Impedance Tomography

“…These evolutionary algorithms work by reducing the root‐mean‐square errors between the analog and measured potentials. Table summarizes how different authors use these different algorithms to solve the EIT image reconstruction, such as improved algorithms, evolutionary algorithms, and intelligent algorithms …”

“…Similarly, the single‐layer elastic fabric coated with the conductive polymer (from Eeonyx Corp.) is also used . However, more emphasis is placed on combining the artificial intelligence with EIT for better image reconstruction.…”

“…However, how to choose the appropriate region of interest (ROI) threshold for different targets is a major drawback. Duan et al combines the deep learning with the EIT dynamic imaging (as shown in Figure b) . NN is trained to verify feasibility in multipoint dynamic touch‐sensing experiments based on independently collected data sets.…”

Tactile Sensor for Minimally Invasive Surgery Using Electrical Impedance Tomography

“…Currently the results are uncalibrated, so measurements of lesion size or tissue properties are only relative quantities. Extension of the phantom tests within this study to a full XYZ map, would provide a dataset to create a localisation map for machine learning approaches [12], in combination with a denoising/post-processing step have been successfully applied to 2D tactile sensors [6]. It may also be possible to obtain a similar transform based on simulations of the deformation of the sensor in SOFA [23].…”

“…The deformation of a saline filled chamber is measured using multiple impedance measurements, and thus the size and stiffness of the contact tissue can be inferred. The majority of impedance based tactile sensors are constructed of thin sheets, which can be approximated to 2D EIT problems [6] , however measuring the deformation of the volume of the saline chamber necessitates a full 3D image reconstruction. We present optimisations of the measurement configurations and investigate candidate metrics from images and from phantom experiments.…”