Towards the development of an EIT-based stretchable sensor for multi-touch industrial human-computer interaction systems

In human-computer interaction studies, an interaction is often considered as a kind of information or discrete internal states of an individual that can be transmitted in a loss-free manner from people to computing interfaces (or robotic interfaces) and vice-versa. This project aims to investigate processes capable of communicating and cooperating by adjusting their schedules to match the evolving execution circumstances, in a way that maximise the quality of their joint activities. By enabling human-computer interactions, the process will emerge as a framework based on the concept of expectancy, demand, and need of the human and computer together, for understanding the interplay between people and computers. The idea of this work is to utilise touch feedback from humans as a channel for communication thanks to an artificial sensitive skin made of a thin, flexible, and stretchable material acting as transducer. As a proof of concept, we demonstrate that the first prototype of our artificial sensitive skin can detect surface contacts and show their locations with an image reconstructing the internal electrical conductivity of the sensor

Multimodal Image Reconstruction Using Supplementary Structural Information in Total Variation Regularization.

In this paper, we propose an iterative reconstruction algorithm which uses available information from one dataset collected using one modality to increase the resolution and signal-to-noise ratio of one collected by another modality. The method operates on the structural information only which increases its suitability across various applications. Consequently, the main aim of this method is to exploit available supplementary data within the regularization framework. The source of primary and supplementary datasets can be acquired using complementary imaging modes where different types of information are obtained (e.g. in medical imaging: anatomical and functional). It is shown by extracting structural information from the supplementary image (direction of level sets) one can enhance the resolution of the other image. Notably, the method enhances edges that are common to both images while not suppressing features that show high contrast in the primary image alone. In our iterative algorithm we use available structural information within a modified total variation penalty term. We provide numerical experiments to show the advantages and feasibility of the proposed technique in comparison to other methods. © 2014 The Author(s)