Electrical impedance tomography (EIT) is a nondestructive imaging technique used to estimate the internal conductivity distribution of a conductive domain by taking potential measurements only at the domain boundaries. If a thin electrically conductive material that responds to pressure with local changes in conductivity is used as a conductive domain, then EIT can be used to create a large-scale pressure-sensitive artificial skin for robotics applications. This paper presents a review of EIT and its application as a robotics sensitive skin, including EIT excitation and image reconstruction techniques, materials, and skin fabrication techniques. Touch interpretation via EIT-based artificial skins is also reviewed.

Research and innovation in the area of robotics in healthcare has seen significant growth in recent years. Global trends indicate that patients are getting older and sicker, while demands in healthcare workers are increasing their chance of injury. Robotic technology has the potential to enable high levels of patient care, clinical productivity and safety for both patients and healthcare workers. This paper surveys the state-of-the-art in robotics in healthcare and well-being, with particular attention to the key barriers and enablers to the implementation of this technology in real-world settings. Desktop research was used to identify available and emerging robotic technology currently in use (or with potential use) in healthcare settings. Primary sources of information included: academic publications, international organisations, commercial websites and online news agencies. In this paper, applications of robots in healthcare were divided into five main areas: service, assistive, socially-assistive, teleoperated and interventional robots. The maturity and readiness of different products is still an open challenge, with service and interventional robots leading the way. Wide-spread adoption of robots is likely to happen as the cost of the technology reduces, and wide evidence of beneficial long-term impact is available. This manuscript identified the main drivers, challenges, opportunities and considerations for implementing robots in healthcare. We hope this manuscript will raise awareness about robotics in healthcare among a wider audience to maximise availability, quality, and acceptability this technology.

During social interaction humans extract important information from tactile stimuli that can improve their understanding of the interaction. The development of a similar capability in a robot will contribute to the future success of intuitive human–robot interaction. This paper presents a thin, flexible and stretchable artificial skin for robotics based on the principle of electrical impedance tomography. This skin, which can be used to extract information such as location, duration and intensity of touch, was used to cover the forearm and upper arm of a full-size mannequin. A classifier based on the ‘LogitBoost’ algorithm was used to classify the modality of eight different types of touch applied by humans to the mannequin arm. Experiments showed that the modality of touch was correctly classified in approximately 71% of the trials. This was shown to be comparable to the accuracy of humans when identifying touch. The classification accuracies obtained represent significant improvements over previous classification algorithms applied to artificial sensitive skins. It is shown that features based on touch duration and intensity are sufficient to provide a good classification of touch modality. Gender and cultural background were examined and found to have no statistically significant effect on the classification results.

Electrical impedance tomography (EIT) is a technique used to estimate the internal conductivity of an electrically conductive body by using measurements made only at its boundary. If this body is made of a thin, flexible, and stretchable material that responds to touch with local changes in conductivity, it can be used to create an artificial sensitive skin. Mathematically, the EIT reconstruction problem is an ill-posed nonlinear inverse problem in which it is commonly assumed that electrodes are located only on the surface of the body. In a thin sensitive skin, however, electrodes can readily be located within the 2-D conducting domain. This paper compares existing electrode-drive patterns with new patterns in which a number of reference electrodes are located inside of the sensitive skin. Simulation results and experimental data show improvements in both resolution and robustness to noise of the reconstructed image. These improvements are shown to be consistent for several commonly used regularization methods.