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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.

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BackgroundTo ensure appropriate and timely care, interpreters are often required to aid communication between clinicians and patients from non-English speaking backgrounds. In a hospital environment, where care is delivered 24 hours a day, interpreters are not always available. Subsequently, culturally and linguistically diverse (CALD) patients are sometimes unable to access timely assessment because of clinicians’ inability to communicate directly with them.ObjectiveThe aim of this study was to design and evaluate CALD Assist, a tablet app to assist communication between patients and allied health clinicians in the absence of an interpreter. CALD Assist uses key phrases translated into common languages and uses pictorial, written, and voice-over prompts to facilitate communication during basic patient assessment.MethodsCALD Assist’s design, functionality, and content were determined through focus groups with clinicians and informed by interpreting and cultural services. An evaluation was conducted in a live trial phase on eight wards across 2 campuses of a hospital in Victoria, Australia.ResultsA commercial grade CALD Assist mobile app for five disciplines within allied health was developed and evaluated. The app includes a total of 95 phrases in ten different languages to assist clinicians during their initial assessment. Evaluation results show that clinicians’ confidence in their assessment increased with use of the CALD Assist app: clinicians’ reports of “complete confidence” increased from 10% (3/30) to 42% (5/12), and assessment reports of “no confidence” decreased from 57% (17/30) to 17% (2/12). Average time required to complete an assessment with patients from non-English speaking backgrounds reduced from 42.0 to 15.6 min.ConclusionsThrough the use of CALD Assist, clinician confidence in communicating with patients from non-English speaking backgrounds in the absence of an interpreter increased, providing patients from non-English speaking backgrounds with timely initial assessments and subsequent care in line with their English speaking peers. Additionally, the inclusion of images and video demonstrations in CALD Assist increased the ability to communicate with patients and overcome literacy-related barriers. Although a number of hurdles were faced, user uptake and satisfaction were positive, and the app is now available in the Apple App Store.

The proposed app can be used to reduce variances in practice and provide a timely and positive patient experience for patients from NESBs who are unable to communicate in English during hospital admissions.

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