This article introduces a soft and stretchable sensor composed of silicone rubber integrating a conductive liquid-filled channel with a biocompatible sodium chloride (NaCl) solution and novel stretchable gold sputtered electrodes to facilitate the biocompatibility of the sensor. By stretching the sensor, the cross section of the channel deforms, thus leading to a change in electrical resistance. The functionalities of the sensor have been validated experimentally: changes in electrical resistance are measured as a function of the applied strain. The experimentally measured values match theoretical predictions, showing relatively low hysteresis. A preliminary assessment on the proposed sensor prototype shows good results with a maximum tested strain of 64%. The design optimization of the saline solution, the electrodes, and the algebraic approximations derived for integrating the sensors in a flexible manipulator for surgery has been discussed. The contribution of this article is the introduction of the biocompatible and stretchable gold sputtered electrodes integrated with the NaCl-filled channel rubber as a fully biocompatible solution for measuring deformations in soft and stretchable medical instruments.

Electrical Impedance Tomography (EIT) is a medical imaging technique that has been recently used to realize stretchable pressure sensors. In this method, voltage measurements are taken at electrodes placed at the boundary of the sensor and are used to reconstruct an image of the applied touch pressure points. The drawback with EIT-based sensors, however, is their low spatial resolution due to the ill-posed nature of the EIT reconstruction. In this paper, we show our performance evaluation of different EIT drive patterns, specifically strategies for electrode selection when performing current injection and voltage measurements. We compare voltage data with Signal-to-Noise Ratio (SNR) and Boundary Voltage Changes (BVC), and study image quality with Size Error (SE), Position Error (PE) and Ringing (RNG) parameters, in the case of one-point and two-point simultaneous contact locations. The study shows that, in order to improve the performance of EIT based sensors, the electrode selection strategies should dynamically change correspondingly to the location of the input stimuli. In fact, the selection of one drive pattern over another can improve the target size detection and position accuracy up to 4.7% and 18%, respectively.

No abstract

Traditional electrical tomography tactile sensors consider the usage of the system's finite element model. This approach brings disadvantages that jeopardise their applicability aspect and wide use. To address this limitation, the main thrust of this work is to present a method for touch position identification for an electrical tomography flexible tactile sensor. This is done by using a supervised machine learning algorithm for performing classification, namely quadratic discriminant analysis. This approach provides accurate contact location identification, increasing the detection speed and the sensor versatility when compared to traditional electrical tomography approaches. Results obtained show classification accuracy rates of up to 91.6% on unseen test data and an average euclidean error ranging from 1 to 10 mm depending on the contact location over the sensor. The sensor is then applied in real case scenarios to show its efficiency. These outcomes are encouraging since they promote the future practical usage of these type of flexible and low-cost sensors.

No abstract