In this paper, a large-area flexible tactile sensor for multi-touch and force detection based on EIT technology was developed. A novel design of a sensor material made of a porous elastic polymer and ionic liquid was proposed. The proposed conductive flexible materials combining elastic porous structures and conductive liquids provide continuous, linear changes in impedance with respect to touch forces. A deep learning scheme PSPNet based on MobileNet was adopted to postprocess the originally reconstructed images to improve the performance of tactile perception. By using this data-driven method, we can improve the spatial resolution of the tactile sensor to achieve a single-point position detection error of 7.5±4.5 mm without using internal electrodes.

A novel two-electrode, frequency-scan electrical impedance tomography (EIT) system for gesture recognition not only reduces the measurement complexity and the number of electrodes, but also achieves a high accuracy in recognizing common gestures and pinch gestures. A bespoke circuit with two medical electrodes was developed to collect data from the back of the hand and presented a frequency-scan method to increase the diversity of impedance data. The data were processed using data cleaning and feature extraction methods. The processed data were then sent to machine learning classification models for training and realizing accurate gesture recognition. To verify the effectiveness of this system, we designed two groups of nine gestures in a hand-gesture recognition experiment. The results showed that the system can achieve a recognition accuracy of 98.3% with a group of four common gestures and an accuracy of 97.4% with a group of five pinch gestures. Additionally, two proof-of-concept interactive scenarios were implemented to demonstrate the general purpose of this system.