A Deformable Interface for Human Touch Recognition Using Stretchable Carbon Nanotube Dielectric Elastomer Sensors and Deep Neural Networks.

This article presents a machine learning approach to map outputs from an embedded array of sensors distributed throughout a deformable body to continuous and discrete virtual states, and its application to interpret human touch in soft interfaces. We integrate stretchable capacitors into a rubber membrane, and use a passive addressing scheme to probe sensor arrays in real time. To process the signals from this array, we feed capacitor measurements into convolutional neural networks that classify and localize touch events on the interface. We implement this concept with a device called OrbTouch. To modularize the system, we use a supervised learning approach wherein a user defines a set of touch inputs and trains the interface by giving it examples; we demonstrate this by using OrbTouch to play the popular game Tetris. Our regression model localizes touches with mean test error of 0.09 mm, whereas our classifier recognizes five gestures with a mean test error of 1.2%. In a separate demonstration, we show that OrbTouch can discriminate between 10 different users with a mean test error of 2.4%. At test time, we feed the outputs of these models into a debouncing algorithm to provide a nearly error-free experience.