A Deformable Interface for Human Touch Recognition using Stretchable Carbon Nanotube Dielectric Elastomer Sensors and Deep Neural Networks
This work addresses the lack of efficient signal processing for soft user interfaces, enabling new modes of human-computer interaction through shape-changing solids.
The paper tackled the problem of interpreting human touch on soft interfaces by developing OrbTouch, a device that uses convolutional neural networks to map deformations from stretchable capacitor signals to gestures and touch locations, achieving successful control of Tetris as a demonstration.
User interfaces provide an interactive window between physical and virtual environments. A new concept in the field of human-computer interaction is a soft user interface; a compliant surface that facilitates touch interaction through deformation. Despite the potential of these interfaces, they currently lack a signal processing framework that can efficiently extract information from their deformation. Here we present OrbTouch, a device that uses statistical learning algorithms, based on convolutional neural networks, to map deformations from human touch to categorical labels (i.e., gestures) and touch location using stretchable capacitor signals as inputs. We demonstrate this approach by using the device to control the popular game Tetris. OrbTouch provides a modular, robust framework to interpret deformation in soft media, laying a foundation for new modes of human computer interaction through shape changing solids.