Toward Standardized Classification of Foveated Displays.

Emergent in the field of head mounted display design is a desire to leverage the limitations of the human visual system to reduce the computation, communication, and display workload in power and form-factor constrained systems. Fundamental to this reduced workload is the ability to match display resolution to the acuity of the human visual system, along with a resulting need to follow the gaze of the eye as it moves, a process referred to as foveation. A display that moves its content along with the eye may be called a Foveated Display, though this term is also commonly used to describe displays with non-uniform resolution that attempt to mimic human visual acuity.

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.