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Tracking users' gaze in virtual reality headsets allows natural and intuitive interaction with virtual avatars and virtual objects. Moreover, a technique known as foveated rendering can help save computational resources and enable hi-resolution but lightweight virtual reality technologies. Predominantly, eye-tracking hardware in modern VR headsets consist of infrared camera(s) and LEDs. Such hardware, together with image processing software consumes a substantial amount of energy, and, provided that hi-speed gaze detection is needed, might be very expensive. A promising technique to overcome these issues is photo-sensor oculography (PS-OG), which allows eye-tracking with high sampling rate and low power consumption. However, the main limitation of the previous PS-OG systems is their inability to compensate for the equipment shifts. In this study, we employ a simple multi-layer perceptron neural network to map raw sensor data to gaze locations and report its performance for shift compensation. Modeling and evaluation is done via a simulation.

Virtual reality (VR) is employed in a variety of different applications. It is our belief that eye-tracking is going to be a part of the majority of VR devices that will reduce computational burden via a technique called foveated rendering and will increase the immersion of the VR environment. A promising technique to achieve low energy, fast, and accurate eye-tracking is photo-sensor oculography (PS-OG). PS-OG technology enables tracking a user's gaze location at very fast rates - 1000Hz or more, and is expected to consume several orders of magnitude less power compared to a traditional video-oculography approach. In this demo we present a prototype of a PS-OG system that we started to develop. The long-term aim of our project is to develop a PS-OG system that is robust to sensor shifts. As a first step we have built a prototype that allows us to test different sensors and their configurations, as well as record and analyze eye-movement data.