Making stand-alone PS-OG technology tolerant to the equipment shifts

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.