Recently, we published a prototype for gaze-guided object classification at UbiComp conference 2016. This topic also raised interest of Pupil Labs, the manufacturer of the applied eye tracking device.
WaterCoaster: A Device to Encourage People in a Playful Fashion to Reach Their Daily Water Intake Level
The WaterCoaster started as a seminar project (Gamified Life) comprising the design of a hardware prototype and a mobile app measuring the water intake of humans. Applying gamification elements, we wanted to persuade the user to drink more frequently and to drink a healthier amount of water during work time. We published the results as late breaking work at CHI 2016
Master Thesis: Gaze Estimation Error in Mobile Eye Tracking
Computational Modelling and Prediction of Gaze Estimation Error for Head-mounted Eye Trackers
The gaze estimation error is inherent in head-mounted eye trackers and seriously impacts performance, usability, and user experience of gaze-based interfaces. Particularly in mobile settings, this error varies constantly as users move in front and look at different parts of a display. We envision a new class of gaze-based interfaces that are aware of the gaze estimation error and adapt to it in real time. As a first step towards this vision, we introduce an error model that is able to predict the gaze estimation error. Our method covers major building blocks of mobile gaze estimation, specifically mapping of pupil positions to scene camera coordinates, marker-based display detection, and mapping of gaze from scene camera to on-screen coordinates. We develop our model through a series of principled measurements of a state-of-the-art head-mounted eye tracker. Continue reading
A Seminar on Human-Robot Interaction
Evaluation of Mobile Eyetracking as Input Modality for Multitouch Surfaces
Eyetracking as an Input Modality
Multi-touch surfaces enable highly interactive and intuitive applications. Nevertheless large devices are also constrained. It’s possible that users cannot reach every part of the display without walking around or leaning on the surface. To compensate this restriction, I present a method to use mobile eyetracking as an additional input modality. In particular I propose an approach relying on marker-based display recognition and homogeneous transformations. In a user study I evaluated the implementation in terms of accuracy. As result I extracted some design guidelines for building interfaces and considered how to solve limitations of the proposed system.