Eye gaze is a fast and ergonomic modality for pointing but limited in precision and accuracy. In this work, we introduce BimodalGaze, a novel technique for seamless head-based refinement of a gaze cursor. The technique leverages eye-head coordination insights to separate natural from gestural head movement. This allows users to quickly shift their gaze to targets over larger fields of view with naturally combined eye-head movement, and to refine the cursor position with gestural head movement. In contrast to an existing baseline, head refinement is invoked automatically, and only if a target is not already acquired by the initial gaze shift. Study results show that users reliably achieve fine-grained target selection, but we observed a higher rate of initial selection errors affecting overall performance. An in-depth analysis of user performance provides insight into the classification of natural versus gestural head movement, for improvement of BimodalGaze and other potential applications.

Ludwig Sidenmark, Diako Mardanbegi, Argenis Ramirez Gomez, Christopher Clarke, and Hans Gellersen. 2020. BimodalGaze: Seamlessly Refined Pointing with Gaze and Filtered Gestural Head Movement. In ACM Symposium on Eye Tracking Research and Applications (ETRA ’20 Full Papers). Association for Computing Machinery, New York, NY, USA, Article 8, 1–9.