A Hands-free Spatial Selection and Interaction Technique using Gaze and Blink Input with Blink Prediction for Extended Reality
This addresses the need for accessible, comfortable interaction methods in public or limited spaces, particularly for users unable to perform hand gestures, though it is incremental as it builds on existing gaze-based techniques.
The paper tackled the problem of hands-free spatial interaction in extended reality by proposing a Gaze+Blink technique, which achieved comparable selection speeds to Gaze+Pinch but was prone to accidental selections from unintentional blinks, and an enhanced version using deep learning to filter these out.
Gaze-based interaction techniques have created significant interest in the field of spatial interaction. Many of these methods require additional input modalities, such as hand gestures (e.g., gaze coupled with pinch). Those can be uncomfortable and difficult to perform in public or limited spaces, and pose challenges for users who are unable to execute pinch gestures. To address these aspects, we propose a novel, hands-free Gaze+Blink interaction technique that leverages the user's gaze and intentional eye blinks. This technique enables users to perform selections by executing intentional blinks. It facilitates continuous interactions, such as scrolling or drag-and-drop, through eye blinks coupled with head movements. So far, this concept has not been explored for hands-free spatial interaction techniques. We evaluated the performance and user experience (UX) of our Gaze+Blink method with two user studies and compared it with Gaze+Pinch in a realistic user interface setup featuring common menu interaction tasks. Study 1 demonstrated that while Gaze+Blink achieved comparable selection speeds, it was prone to accidental selections resulting from unintentional blinks. In Study 2 we explored an enhanced technique employing a deep learning algorithms for filtering out unintentional blinks.