EyeSwipe: text entry using gaze paths - Kurauchi, Andrew Toshiaki Nakayama (2018)
People with severe motor disabilities may communicate using their eye movements aided by a virtual keyboard and an eye tracker. Text entry by gaze may also benefit users immersed in virtual or augmented realities, when they do not have access to a physical keyboard or touchscreen. Thus, both users with and without disabilities may take advantage of the ability to enter text by gaze. However, methods for text entry by gaze are typically slow and uncomfortable. In this thesis we propose EyeSwipe as a step further towards fast and comfortable text entry by gaze. EyeSwipe maps gaze paths into words, similarly to how finger traces are used on swipe-based methods for touchscreen devices. A gaze path differs from the finger trace in that it does not have clear start and end positions. To segment the gaze path from the user\'s continuous gaze data stream, EyeSwipe requires the user to explicitly indicate its beginning and end. The user can quickly glance at the vicinity of the other characters that compose the word. Candidate words are sorted based on the gaze path and presented to the user. We discuss two versions of EyeSwipe. EyeSwipe 1 uses a deterministic gaze gesture called Reverse Crossing to select both the first and last letters of the word. Considering the lessons learned during the development and test of EyeSwipe 1 we proposed EyeSwipe 2. The user emits commands to the interface by switching the focus between regions. In a text entry experiment comparing EyeSwipe 2 to EyeSwipe 1, 11 participants achieved an average text entry rate of 12.58 words per minute (wpm) with EyeSwipe 1 and 14.59 wpm with EyeSwipe 2 after using each method for 75 minutes. The maximum entry rates achieved with EyeSwipe 1 and EyeSwipe 2 were, respectively, 21.27 wpm and 32.96 wpm. Participants considered EyeSwipe 2 to be more comfortable and faster, while less accurate than EyeSwipe 1. Additionally, with EyeSwipe 2 we proposed the use of gaze path data to dynamically adjust the gaze estimation. Using data from the experiment we show that gaze paths can be used to dynamically improve gaze estimation during the interaction.