EyeTAP: Introducing a multimodal gaze-based technique using voice inputs with a comparative analysis of selection techniques

Abstract

One of the main challenges of gaze-based interactions is the ability to distinguish normal eye function from a deliberate interaction with the computer system, commonly referred to as ‘Midas touch’. In this paper we propose EyeTAP (Eye tracking point-and-select by Targeted Acoustic Pulse) a contact-free multimodal interaction method for point-and-select tasks. We evaluated the prototype in four user studies with 33 participants and found that EyeTAP is applicable in the presence of ambient noise, results in a faster movement time, and faster task completion time, and has a lower cognitive workload than voice recognition. In addition, although EyeTAP did not generally outperform the dwell-time method, it did have a lower error rate than the dwell-time in one of ∗Corresponding author Email address: [email protected] (Mohsen Parisay) Preprint submitted to International Journal of Human-Computer Studies June 3, 2021 our experiments. Our study shows that EyeTAP would be useful for users for whom physical movements are restricted or not possible due to a disability or in scenarios where contact-free interactions are necessary. Furthermore, EyeTAP has no specific requirements in terms of user interface design and therefore it can be easily integrated into existing systems.