We developed a dwell selection system with ML-based prediction of a user’s intent to select. Because a user perceives visual information through the eyes, precise prediction of a user’s intent will be essential to the establishment of gaze-based interaction. Our system first detects a dwell to roughly screen the user’s intent to select and then predicts the intent by using an ML-based prediction model. We created the intent prediction model from the results of an experiment with five different gaze-only tasks representing everyday situations. The intent prediction model resulted in an overall area under the curve (AUC) of the receiver operator characteristic curve of 0.903. Moreover, it could perform independently of the user (AUC=0.898) and the eye-tracker (AUC=0.880). In a performance evaluation experiment with real interactive situations, our dwell selection method had both higher qualitative and quantitative performance than previously proposed dwell selection methods. 

Presented at Eyes4ICU 2023, IMWUT 2022, WISS 2020

Dwell time is one significant parameter for triggering an accurate dwell selection. In order to develop future implicit interactions, it is important to understand the duration a user needs to recognize a visual object. By providing interactions that are triggered after a user recognizes an object, confusion resulting from the discrepancy between completing a cognitive process, which we define as the process from perceiving a visual stimulus to determining a selection, and triggering an interaction can be reduced. To understand this duration, we developed a model to derive dwell-times, allowing dwell selection to be performed after completing a cognitive process based on the Model Human Processor and the number of fixations. Our model revealed a minimum dwell-time of 174.2 ms for a colored target selection task. For an image selection task, the minimum dwell-time was 272.5 ms, which increased to 835.8 ms when a participant had not previously fixated on the object. 

Presented at ETRA 2023, SAP 2021

We demonstrate a gaze-based command activation technique that is robust against unintentional command activations using a series of dwelling on a target and performing a specific gesture (dwell-then-gesture manipulation). The gesture adopted is a simple two-level stroke, which consists of a sequence of two orthogonal strokes. To achieve robustness against unintentional command activations, we designed and fine-tuned a gesture detection system based on how users move their gaze, as revealed through three experiments. Although our technique seems to simply combine well-known dwell and gesture-based manipulations, implying a low rate of success, our technique is actually the first technique that consists of a short time dwelling for target selection and a simple gesture for command activation. In addition, our technique will be the first technique adopting a marking menu, which is a traditional menu for command activation used in mouse- or pen-based interactions to gaze-based interactions.

Presented at GI 2021, Journal on Human Interface 2021

We present a dwell time reduction technique for gaze-based target acquisition. We adopt Fitts’ Law to achieve the dwell time reduction. Our technique uses both the eye movement time for target acquisition estimated using Fitts’ Law (𝑇𝑒 ) and the actual eye movement time (𝑇𝑎) for target acquisition; a target is acquired when the difference between 𝑇𝑒 and 𝑇𝑎 is small. First, we investigated the relation between the eye movement for target acquisition and Fitts’ Law; the result indicated a correlation of 0.90 after error correction. Then, we designed and implemented our technique. Finally, we conducted a user study to investigate the performance of our technique; an average dwell time of 86.7 ms was achieved, with a 10.0% Midas-touch rate. 

Presented at ETRA 2018