Usability of various dwell times for eye-gaze-based object selection with eye tracking

Yesaya Tommy Paulus, Gerard Bastiaan Remijn

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This study investigates the usability of various “dwell times” for selecting visual objects with eye-gaze-based input by means of eye tracking. Two experiments are described in which participants used eye-gaze-based input to select visual objects consisting of alphanumeric characters, dots, or visual icons. First, a preliminary experiment was designed to identify the range of dwell time durations suitable for eye-gaze-based object selection. Twelve participants were asked to evaluate, on a 7-point rating scale, how easily they could perform an object-selection task with a dwell time of 250, 500, 1000, or 2000 ms per object. The evaluations showed that a dwell time of 250 ms to around 1000 ms was rated as potentially useful for object selection with eye-gaze-based input. In the following main experiment, therefore, 30 participants used eye tracking to select object sequences from a display with a dwell time of 200, 400, 800, 1000 or 1200 ms per object. Object selection time, object selection success rate, the number of object selection corrections, and dwell time evaluations were obtained. The results showed that the total time necessary to select visual objects (object selection time) increased when dwell time increased, but longer dwell times resulted in a higher object-selection success rate and fewer object selection corrections. Furthermore, regardless of object type, eye-gaze-based object selection with dwell times of 200–800 ms was significantly slower for participants with glasses than for those without glasses. Most importantly, participant evaluations showed that a dwell time of 600 ms per object was easiest to use for eye-gaze-based selection of all three types of visual objects.

Original languageEnglish
Article number101997
JournalDisplays
Volume67
DOIs
Publication statusPublished - Apr 2021

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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