Using Variable Dwell Time to Accelerate Gaze-Based Web Browsing with Two-Step Selection

TL;DR

This paper introduces a two-step gaze-based web browsing method with variable dwell times, using probabilistic models to improve speed and accuracy over fixed dwell time approaches.

Contribution

It proposes a novel two-step selection policy with probabilistic dwell time variation based on gaze behavior, enhancing gaze-based web browsing efficiency.

Findings

Probabilistic models significantly outperform heuristics in dwell time variation.

The best model reduces error rate by 50% compared to fixed dwell time.

Response time is reduced by 60% with maintained accuracy.

Abstract

In order to avoid the "Midas Touch" problem, gaze-based interfaces for selection often introduce a dwell time: a fixed amount of time the user must fixate upon an object before it is selected. Past interfaces have used a uniform dwell time across all objects. Here, we propose a gaze-based browser using a two-step selection policy with variable dwell time. In the first step, a command, e.g. "back" or "select", is chosen from a menu using a dwell time that is constant across the different commands. In the second step, if the "select" command is chosen, the user selects a hyperlink using a dwell time that varies between different hyperlinks. We assign shorter dwell times to more likely hyperlinks and longer dwell times to less likely hyperlinks. In order to infer the likelihood each hyperlink will be selected, we have developed a probabilistic model of natural gaze behavior while surfing the web. We have evaluated a number of heuristic and probabilistic methods for varying the dwell times using both simulation and experiment. Our results demonstrate that varying dwell time improves the user experience in comparison with fixed dwell time, resulting in fewer errors and increased speed. While all of the methods for varying dwell time resulted in improved performance, the probabilistic models yielded much greater gains than the simple heuristics. The best performing model reduces error rate by 50% compared to 100ms uniform dwell time while maintaining a similar response time. It reduces response time by 60% compared to 300ms uniform dwell time while maintaining a similar error rate.