Using Variable Dwell Time to Accelerate Gaze-Based Web Browsing with Two-Step Selection
This work addresses the 'Midas Touch' problem in gaze-based interfaces for web browsing, offering incremental improvements in user experience for individuals using such assistive technologies.
The paper tackles the problem of gaze-based web browsing by proposing a two-step selection policy with variable dwell time, where hyperlinks are assigned shorter dwell times if they are more likely to be selected, based on a probabilistic model of gaze behavior. The results show that this approach reduces error rates by 50% compared to a 100ms uniform dwell time and reduces response time by 60% compared to a 300ms uniform dwell time while maintaining similar performance.
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.